negative control group experiments

Positive Control vs Negative Control: Differences & Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A positive control is designed to confirm a known response in an experimental design , while a negative control ensures there’s no effect, serving as a baseline for comparison.

The two terms are defined as below:

• Positive control refers to a group in an experiment that receives a procedure or treatment known to produce a positive result. It serves the purpose of affirming the experiment’s capability to produce a positive outcome.
• Negative control refers to a group that does not receive the procedure or treatment and is expected not to yield a positive result. Its role is to ensure that a positive result in the experiment is due to the treatment or procedure.

The experimental group is then compared to these control groups, which can help demonstrate efficacy of the experimental treatment in comparison to the positive and negative controls.

Positive Control vs Negative Control: Key Terms

Control groups.

A control group serves as a benchmark in an experiment. Typically, it is a subset of participants, subjects, or samples that do not receive the experimental treatment (as in negative control).

This could mean assigning a placebo to a human subject or leaving a sample unaltered in chemical experiments. By comparing the results obtained from the experimental group to the control, you can ascertain whether any differences are due to the treatment or random variability.

A well-configured experimental control is critical for drawing valid conclusions from an experiment. Correct use of control groups permits specificity of findings, ensuring the integrity of experimental data.

See More: Control Variables Examples

The Negative Control

Negative control is a group or condition in an experiment that ought to show no effect from the treatment.

It is useful in ensuring that the outcome isn’t accidental or influenced by an external cause. Imagine a medical test, for instance. You use distilled water, anticipating no reaction, as a negative control.

If a significant result occurs, it warns you of a possible contamination or malfunction during the testing. Failure of negative controls to stay ‘negative’ risks misinterpretation of the experiment’s result, and could undermine the validity of the findings.

The Positive Control

A positive control, on the other hand, affirms an experiment’s functionality by demonstrating a known reaction.

This might be a group or condition where the expected output is known to occur, which you include to ensure that the experiment can produce positive results when they are present. For instance, in testing an antibiotic, a well-known pathogen, susceptible to the medicine, could be the positive control.

Positive controls affirm that under appropriate conditions your experiment can produce a result. Without this reference, experiments could fail to detect true positive results, leading to false negatives. These two controls, used judiciously, are backbones of effective experimental practice.

Experimental Groups

Experimental groups are primarily characterized by their exposure to the examined variable.

That is, these are the test subjects that receive the treatment or intervention under investigation. The performance of the experimental group is then compared against the well-established markers – our positive and negative controls.

For example, an experimental group may consist of rats undergoing a pharmaceutical testing regime, or students learning under a new educational method. Fundamentally, this unit bears the brunt of the investigation and their response powers the outcomes.

However, without positive and negative controls, gauging the results of the experimental group could become erratic. Both control groups exist to highlight what outcomes are expected with and without the application of the variable in question. By comparing results, a clearer connection between the experiment variables and the observed changes surfaces, creating robust and indicative scientific conclusions.

Positive and Negative Control Examples

1. a comparative study of old and new pesticides’ effectiveness.

This hypothetical study aims to evaluate the effectiveness of a new pesticide by comparing its pest-killing potential with old pesticides and an untreated set. The investigation involves three groups: an untouched space (negative control), another treated with an established pesticide believed to kill pests (positive control), and a third area sprayed with the new pesticide (experimental group).

• Negative Control: This group consists of a plot of land infested by pests and not subjected to any pesticide treatment. It acts as the negative control. You expect no decline in pest populations in this area. Any unexpected decrease could signal external influences (i.e. confounding variables ) on the pests unrelated to pesticides, affecting the experiment’s validity.
• Positive Control: Another similar plot, this time treated with a well-established pesticide known to reduce pest populations, constitutes the positive control. A significant reduction in pests in this area would affirm that the experimental conditions are conducive to detect pest-killing effects when a pesticide is applied.
• Experimental Group: This group consists of the third plot impregnated with the new pesticide. Carefully monitoring the pest level in this research area against the backdrop of the control groups will reveal whether the new pesticide is effective or not. Through comparison with the other groups, any difference observed can be attributed to the new pesticide.

2. Evaluating the Effectiveness of a Newly Developed Weight Loss Pill

In this hypothetical study, the effectiveness of a newly formulated weight loss pill is scrutinized. The study involves three groups: a negative control group given a placebo with no weight-reducing effect, a positive control group provided with an approved weight loss pill known to cause a decrease in weight, and an experimental group given the newly developed pill.

• Negative Control: The negative control is comprised of participants who receive a placebo with no known weight loss effect. A significant reduction in weight in this group would indicate confounding factors such as dietary changes or increased physical activity, which may invalidate the study’s results.
• Positive Control: Participants in the positive control group receive an FDA-approved weight loss pill, anticipated to induce weight loss. The success of this control would prove that the experiment conditions are apt to detect the effects of weight loss pills.
• Experimental Group: This group contains individuals receiving the newly developed weight loss pill. Comparing the weight change in this group against both the positive and negative control, any difference observed would offer evidence about the effectiveness of the new pill.

3. Testing the Efficiency of a New Solar Panel Design

This hypothetical study focuses on assessing the efficiency of a new solar panel design. The study involves three sets of panels: a set that is shaded to yield no solar energy (negative control), a set with traditional solar panels that are known to produce an expected level of solar energy (positive control), and a set fitted with the new solar panel design (experimental group).

• Negative Control: The negative control involves a set of solar panels that are deliberately shaded, thus expecting no solar energy output. Any unexpected energy output from this group could point towards measurement errors, needed to be rectified for a valid experiment.
• Positive Control: The positive control set up involves traditional solar panels known to produce a specific amount of energy. If these panels produce the expected energy, it validates that the experiment conditions are capable of measuring solar energy effectively.
• Experimental Group: The experimental group features the new solar panel design. By comparing the energy output from this group against both the controls, any significant output variation would indicate the efficiency of the new design.

4. Investigating the Efficacy of a New Fertilizer on Plant Growth

This hypothetical study investigates the efficacy of a newly formulated fertilizer on plant growth. The study involves three sets of plants: a set without any fertilizer (negative control), a set treated with an established fertilizer known to promote plant growth (positive control), and a third set fed with the new fertilizer (experimental group).

• Negative Control: The negative control involves a set of plants not receiving any fertilizer. Lack of significant growth in this group will confirm that any observed growth in other groups is due to the applied fertilizer rather than other uncontrolled factors.
• Positive Control: The positive control involves another set of plants treated with a well-known fertilizer, expected to promote plant growth. Adequate growth in these plants will validate that the experimental conditions are suitable to detect the influence of a good fertilizer on plant growth.
• Experimental Group: The experimental group consists of the plants subjected to the newly formulated fertilizer. Investigating the growth in this group against the growth in the control groups will provide ascertained evidence whether the new fertilizer is efficient or not.

5. Evaluating the Impact of a New Teaching Method on Student Performance

This hypothetical study aims to evaluate the impact of a new teaching method on students’ performance. This study involves three groups, a group of students taught through traditional methods (negative control), another group taught through an established effective teaching strategy (positive control), and one more group of students taught through the new teaching method (experimental group).

• Negative Control: The negative control comprises students taught by standard teaching methods, where you expect satisfactory but not top-performing results. Any unexpected high results in this group could signal external factors such as private tutoring or independent study, which in turn may distort the experimental outcome.
• Positive Control: The positive control consists of students taught by a known efficient teaching strategy. High performance in this group would prove that the experimental conditions are competent to detect the efficiency of a teaching method.
• Experimental Group: This group consists of students receiving instruction via the new teaching method. By analyzing their performance against both control groups, any difference in results could be attributed to the new teaching method, determining its efficacy.

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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Control Group vs Experimental Group

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Associate Editor for Simply Psychology

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Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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In a controlled experiment , scientists compare a control group, and an experimental group is identical in all respects except for one difference – experimental manipulation.

Differences

Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control or experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are identical except for manipulating the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

Control Group

A control group consists of participants who do not receive any experimental treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

The difference between the control and experimental groups is that the control group is not exposed to the independent variable , which is thought to be the cause of the behavior being investigated.

Researchers will compare the individuals in the control group to those in the experimental group to isolate the independent variable and examine its impact.

The control group is important because it serves as a baseline, enabling researchers to see what impact changes to the independent variable produce and strengthening researchers’ ability to draw conclusions from a study.

Without the presence of a control group, a researcher cannot determine whether a particular treatment truly has an effect on an experimental group.

Control groups are critical to the scientific method as they help ensure the internal validity of a study.

Assume you want to test a new medication for ADHD . One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.

Types of Control Groups

Positive control group.

• A positive control group is an experimental control that will produce a known response or the desired effect.
• A positive control is used to ensure a test’s success and confirm an experiment’s validity.
• For example, when testing for a new medication, an already commercially available medication could serve as the positive control.

Negative Control Group

• A negative control group is an experimental control that does not result in the desired outcome of the experiment.
• A negative control is used to ensure that there is no response to the treatment and help identify the influence of external factors on the test.
• An example of a negative control would be using a placebo when testing for a new medication.

Experimental Group

An experimental group consists of participants exposed to a particular manipulation of the independent variable. These are the participants who receive the treatment of interest.

Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.

An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.

Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group that listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group, and the groups listening to music, whether with or without lyrics, are the two experimental groups.

Frequently Asked Questions

1. what is the difference between the control group and the experimental group in an experimental study.

Put simply; an experimental group is a group that receives the variable, or treatment, that the researchers are testing, whereas the control group does not. These two groups should be identical in all other aspects.

2. What is the purpose of a control group in an experiment

A control group is essential in experimental research because it:

Provides a baseline against which the effects of the manipulated variable (the independent variable) can be measured.

Helps to ensure that any changes observed in the experimental group are indeed due to the manipulation of the independent variable and not due to other extraneous or confounding factors.

Helps to account for the placebo effect, where participants’ beliefs about the treatment can influence their behavior or responses.

In essence, it increases the internal validity of the results and the confidence we can have in the conclusions.

3. Do experimental studies always need a control group?

Not all experiments require a control group, but a true “controlled experiment” does require at least one control group. For example, experiments that use a within-subjects design do not have a control group.

In  within-subjects designs , all participants experience every condition and are tested before and after being exposed to treatment.

These experimental designs tend to have weaker internal validity as it is more difficult for a researcher to be confident that the outcome was caused by the experimental treatment and not by a confounding variable.

4. Can a study include more than one control group?

Yes, studies can include multiple control groups. For example, if several distinct groups of subjects do not receive the treatment, these would be the control groups.

5. How is the control group treated differently from the experimental groups?

The control group and the experimental group(s) are treated identically except for one key difference: exposure to the independent variable, which is the factor being tested. The experimental group is subjected to the independent variable, whereas the control group is not.

This distinction allows researchers to measure the effect of the independent variable on the experimental group by comparing it to the control group, which serves as a baseline or standard.

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.

Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.

Understanding Control Groups for Research

Introduction

What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.

A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.

The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.

As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .

Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.

In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.

Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.

The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.

Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.

Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.

However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.

Workplace efficiency research

How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.

If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.

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Mental health research

Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.

Educational research

Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?

A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.

As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.

Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.

There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.

No-treatment control group

This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.

Placebo control group

In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.

Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.

Positive control group

Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.

If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.

Historical control group

Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.

Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.

That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.

Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.

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Negative Control vs. Positive Control

What's the difference.

Negative control and positive control are two important concepts in scientific experiments. The negative control is a group or sample that is not exposed to the experimental treatment or condition being tested. It is used to ensure that any observed effects are not due to factors other than the treatment being investigated. On the other hand, the positive control is a group or sample that is exposed to a known treatment or condition that is expected to produce a specific response. It is used to validate the experimental setup and demonstrate that the experiment is capable of detecting the expected response. In summary, while the negative control helps to rule out confounding factors, the positive control serves as a benchmark to confirm the validity and sensitivity of the experiment.

Further Detail

Introduction.

In scientific experiments, control groups play a crucial role in ensuring the validity and reliability of the results. Control groups are used to establish a baseline against which the experimental groups are compared. Within control groups, there are two main types: negative control and positive control. While both types serve important purposes, they differ in their attributes and the roles they play in experimental design and analysis. In this article, we will explore and compare the attributes of negative control and positive control, shedding light on their significance in scientific research.

Negative Control

A negative control is an experimental group in which no response or effect is expected. It is designed to provide a baseline for comparison, ensuring that any observed effects in the experimental group are not due to external factors or random chance. Negative controls are typically treated identically to the experimental group, except for the absence of the variable being tested. By comparing the results of the experimental group to the negative control, researchers can determine if the observed effects are truly caused by the variable under investigation.

One of the key attributes of a negative control is that it helps identify and account for any potential confounding variables. Confounding variables are factors that may influence the outcome of an experiment but are not the variable of interest. By including a negative control, researchers can ensure that any observed effects are not due to these confounding variables, as the negative control group should exhibit no response or effect.

Another attribute of a negative control is that it helps establish the baseline level of variability in the experimental system. By comparing the results of the experimental group to the negative control, researchers can assess the natural variability in the system and determine if the observed effects are statistically significant. This is particularly important in experiments where small changes or subtle effects are being investigated.

Furthermore, negative controls are essential for quality control purposes. They help researchers identify any potential issues with the experimental setup, reagents, or procedures. If the negative control group shows unexpected results, it indicates a problem that needs to be addressed before drawing conclusions from the experimental group. Negative controls act as a reference point, ensuring the reliability and reproducibility of the experiment.

In summary, negative controls provide a baseline for comparison, help identify confounding variables, establish the baseline variability, and act as a quality control measure in scientific experiments.

Positive Control

A positive control is an experimental group in which a known response or effect is expected. It is designed to validate the experimental setup and procedures, ensuring that the system is capable of producing the desired outcome. Positive controls are treated identically to the experimental group, except for the inclusion of the variable that is known to elicit a response.

One of the key attributes of a positive control is that it serves as a benchmark for comparison. By comparing the results of the experimental group to the positive control, researchers can assess the sensitivity and reliability of the experimental system. If the positive control does not produce the expected response, it indicates a problem with the experimental setup or procedures, allowing researchers to troubleshoot and make necessary adjustments.

Another attribute of a positive control is that it helps establish the validity of the experimental results. By including a positive control, researchers can demonstrate that the experimental system is capable of detecting the expected response. This is particularly important in experiments where the outcome may be subtle or difficult to measure. The positive control group provides confidence in the experimental design and the ability to detect the desired effect.

Furthermore, positive controls are crucial for standardization and comparison across different experiments or laboratories. By using a known positive control, researchers can ensure that their results are comparable to those obtained by other researchers. This allows for the replication and validation of findings, contributing to the overall progress of scientific knowledge.

In summary, positive controls validate the experimental setup, serve as a benchmark for comparison, establish the validity of results, and enable standardization and comparison across experiments.

Negative control and positive control are both essential components of experimental design in scientific research. While negative controls provide a baseline for comparison, identify confounding variables, establish baseline variability, and act as a quality control measure, positive controls validate the experimental setup, serve as a benchmark for comparison, establish the validity of results, and enable standardization. By incorporating both types of controls, researchers can ensure the reliability, validity, and reproducibility of their experiments, ultimately advancing our understanding of the natural world.

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What is a control group?

Last updated

6 February 2023

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The independent variable is the thing the researchers are testing. They are trying to determine whether it’s responsible for any change that occurs in the experiment. The research control group is key for this as it allows them to isolate the independent variable’s effect on the experiment.

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• What is a control group in simple terms?

Splitting the audience you’re testing into two identical groups will give you a control group and an experimental group.

Nothing will change for the control group during the research. For example, this group would receive a placebo in pharmaceutical research.

In contrast, one key variable changes for the experimental group. In a pharmaceutical experiment, researchers might administer a different drug. In advertising research, this might involve increasing the experimental group’s exposure to ads.

When evaluating the results, researchers will compare those obtained from the experimental group against the control group. The control group is the baseline.

In research where the two groups are truly identical, seeing different results between the groups suggests they were caused by the independent variable—the only thing that changed.

Control gr oup examples

Examples of control groups in research exist in a wide range of business contexts. For example:

You want to test whether a 15% loyalty discount for repeat purchases would positively impact retention and revenue. So, you send a discount email to 50% of your customers who were randomly selected. The other 50% of customers are your control group.

You want to test whether a personal sales call will increase your chance of a sales conversion. You add this step to your existing nurturing campaign for a randomly selected portion of leads. Those who don’t receive a phone call are your control group.

You want to test whether different product packaging can change brand perceptions. To do this, you change the packaging for a randomly selected portion of customers. Customers who receive the same packaging as before are your control group. Sending a survey to all customers about their brand perceptions before and after the experiment will reveal the impact of the new packaging.

These are just some of the countless examples of control groups. Perhaps the most well-known example is in the medical field, where placebos treatments are used. Control groups receive placebo treatments under the exact same conditions as the experimental group to determine the treatment’s effects.

• The importance of control groups

Control groups matter in research because they act as the benchmark to establish your results’ validity . They enable you to compare the results you see in your experimental group and determine if the variable you changed caused a different outcome. 

Control groups and experimental groups should be identical in their makeup and environment in every possible way. You’ll be able to draw more definitive conclusions as long as the research process is identical for both groups. In other words, working with control groups improves your research’s internal validity .

• Control groups in experiments

Control groups are most common in experimental research, where you’re trying to determine the impact of a variable you’re changing. You split your research group into two groups that are as identical as possible. One receives a placebo, for example, while the other receives a treatment.

In this environment, the identical makeup of the group is essential. The most common way to accomplish this is by randomly splitting the group in two and ensuring that any variables you’re not testing remain the same throughout the research process.

You can also conduct experiments with multiple control groups. For example, when testing new ad messaging, the split between two control groups and one experimental group may be as follows:

Control group 1 receives no advertising

Control group 2 receives the existing advertising

Control group 3 receives the new ad messaging

This more complex type of experiment can test both the overall impact of ads and how much of that impact you could attribute to the new messaging.

• Control groups in non-experimental research

Control groups are less common in non-experimental research but can still be useful. They most commonly occur in the following process designs:

Matching design

In this research process, every person in the experimental group is matched to one other person based on their environmental and demographic similarities.

This is most common when randomly selecting two groups on a broader scale would not result in them being equal. It can help you ensure that the control group or individual continues to act as the baseline for the variable you are studying.

Quasi-experimental design

This is where multiple groups are part of the research, but they are not randomly assigned to test and control conditions.

Quasi-experimental design is most common when the groups you are studying already exist, like customers being shown new ad messaging versus non-customers. The control group in this example is made up of your non-customers, as the variable did not change for them.

• Two common types of control groups

While control groups tend to be similar across research contexts, they generally fall into two categories: negative and positive control groups.

Negative control groups

The independent variable does not change in a negative control group. This group represents the true status quo, and you would test the experimental group against it.

Examples of negative control groups include many of the experiments listed above, like only changing product packaging or only offering a discount for one group of customers.

Positive control groups

In positive control groups, the independent variable is changed where it is already known to have an effect. You would compare this group’s results against those from the experimental group receiving a variation of the same independent variable. This would enable you to determine if the effect changes.

In the example of a multi-control group experiment seen above, control group 1 (receiving no advertising) is a negative control group, while control group 2 (receiving the current level of advertising) is a positive control group.

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

Malay S, Chung KC. The choice of controls for providing validity and evidence in clinical research . Plast Reconstr Surg. 2012 Oct;130(4):959-965. doi:10.1097/PRS.0b013e318262f4c8

National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

Myers A, Hansen C. Experimental Psychology . Belmont, CA: Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Scientific Control Group

A scientific control group is an essential part of many research designs, allowing researchers to minimize the effect of all variables except the independent variable. The control group, receiving no intervention, is used as a baseline to compare groups and assess the effect of that intervention.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

If you wanted to determine the effectiveness of a program designed to improve school reading level, you would naturally be interested in measuring children’s reading level before and after doing the program. If you did exactly this with a group of children, and their reading level improved by 50%, you would then have to answer a tricky question: how do you know the children’s reading level wouldn’t have improved on its own anyway?

What is a Scientific Control Group?

The most common way to avoid this is to build a control group into the research design. Normal biological variation, researcher bias and environmental variation are all external variables that can interfere with the relationship you are trying to understand. “Anchoring” one group by making it identical to another group in all ways except for one variable gives you much more insight into that variable.

Experimental / Treatment Group: Receives the treatment or intervention, usually manipulation of the independent variable. Control Group: Receives no treatment or intervention, or else receives standard treatment that can be understood as a baseline.

As well as controlling for variables in this way, control groups in the experimental design also give an indication of the magnitude of effect. If the researcher discovers that children who didn’t take the program still increased their reading level by 10%, he can reason that not all the result he sees in his experimental group is due to the program alone. Control groups allow for meaningful comparisons to be drawn.

Another example is an experiment that uses a placebo. A medical study will use two groups, giving one group the real medicine and the other a placebo. A placebo has no effect but is indistinguishable from an intervention that does, for example a pill that resembles medicine but is really just made of sugar. Researchers learnt early on that a patient’s condition can improve merely because of their belief that a treatment will work. This is called the placebo effect and is one of the most common reasons for including a control group.

In this particular type of research, the experiment is double blind , meaning neither the doctors nor the patients are aware of which pill they are receiving, eliminating potential research bias. Another precaution is to randomly assign participants to either control or treatment group, to try and make the two groups as similar as possible.

In addition to the placebo effect, the Hawthorne Effect is another phenomenon where, if people know that they are the subjects of an experiment, they automatically change their behavior. Researchers sometimes design ingenious ways to get around this, usually by telling participants they are testing for one thing while actually testing for another. This can be a very clever approach, as long as care is given to the ethics of the study first.

In the social sciences, control groups are an especially important part of the experiment , because it often very difficult to eliminate all of the confounding variables and bias.

There are two main types of control, positive and negative, both providing researchers with ways of increasing the statistical validity of their data.

Positive Scientific Control Groups

A positive scientific control group is a control group that is expected to have a positive result. By using a treatment that is already known to produce an effect, the researcher can compare the test results with the (positive) control and see whether the results can match the effect of the treatment known to work..

For example, a researcher testing the effect of new antibiotics on Petri dishes of bacteria, may use an established antibiotic that is known to work as the control. If all the samples of the new antibiotic fail, except the established antibiotic, it’s likely that the new antibiotics are ineffective.

However, if the control fails too, there may be something wrong with the design. Positive scientific control groups reduce the chances of false negatives.

Negative Scientific Control Groups

In a negative scientific control group, no result is expected. In this case, the control group ensures that no confounding variable or bias has affected the results.

In the same antibiotic example, the negative control group would be a Petri dish of bacteria with no antibiotic of any kind added. The results of the control and the experimental group are then compared. This allows the researcher to show that any reduction of bacteria in the experimental group is due to the effect of the new antibiotic being tested, since it didn’t happen in the control Petri dish.

If all new antibiotic inhibited the bacteria, but the negative control group also did, then some other variable may have had an effect, confounding the results.

Finally, control groups can be sued to establish a baseline. For example, a researcher testing the radioactivity levels of various samples with a Geiger counter would also sample the background level, allowing them to adjust the results accordingly. The background level serves as a control.

Establishing strong scientific control groups is arguably a more important part of any scientific design than the actual samples. Failure to provide sufficient evidence of strong control groups can completely undermine a study, however high significance-levels indicate low probability of error .

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Martyn Shuttleworth , Lyndsay T Wilson (Jun 16, 2010). Scientific Control Group. Retrieved Nov 05, 2024 from Explorable.com: https://explorable.com/scientific-control-group

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The Difference Between Control Group and Experimental Group

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In an experiment , data from an experimental group is compared with data from a control group. These two groups should be identical in every respect except one: the difference between a control group and an experimental group is that the independent variable is changed for the experimental group, but is held constant in the control group.

Key Takeaways: Control vs. Experimental Group

• The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group.
• A single experiment may include multiple experimental groups, which may all be compared against the control group.
• The purpose of having a control is to rule out other factors which may influence the results of an experiment. Not all experiments include a control group, but those that do are called "controlled experiments."
• A placebo may also be used in an experiment. A placebo isn't a substitute for a control group because subjects exposed to a placebo may experience effects from the belief they are being tested; this itself is known as the placebo effect.

What Are Is an Experimental Group in Experiment Design?

An experimental group is a test sample or the group that receives an experimental procedure. This group is exposed to changes in the independent variable being tested. The values of the independent variable and the impact on the dependent variable are recorded. An experiment may include multiple experimental groups at one time.

A control group is a group separated from the rest of the experiment such that the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results.

While all experiments have an experimental group, not all experiments require a control group. Controls are extremely useful where the experimental conditions are complex and difficult to isolate. Experiments that use control groups are called controlled experiments .

A Simple Example of a Controlled Experiment

A simple example of a controlled experiment may be used to determine whether or not plants need to be watered to live. The control group would be plants that are not watered. The experimental group would consist of plants that receive water. A clever scientist would wonder whether too much watering might kill the plants and would set up several experimental groups, each receiving a different amount of water.

Sometimes setting up a controlled experiment can be confusing. For example, a scientist may wonder whether or not a species of bacteria needs oxygen in order to live. To test this, cultures of bacteria may be left in the air, while other cultures are placed in a sealed container of nitrogen (the most common component of air) or deoxygenated air (which likely contained extra carbon dioxide). Which container is the control? Which is the experimental group?

Control Groups and Placebos

The most common type of control group is one held at ordinary conditions so it doesn't experience a changing variable. For example, If you want to explore the effect of salt on plant growth, the control group would be a set of plants not exposed to salt, while the experimental group would receive the salt treatment. If you want to test whether the duration of light exposure affects fish reproduction, the control group would be exposed to a "normal" number of hours of light, while the duration would change for the experimental group.

Experiments involving human subjects can be much more complex. If you're testing whether a drug is effective or not, for example, members of a control group may expect they will not be unaffected. To prevent skewing the results, a placebo may be used. A placebo is a substance that doesn't contain an active therapeutic agent. If a control group takes a placebo, participants don't know whether they are being treated or not, so they have the same expectations as members of the experimental group.

However, there is also the placebo effect to consider. Here, the recipient of the placebo experiences an effect or improvement because she believes there should be an effect. Another concern with a placebo is that it's not always easy to formulate one that truly free of active ingredients. For example, if a sugar pill is given as a placebo, there's a chance the sugar will affect the outcome of the experiment.

Positive and Negative Controls

Positive and negative controls are two other types of control groups:

• Positive control groups are control groups in which the conditions guarantee a positive result. Positive control groups are effective to show the experiment is functioning as planned.
• Negative control groups are control groups in which conditions produce a negative outcome. Negative control groups help identify outside influences which may be present that were not unaccounted for, such as contaminants.
• Bailey, R. A. (2008). Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). "The placebo response: an important part of treatment". Prescriber : 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
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Negative Control Outcomes : A Tool to Detect Bias in Randomized Trials

• 1 Division of Epidemiology, School of Public Health, University of California-Berkeley

Investigators have several design, measurement, and analytic tools to detect and reduce bias in epidemiological studies. One such approach, “negative controls,” has been used on an ad hoc basis for decades. A formal approach has recently been suggested for its use to detect confounding, selection, and measurement bias in epidemiological studies. 1 , 2 Negative controls in epidemiological studies are analogous to negative controls in laboratory experiments, in which investigators test for problems with the experimental method by leaving out an essential ingredient, inactivating the hypothesized active ingredient, or checking for an effect that would be impossible by the hypothesized mechanism. 1 A placebo treatment group in a randomized trial is an example of a negative control exposure (leaving out an essential ingredient) that helps remove bias that can result from participant or practitioner knowledge of an individual’s treatment assignment—the placebo treatment is susceptible to the same bias structure as the actual treatment but is causally unrelated to the outcome of interest.

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Arnold BF , Ercumen A. Negative Control Outcomes : A Tool to Detect Bias in Randomized Trials . JAMA. 2016;316(24):2597–2598. doi:10.1001/jama.2016.17700

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• Published: 05 November 2024

Application of the online teaching model based on BOPPPS virtual simulation platform in preventive medicine undergraduate experiment

• Cai-Yun Chen 1   na1 ,
• Xiu-Wen Shi 1   na1 ,
• Shu-Ying Yin 1 ,
• Nai-Yuan Fan 1 ,
• Tian-Yuan Zhang 1 ,
• Xue-Ning Zhang 1 ,
• Chang-Ting Yin 1 &

BMC Medical Education volume  24 , Article number:  1255 ( 2024 ) Cite this article

Metrics details

As online teaching gains prevalence in higher education, traditional face-to-face methods are encountering limitations in meeting the demands of medical ethics, the availability of experimental resources, and essential experimental conditions. Consequently, under the guidance of the BOPPPS (bridge-in, objective, preassessment, participatory learning, postassessment, summary) teaching model, the application of virtual simulation platform has become a new trend. The purpose of this study is to explore the effect of BOPPPS combined with virtual simulation experimental teaching on students’ scores and the evaluation of students’ participation, performance and teachers’ self-efficacy in preventive medicine experiment.

Students from Class 1 and Class 2 of 2019 preventive medicine major in Binzhou Medical University were selected as the research objects. The experimental group (class 2) ( n  = 51) received the teaching mode combined with BOPPPS and virtual simulation platform, while the control group (class 1) ( n  = 49) received the traditional experimental teaching method. After class, the experimental report scores, virtual simulation scores, students’ engagement scale (SES), Biggs questionnaires, and teachers’ sense of self-efficacy (TSES) questionnaires were analyzed.

The experimental report results demonstrated a significant increase in the total score of the experimental group and the scores of each of the four individual experiments compared to the control group ( P  < 0.05). To investigate the impact of the new teaching model on students’ learning attitudes and patterns, as well as to evaluate teachers’ self-efficacy, a questionnaire survey was administered following the course. The SES results showed that students in the experimental group had high performance scores on the two dimensions of learning methods and learning emotions ( t  = 2.476, t  = 2.177; P  = 0.015, P  = 0.032). Furthermore, in the Biggs questionnaire, the total deep learning score of the experimental group was higher than that of the control group ( t  = 2.553, P  = 0.012), and the deep learning motivation score of the experimental group was higher than that of the control group ( t  = 2.598, P  = 0.011). The TSES questionnaire shows that most teachers think it is easier to manage students and the classroom and easier to implement teaching strategies under this mode.

Conclusions

The combination of BOPPPS and the virtual simulation platform effectively enhances the experimental environment for students, thereby improving their academic performance, engagement and learning approach in preventive medicine laboratory courses.

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Introduction

Preventive medicine is a discipline that studies the relationship between external environmental factors and human health. It is a highly practical and applied discipline [ 1 ]. Experimental teaching is an important part of preventive medicine teaching and plays an important role in the training of public health talents [ 2 ]. Traditional laboratory teaching methods have long been the cornerstone of undergraduate preventive medicine education. While these methods are effective in many respects, they encounter significant limitations when applied to experiments involving hazardous chemicals and biological agents. Such experiments often cannot be conducted in a laboratory setting due to safety and ethical concerns. Addressing these challenges will require exploring alternative approaches and refining existing methodologies [ 3 , 4 ]. Therefore, there is an urgent need for a novel pedagogical model to enhance the experimental teaching of preventive medicine. This model should aim to significantly improve instructional quality, foster student engagement, and promote autonomous learning capabilities.

The utilization of virtual simulation experiment teaching represents a novel pedagogical approach that effectively addresses the limitations associated with traditional experimental teaching methods [ 5 ]. This teaching model is a visual experimental operating environment constructed by using computer and virtual reality (VR) technology to simulate experiments by operating computers. It breaks through the limitations of time, region and experimental resources, and achieves the teaching effect that traditional experiments cannot achieve. Therefore, it has been widely used in practical teaching [ 4 ]. Recent research indicates that virtual simulation can effectively improve students’ academic performance, fosters students’ intrinsic motivation and satisfaction while underscoring the practical application of foundational knowledge, can significantly enhance student engagement [ 6 , 7 , 8 ]. Nevertheless, it is not without its limitations and shortcomings, including an absence of clearly defined learning objectives and effective interaction, and students are still in a passive learning approach. BOPPPS, or instructor-learner interaction teaching model, was proposed by Douglas Cole of the University of British Columbia as a goal-oriented and student-centered teaching method. This model consists of six interconnected phases: bridge-in (B), objective (O), preassessment (P), participatory learning (P), postassessment (P), and summary (S). These elements are closely linked to teaching activities, forming a comprehensive closed-loop teaching unit [ 9 , 10 , 11 , 12 ]. In recent years, this model has been increasingly adopted in medical education, resulting in commendable teaching outcomes [ 9 ]. The research findings demonstrate that this model effectively integrates teaching content with instructional methods, enabling educators to systematically organize the teaching process. It enhances interactive communication and feedback between teachers and students and activates students’ subjective initiative in learning. This approach addresses the limitations of virtual simulation teaching, thereby improving the overall effectiveness of the educational experience [ 6 , 12 , 13 ]. In educational reform, the Students’ Engagement Scale (SES), the Biggs Questionnaire (Biggs), and the Teachers’ Sense of Students’ Engagement Scale (TSES) are commonly used to assess the effectiveness and indispensability of instructional approaches. These tools have also been instrumental in enhancing the BOPPPS teaching model by evaluating student engagement, learning strategies, and pedagogical outcomes [ 14 ].

BOPPPS and virtual simulation platforms have individually been implemented in the teaching of medical specialties with positive outcomes [ 10 , 15 ]. However, combination use is rare. Therefore, this study integrates the BOPPPS teaching model with a virtual simulation platform to evaluate its effects on students’ achievement, engagement, learning approaches and teachers’ self-efficacy in preventive medicine experiments, as assessed through experimental reports and questionnaire results. The aim is to enhance the learning experience in preventive medicine laboratories, ignite students’ passion and curiosity, and foster their ability to integrate theory with practical application.

Participants

The participants were 100 undergraduates of preventive medicine in Binzhou Medical University, class 2019. They were randomly divided into two groups according to class. There were no statistically significant differences in gender, learning interest attitude, and previous academic performance between the two groups ( P  > 0.05). Among them, the control group (Class 1) comprised 49 students who received traditional experimental teaching methods, while the experimental group (Class 2) consisted of 51 students who were exposed to the new experimental teaching model of BOPPPS combined with a virtual simulation platform. All participants have informed consent to the content of this study.

In this study, four experiments that meet the requirements of virtual simulation experiment were selected from six experiments for teaching reform. Both the experimental group and the control group carried out four experimental teaching (OPI, SPI, BFS, FCD), and the course hours were arranged for 3 class hours.

The control group was taught by traditional experimental teaching methods. Before class, the teacher forwarded the learning objective, learning focus, and accompanying learning materials to the students based on the lesson content. And students previewed before class according to the above content. In class, the teacher initially presented the primary learning objectives and content of the lesson, utilizing instructional videos related to the experiment to enhance students’ comprehension. The students took notes as they listened. After the video ended, the teacher used a PowerPoint presentation to display case studies and related questions regarding the experiment, followed by group discussions among students. Subsequently, both the teacher and students engaged in an in-depth analysis of the case. Finally, the teacher summarize the key points and precautions of this experiment course. After class, the students completed their experiment reports, which the teacher then corrected and provided feedback on.

The experimental group adopted virtual simulation teaching based on BOPPPS. The BOPPPS-Virtual simulation platform flowchart is summarized in Fig.  1 . Before class, the same procedure was used for the experimental and control groups, except that the experimental group was given relevant experimental focus and materials according to the new instructional design. During the class, the BOPPPS teaching model is structured into six distinct components. Bridge-in (B): The teacher played relevant videos according to the course content to stimulate students’ interest. Through this visual medium, students acquire a comprehensive understanding of the experiment’s contextual background. Objective (O): The teacher defined the important and difficult points of this experiment course according to the teaching syllabus and presents them to the students in the form of courseware. Preassessment (P): By asking students about the experimental knowledge in this section, we can understand the degree of mastery of experimental knowledge by students before class. Participatory learning (P): Participatory learning allowed students to engage in the learning process, positioning them as the core participants. Using a virtual simulation experiment platform, students completed a series of experiments, namely Organophosphate poisoning (OPI), solanine poisoning (SPI), bulk food sampling (BFS) and formaldehyde content determination (FCD). The virtual simulation platform had multiple virtual places built in, and each place simulated different public health events (Fig.  2 ). After mastering the basic knowledge, students selected the scene related to the experiment in this section according to the course content and complete the experiment process by following the system prompts on the computer. Taking the case of food poisoning as an example (Fig.  3 ), the platform adopted the form of 3D real scene simulation to model the real event as the background. The scene began with a briefing describing the background of the patient’s food poisoning. Subsequently, the user could interact with the virtual patient through the dialogue, asking about the previous dietary history and looking for the cause of food poisoning. The physiological parameters of the patients were monitored, and the patients were observed and treated. Immediately after the simulation, the platform presented an accident cause interface. Moreover, the simulation report was given to inform the correct cause of food poisoning. So that students can understand the correct process. Postassessment (P): After the completion of the experiment on the virtual simulation experiment platform, the platform formed operational scores. This was the result of the postassessment. Summary (S): Finally, the teacher made a specific summary of this experiment class according to the operation scores of the virtual simulation platform and helped them to establish a thorough knowledge framework. After class, the students proceeded to consolidate their understanding in alignment with the teacher’ s summary, while concurrently finalizing the experimental report. Moreover, the teacher gives feedback to the students according to the results of experiment report.

Example of class design for the BOPPPS model

Virtual simulation experiment of preventive medicine

Virtual simulation experiment of “food poisoning”

Effectiveness assessment

At the conclusion of the course, the effectiveness and satisfaction of the two different teaching methods were evaluated based on scores from experimental reports, virtual simulation platform tests of the experimental group, and the results of three distinct questionnaire surveys. These questionnaires were distributed by the instructor via the “Dui Fen Yi” WeChat public account.

Student learning

Experimental report scores: Both the experimental group and the control group were evaluated using the same experimental report. The results of the experimental report consisted of four scores: OPI, SPI, BFS, and FCD. Each test was graded on a 100-point scale.

Virtual simulation platform test scores of the experimental group: The results of the virtual simulation platform were composed of four test scores. Respectively, there were OPI, SPI, BFS, and FCD. Each examination was based on a 100-mark system. The total score was the average of the four experimental scores.

Student engagement

The Students’ engagement scale (SES) questionnaire mainly involves four aspects: learning method, emotions, participation and manifestation [ 16 ]. Each dimension contains different test items, so as to better understand the students’ learning situation. Five-point Likert scales were used to evaluate the questionnaire variables. The scoring options were 1 = not at all like me, 2 = not quite like me, 3 = not sure, 4 = like me, 5 = very much like me, the Cronbach alpha coefficient in this study was 0.816–0.871.

Learning approach

The Biggs questionnaire delves into both deep and shallow learning approaches [ 17 ], encompassing various learning strategies and motivations within each approaches. The scoring options available were: 1 = never, 2 = sometimes, 3 = half of each, 4 = often, 5 = always. The Cronbach alpha coefficient in this study was 0.747–0.892.

Teacher self-efficacy

The Teachers’ Sense of Self-Efficacy (TSES) questionnaire includes three aspects: student management, teaching strategy implementation and classroom management. Each dimension contains different test items, which is an effective tool to evaluate teachers’ feedback on teaching efficacy. According to the feedback, the score is 1–5 points. The scoring options available were: 1 = completely impossible, 2 = almost impossible, 3 = able to do a little, 4 = basically able, 5 = completely achievable. Cronbach’ s alpha coefficient in this study was 0.711–0.862.

Statistical analysis

The SPSS version 26.0 software was used for data analysis. The measurement data were expressed as mean ± standard deviation(‾ x  ±  s ), and the independent sample t -test was used to compare between different groups. Counting data are expressed in frequency and percentage, using the χ² test to compare the percentage of people with 4 points or more. The total scores of both the experimental report and the virtual simulation platform test are derived from the percentage-based evaluation of four experimental results. Due to the non-normal distribution of the experimental report scores, a statistical description was employed using the median (inter-quartile range), M ( P 25, P 75), and a non-parametric rank sum test was utilized for group comparisons. Test level a = 0.05.

Student Learning

Experimental report scores.

Table  1 presents an analysis of the experimental report scores for the experimental and control groups. The total score for the experimental group 382.00 (377.00, 386.67) was significantly higher than that of the control group 374.00 (360.00, 379.00) ( z  = 5.311, P  = 0.001). Furthermore, the scores for each of the four individual experiments were significantly higher in the experimental group compared to the control group ( P  < 0.05).

Virtual simulation platform test scores of the experimental group.

The correspondence analysis was made between the Student IDs and the four scores of the experimental group students. The results of the correspondence analysis show that the students in the second quadrant have better BFS scores, but fewer students; students in the third quadrant have better SPI scores; students in the fourth quadrant have better OPI scores, and the number of students is relatively large, indicating that students are relatively good at OPI operation. Therefore, it is necessary to strengthen students’ operation ability and experimental knowledge of FCD and BFS experiments to improve their performance (Fig.  4 ).

Correspondence analysis result

Questionnaire result

Both groups of students and teachers participated in the survey and completed the questionnaire. Notably, the questionnaire recovery rate and effective rate were both 100%.

As shown in SES, statistical analysis revealed that the learning methods and learning emotions scores of the students in the experimental group was higher than that of the control group ( t  = 2.476, t  = 2.177; P  = 0.015, P  = 0.032) (Table  2 ). On the learning method dimension, students in the experimental group demonstrated greater effectiveness in pre-learning before class (3.294 ± 0.923) and keep learning (3.765 ± 0.790) compared to the control group ( P  = 0.002; P  = 0.022); In terms of learning emotion, the experimental group scored higher than the control group in doing their best to learn (4.098 ± 0.671), finding ways to maintain interested in the course (3.941 ± 0.732), and really wanting to learn (3.628 ± 0.871) (Table  3 ). The above results showed that the students in the experimental group had better learning enthusiasm and learning situation under the new teaching model.

The results of Biggs showed that the total score of deep learning (33.04 ± 6.190) and the score of deep learning motivation (16.47 ± 3.645) in the experimental group were higher than those in the control group (30.10 ± 5.253;14.69 ± 3.177) (Table  2 ). Further analysis revealed that in the deep learning motivation dimension, the experimental group students found exploring academic issues as engaging as reading novels and watching movies (3.294 ± 1.006), found the content interesting enough to study diligently (3.529 ± 1.102), and sought answers with questions in most classes (2.922 ± 0.997). These scores were higher than those of the control group. In terms of deep learning strategies, the experimental group outperformed the control group by dedicating substantial time outside of class to researching intriguing topics discussed in class (2.961 ± 1.019). On the shallow learning motivation dimension, students in the experimental group who exerted minimal effort to pass the course (2.373 ± 1.095) scored significantly lower ( P  = 0.022), as shown in Table  4 . These findings suggest that the new teaching model enhances deep learning motivation.

In the TSES survey, all teachers (100%) unanimously affirmed their effectiveness of teaching in addressing students’ challenging questions, managing the classroom learning atmosphere, establishing rules to ensure smooth teaching, making students abide by classroom discipline and responding to emergent problems in student learning. In addition to this, the approval rating of teachers in using formative assessment and providing an alternative explanation when students are confused was 93.33%. However, the approval rate was relatively low in the aspects of motivating students who were not interested in learning (40%), improving the comprehension of failing students (46.67%) and providing advanced challenges for students with strong abilities (40%) (Table  5 ). The above results show that teachers think it is easier to manage the classroom and ensure the smooth progress of teaching under the new teaching model. Furthermore, it is recommended that teachers adjust their teaching plans to accommodate both struggling students and those with strong abilities.

In this study, the teaching model of BOPPPS combined with virtual simulation platform was implemented in preventive medicine experiments, and its effects on the performance of preventive medicine students, learning participation, learning approach and self-efficacy of teachers were good.

Enhancing students’ academic performance

The experimental report results indicate that students in the experimental group achieved higher scores compared to those in traditional experimental teaching within the same academic time frame. Based on the results of the correspondence analysis, educators can enhance guidance for lower-scoring experiments and students with lower scores to improve the overall quality of experimental teaching. The effectiveness of the new teaching model can be attributed to its structured learning process, which emphasizes the integration of theory and practice. The virtual simulation experiments alleviate confusion among students by offering standardized and comprehensive guidance throughout the experimental operation, creating a safe and repeatable experimental environment that facilitates deep understanding and exploration of the experimental process. Meanwhile, the BOPPPS model clarifies learning objectives and enhances student engagement and mastery by incorporating interactive feedback, which boosts motivation and initiative. It addresses the shortcomings of passive learning in virtual simulations and improves student performance. However, it places greater demands on teachers, who must adapt their teaching strategies to suit the specific learning situations of their students.

Improvements of students’ autonomy and interest in learning

This study combined the two research models and found that the new model was more conducive to stimulating students’ learning interest and enthusiasm and improving teaching efficiency. The SES questionnaire showed that the experimental group scored higher than the control group in terms of learning methods and learning emotions. In the learning method, the scores of pre-learning before class and keep learning of students in the experimental group were higher than those in the control group. This shows that the new teaching mode gives students a lot of autonomy in learning. Previous research has shown that effective learning methods not only improve teaching quality but also promote students’ self-directed learning [ 18 ]. The enhancement of self-directed learning ability can motivate students to more effectively acquire the essential professional knowledge and skills required for preventive medicine practice. In addition, students in the experimental group were more eager to learn and more interested in the course in their learning emotions, which indicates that the BOPPPS teaching method improves the interest of students in learning. The newly teaching model prioritizes a student-centered approach with comprehensive engagement at every course stage, particularly through the use of virtual simulation. In this framework, students utilize a virtual simulation platform to conduct scenario simulations. Acting as health investigators, students are actively involved from the onset of an incident to its resolution within the virtual environment. Consequently, this model significantly enhances students’ enthusiasm for experimental work compared to traditional teaching methods. These results are consistent with previous studies indicating that the utilization of virtual simulation can enhance students’ sense of experience with virtual scenes and their ability to deal with emergencies [ 19 , 20 , 21 ]. In addition, the multi-dimensional teaching methods employed in the BOPPPS model, including pre-assessment, scene-based teaching, post-assessment, and summary sessions, enable teachers to promptly identify and address students’ shortcomings. These methods not only guide students but also motivate and engage them in their learning process.

Improvements of students’ deep learning motivation

Biggs questionnaire survey results show that in the preventive medicine laboratory course mixed teaching based on BOPPPS combined with the learning mode of virtual simulation platform, the score of deep learning motivation of the students in the experimental group was higher than that of the control group, indicating that the students loved learning, studied seriously and were eager to explore knowledge in the learning process. This study aligns with the findings of Berman et al. [ 22 ]. The combination mode of BOPPPS and simulation platform is connected with each other in teaching, and teachers can flexibly adjust individual links according to different teaching contents. This improves students’ concentration in class. Students constantly think in the process of computer operation, which is conducive to the transformation of learning from shallow layer to deep. Moreover, this study fostered a relaxed, enjoyable, and collaborative learning environment, promoting both independent thinking and teamwork. This approach stimulated students’ self-awareness and enhanced their communication and collaboration skills [ 23 , 24 , 25 ]. The combination of BOPPPS and virtual simulation platform enables students to participate in the whole experiment process and establish their own knowledge system. It stimulates students to think about the experiment process and experiment content and helps to improve students’ innovative thinking ability and strengthen their mastery of knowledge.

The online practice model based on BOPPPS is highly recognized by teachers

Based on the findings of the TSES questionnaire employed in this research, more than 50% of educators believe that the integration of BOPPPS and the virtual simulation platform as an experimental teaching approach facilitates classroom management and the execution of instructional strategies. In addition, the new teaching model can improve “addressing challenging questions from students”, “manage classroom learning atmosphere” and other aspects, all the teachers’ approval rate was as high as 100%. This experimental teaching model imposes elevated demands on teachers’ pedagogical proficiency. Consequently, teachers are no longer mere purveyors of knowledge, but rather assume the roles of knowledge facilitators, process supervisors, and evaluation participants. The BOPPPS teaching mode also has higher requirements for teaching quality [ 26 ]. At the same time, the results of the questionnaire showed that “motivating students who are not interested in learning” (40.0%), “improving the understanding ability of students failing” (46.67%) and “providing advanced challenges for students with strong ability” (40.0%) had low approval rates. The reason for the above may be that, on the one hand, some students only systematically follow the operation of the platform without thinking about it, and do not bring themselves into the identity of an “investigator”. On the other hand, teachers did not follow up students’ learning progress in time during the teaching process. It is suggested that teachers should adjust the teaching content in time according to students’ virtual simulation platform results and students’ feedback. More attention should be paid to students with poor academic performance so as to improve the overall experimental learning performance of students and better mobilize the enthusiasm of students. Moreover, high-achieving students should be encouraged to utilize the virtual simulation platform to conduct additional experiments beyond the classroom requirements, fostering further development of their skills.

Limitations and future directions

BOPPPS and virtual simulation platform have been paid attention to and tried in a variety of educational institutions and teaching practices, and have achieved good practice results [ 22 , 27 ], but there are still some limitations and challenges. First of all, this mode requires high ability of teachers. Teachers should not only master the integration of virtual simulation platform and teaching resources, but also play a role in guiding students to think and discuss problems in offline BOPPPS classroom [ 6 ]. Secondly, the design of virtual simulation experiments remains inadequate. The feedback indicates that certain virtual simulations are overly simplistic and lack interactive elements, which diminishes students’ engagement and interest in the exercises [ 12 ]. Thirdly, the quasi-experimental design of our study was unable to account for all factors, including the psychological aspects of the participants. In view of the above problems, we put forward the following prospects for the future. First of all, teachers need to adjust the teaching plan in time according to the classroom performance of students and the completion of experimental homework after class in the teaching practice, and constantly explore and summarize, to create a teaching model that is in line with themselves and students. Secondly, for the improvement of the virtual simulation platform, the development enterprises of the virtual simulation experiment platform can work together with teachers to continuously improve the application of the platform, so as to improve the learning effect and experience of students. For example, simulate more real-life scenarios, allowing students to apply their knowledge to practical problem-solving. This will enhance the realism and interest of the learning experience. Overall, although the combined use of BOPPPS with virtual simulation platforms is still in its infancy, it is foreseeable that they will receive more attention and applications in the future.

The purpose of this study is to compare the effects of the BOPPPS model integrated with a virtual simulation platform versus the traditional teaching model in a preventive medicine experiment. The findings suggest that the innovative teaching model significantly enhances students’ experimental performance, engagement, and comprehension, while also improving their learning approaches and overall instructional efficiency in preventive medicine. Teachers believe that this new teaching model enhances classroom management and effectively stimulates student motivation. Furthermore, by utilizing the virtual simulation platform, students engaged in simulated experiments that continuously reinforced their theoretical knowledge and improved their ability to respond to public health emergencies. This approach provides valuable insights for training future professionals in public health emergency management. Future research should further explore the effects of this teaching model.

Data availability

All data generated or analysed during this study are included in this manuscript.

Abbreviations

bridge-in (B), objective (O), preassessment (P), participatory learning (P), postassessment (P) and summary (S)

Bulk food sampling

Formaldehyde content determination

Organophosphate poisoning

Power point

The Students’ Engagement Scale

Solanine poisoning

The Teachers’ Sense of Self-efficacy

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Acknowledgements

We thank all colleagues and students who participated in this study.

This research was financially supported by Teaching reform and research topic of Binzhou Medical University (JYKTMS202246, JYKTMS2021058).

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Cai-Yun Chen and Xiu-Wen Shi contributed equally to this work.

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School of Public Health, Binzhou Medical University, Yantai, 264003, People’s Republic of China

Cai-Yun Chen, Xiu-Wen Shi, Shu-Ying Yin, Nai-Yuan Fan, Tian-Yuan Zhang, Xue-Ning Zhang, Chang-Ting Yin & Wei Mi

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C-CY contributed to the conception and design of the study and critically revised all versions of the manuscript. S-XW and MW contributed to the development of the study and analysis of the data and the writing, Y-SY, F-NY, Z-TY, Z-XN and Y-CT participated in the study conceptualization, analysed the data. All authors have read and approved the manuscript.

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Chen, CY., Shi, XW., Yin, SY. et al. Application of the online teaching model based on BOPPPS virtual simulation platform in preventive medicine undergraduate experiment. BMC Med Educ 24 , 1255 (2024). https://doi.org/10.1186/s12909-024-06175-7

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DOI : https://doi.org/10.1186/s12909-024-06175-7

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Conditioned media (CM) is derived from mesenchymal stem cells (MSC) culture and contains biologically active components. CM is easy to handle and reduces inflammation while repairing injured joints. Combination therapy of the CM with cross-linked hyaluronic acid (HA) could ameliorate the beneficial effect of HA in treating degenerative changes of articulating surfaces associated with arthritic rats’ temporomandibular joints (TMJs). This study aimed to evaluate the therapeutic potential of HA hydrogel combined with bone marrow stem cells-conditioned medium (BMSCs-CM) on the articulating surfaces of TMJs associated with complete Freund’s adjuvant (CFA)-induced arthritis. Fifty female Sprague-Dawley rats were divided randomly into five equal groups. Rats of group I served as the negative controls and received intra-articular (IA) injections of 50 µl saline solution, whereas rats of group II were subjected to twice IA injections of 50 µg CFA in 50 µl; on day 1 of the experiment to induce persistent inflammation and on day 14 to induce arthritis. Rats of group III and IV were handled as group II and instead, they received an IA injection of 50 µl HA hydrogel and 50 µl of BMSCs-CM, respectively. Rats of group V were given combined IA injections of 50 µl HA hydrogel and BMSCs-CM. All rats were euthanized after the 4th week of inducing arthritis. The joints were processed for sectioning and histological staining using hematoxylin and eosin, Masson’s trichrome and toluidine blue special staining, and immunohistochemical staining for nuclear factor-kappa B (NF-κB). SPSS software was used to analyze the data and one-way analysis of variance followed by post-hoc Tukey statistical tests were used to test the statistical significance at 0.05 for alpha and 0.2 for beta. In the pooled BMSC-CM, 197.14 pg/ml of platelet-derived growth factor and 112.22 pg/ml of interleukin-10 were detected. Compared to TMJs of groups III and IV, TMJs of group V showed significant improvements ( P  = 0.001) in all parameters tested as the disc thickness was decreased (331.79 ± 0.73), the fibrocartilaginous layer was broadened (0.96 ± 0.04), and the amount of the trabecular bone was distinctive (19.35 ± 1.07). The mean values for the collagen amount were increased (12.29 ± 1.38) whereas the mean values for the NF-κB expression were decreased (0.62 ± 0.15). Combination therapy of HA hydrogel and BMSCs-CM is better than using HA hydrogel or BMSCs-CM, separately to repair degenerative changes in rats’ TMJs associated with CFA-induced arthritis.

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Introduction.

Arthritis affects millions of people worldwide, with an increasing prevalence due to aging 1 . The prevalence of rheumatoid arthritis (RA) is 0.5-1% in the general population, and it is more frequent in women 2 . The frequency of clinical temporomandibular joints (TMJs) involvement in patients with RA ranges from 5 to 86%, with bilateral involvement reported as the most frequent 3 . Izawa et al. 4 reported the prevalence of TMJ osteoarthritis (OA) by magnetic resonance imaging and clinical examinations, estimating 70% in the 73–75 years age group and 25% prevalence in the 20–49 years age group. Arthritis is a chronic or acute joint inflammation that often coexists with structural damage and pain. Arthritis of more than 100 different types has been described, the most common ones are RA and OA 5 . They are both inflammatory joint diseases that involve synovial and joint destruction, immune cell infiltration accompanied by joint swelling, pain, and limited movement. These devastating effects reduce the quality of life, increase dependency on certain medications, and decline the physical function which makes even the simplest tasks seem challenging 6 .

The primary goal in the treatment of arthritis is to find therapies that allow patients to move their joints without pain. Treatments of arthritis could include physical or occupational therapy, hot or cold compresses, joint immobilization, massage and exercise, transcutaneous electrical nerve stimulation, acupuncture, and drugs. Nonsteroidal anti-inflammatory and corticosteroid medications only control symptoms and delay injury but cannot reverse the progression of arthritis. Thus, end-stage degenerative joint diseases may require arthroplasty and osteotomy, but also these procedures can result in serious complications such as prosthetic infection, thromboembolism, pain, and postoperative stiffness 7 . Viscosupplementation with intra-articular (IA) hyaluronic acid (HA) hydrogel injection is a well-established treatment option to contrast joint damage, improve joint function, and reduce pain 8 .

Hyaluronic acid (known as sodium hyaluronate or hyaluronan) is a linear non-sulfated polysaccharide consisting of alternately repeating D-glucuronic and D-N-acetylglucosamine units. HA exists naturally in all vertebrates of humans and animals beings, and almost originated in all body tissues and fluids, such as eye vitreous humor, synovial fluid, and hyaline cartilage 9 . There are several varieties of HA with different molecular weights; low (500–730 kDa), intermediate (800–2000 kDa), and high (2000–6000 kDa), including cross-linked formulations of HA 10 . HA is involved in many key processes including pathobiology, matrix organization, morphogenesis, tissue regeneration, wound reparation, and cell signaling, in addition, it has unique physico-chemical properties, such as viscoelasticity, hygroscopicity, mucoadhesivity, biodegradability, and biocompatibility 11 . The FDA premarket approval database revealed no post-marketing reports concerning unexpected adverse events of HA 12 .

Using mesenchymal stem cells-conditioned medium (MSCs-CM) in regenerative medicine is still in its early stages. MSCs-CM reduces disease severity and immune responses in inflammatory arthritis 13 , has a beneficial effect in enhancing processes associated with chondrocyte OA pathomechanism 14 and represents a new regenerative treatment in several musculoskeletal pathologies 15 . Emerging evidence suggests that MSCs exert effects by generating a wide range of bioactive factors. The factors referred to as CM consists of exosomes, ectosomes, lipid mediators, cell adhesion molecules, chemokines, cytokines, hormones, and growth factors 16 . CM may provide significant benefits over cells in handling, preservation, manufacture, potential as a ready-to-use biologic product, and longevity of the product 17 . As there is a lack of evidence about the beneficial impact of MSC-CM on both immunological and clinical outcomes, and before clinical trials, animal models should be used in pre-clinical research with prolonged observation periods 18 .

Clinically, IA injection of HA for joint arthritis has been widely studied. Concentrated growth factor combined with sodium hyaluronate was effective in TMJ OA treatment 19 . IA injection of platelet rich plasma with HA following arthrocentesis had a synergistic effect in reducing pain and improving function in TMJ OA 20 . CM from stem cells of human exfoliated deciduous teeth alleviates mouse OA by inducing secreted frizzled-related protein 1-Expressing anti-inflammatory M2 macrophages in the synovium 21 . Combined therapy of HA and BMSCs-CM has not been investigated clinically or experimentally on animal models as a treatment option for OA. Only one study evaluated and compared the anti‑inflammatory effect of non‑animal stabilized HA and MSCs-CM in an explant‑based coculture model of OA. The finding indicated that treatments with this combination therapy could be a therapeutic option that may help counteract the catabolism produced by the inflammatory state in knee OA 22 .

There is no treatment that cures arthritis and therefore development of therapeutic alternatives that can prevent the destruction of the joint structures or stimulate its adequate repair is required. Since the combination therapy of HA hydrogel with bone marrow stem cells BMSCs-CM could produce a more effective treatment for arthritis, this study aimed at evaluating the rational use of HA hydrogel combined with BMSCs-CM on the articulating surfaces associated with complete Freund’s adjuvant (CFA)-induced arthritis in rats’ TMJs through using IA injections. The null hypothesis was that the combination therapy of HA hydrogel and BMSCs-CM would have the same effect as HA hydrogel or BMSCs-CM when used separately.

Materials and methods

Fifty, male healthy Sprague-Dawley rats, eight weeks old, weighing 150–200 g were purchased from the Medical Research Centre at Faculty of Medicine, Mansoura University. In a light-controlled room with a 12:12 h light-dark cycle and a temperature of 22 °C, rats were kept in separate cages. The relative humidity was maintained between 65 and 70%. The rats were free to roam and were given water and commercial soft diet. Regarding use and care of animals and for this experiment, the Institutional Animal Care and Use Committee of the Faculty of Dentistry, Mansoura University, Egypt (A14060421), approved this study. The guidelines of the Animal Research: Reporting In Vivo Experiments and the ARRIVE Checklist ( https://www.nc3rs.org.uk/arrive-guidelines ) were followed in performing this study and all methods were performed in accordance with the American Veterinary Medical Association guidelines.

Complete Freund’s adjuvant (Sigma Aldrich, Saint Louis, Missouri, USA). Each 1 mL consists of 1 mg of heat-killed and dried mycobacterium tuberculosis , 0.85 mL paraffin oil, and 0.15 mL mannide monooleate 23 .

Hyaluronic acid hydrogel (Crespine ® Gel, GmbH & Co. KG, Dümmer, Germany). The gel is a cross-linked hyaluronic acid, sterile, viscoelastic, biocompatible, resorbable IA implant of high purity. It contains 1.0 mg hyaluronic acid, 14.0 mg hyaluronic acid cross-linked, 6.9 mg sodium chloride and 1.0 ml water for injection 24 .

Enzyme-linked immunosorbent assay (ELISA) Kits (CAT# CSB-E04595r, Cusabio Biotech Co, Wuhan, China) to measure the amount of platelet-derived growth factor (PDGF) and interleukin-10 (IL10) cytokines in the samples of the BMSC-CM.

Study design

The sample size was calculated using G* Power 3.1.9.2 software. The statistical test was ANOVA (Fixed effects, special, main effects, and interactions) and the test family was F test. The type of power analysis was to estimate the required sample size given the following; power, affect size, and α. The input parameters were effect size, 0.50; power (1-β error probability), 0.80; and α error probability, 0.15. Notably, 5% is commonly used by researchers as a cutoff p-value to determine the significance level. However, the 15% for α is acceptable depending on cost/benefit ratio of the research 25 , 26 The number of groups was 5 and the numerator degree of freedom was 10. The total sample size was 50. The rats were divided randomly into five equal groups using a random-numbers table, 10 rats each. The left TMJ of each rat was used only in this experiment to avoid hindering the chewing ability and feeding. Rats of group I served as the negative controls, and received IA injections of 50 µl saline, whereas rats of group II were subjected to twice IA injections of 50 µg CFA in 50 µl; on day 1 of the experiment to induce persistent inflammation and at day 14 to induce arthritis 27 , 28 Rats of groups III and IV were handled as group II and instead, they received an IA injection of 50 µl HA hydrogel 29 . and 50 µl of the BMSCs-CM, respectively 30 . Rats of group V were given a combined IA injection of 50 µl HA hydrogel and BMSCs-CM 31 . All rats were euthanized after the 4th week of inducing arthritis.

Experimental induction of arthritis

All rats were anesthetized by an intraperitoneal infusion of 25 mg/kg xylazine and 75 mg/kg ketamine. The skin surrounding the TMJ was shaved and was cleaned carefully with 70% ethyl alcohol, and then the postero-inferior border of the zygomatic arch was palpated before IA injections. A 26-gauge needle was inserted underneath this location and was advanced anteriorly and medially contacting with the condyle. The needle’s penetration into the joint space was verified by the loss of resistance after the mandible was moved to confirm this contact. Aspiration was cautiously ruled out before the administration of CFA to avoid intravascular implantation. The suspension was frequently disseminated throughout the articular area by repeatedly extending and flexing the joints 32 . The animals were monitored daily for the onset of arthritis for 22–28 days 28 . In accordance to the Osteoarthritis Cartilage Histopathology Assessment System proposed by Pritzker et al., 33 [OA Research Society International (OARSI)], the OA scoring was estimated according to two parameters; the grade and stage assessing cartilage pathogenesis semi-quantitatively by measuring both the vertical grade (0–6 points) and horizontal stage (0–4 points) progression of OA cartilage manifestations. The overall score is defined as the combined assessment of OA severity and extent 0–24.

Preparation and collection of BMSCs-CM

A cryopreserved rat cell line with cell density 1 × 10 6 from the Nile Centre for Experimental Research, Mansoura, Egypt, was used to produce BMSCs-CM. At passage 3 and after BMSCs reached 70–80% confluence, the Gibco Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12 (Thermo Fisher Scientific, USA) supplemented with 10% fetal bovine serum was removed. The cells were washed thrice with phosphate buffer saline (PBS), and the culture medium was replaced with serum-free minimal essential medium (Thermo Fisher Scientific, USA). Then, the culture flasks were incubated for 72 h before collecting and centrifuging the medium at 1500 rpm for 5 min at 4 o C and the supernatants were collected and centrifuged again at 3000 rpm for 3 min at 4 o C. The resultant supernatants were filtered through a 0.22- µm filter unit and kept in tiny tubes at -80 o C until they were needed in the study 34 , 35 These procedures were completed aseptically using class II A2, UNIL@B biological safety cabinet.

ELISA assay of BMSCs-CM

According to the manufacturer’s instructions, ELISA was performed using the pooled BMSC-CM to determine the concentrations of PDGF and IL10. The quantitative enzyme immunoassay technique was used with specific antibodies for the target PDGF or IL10 proteins. The antibodies had been pre-coated onto a microplate (100 µL). Standards and samples were pipetted into the wells and the immobilized antibody bound any target protein present. After removing any unbound substances, a biotin-conjugated antibody specific for PDGF or IL10 was added to the wells. After washing, avidin-conjugated horseradish peroxidase was added to the wells. Following a wash to remove any unbound avidin-enzyme reagent, a substrate solution was added to the wells, and color was developed in proportion to the amount of protein bound in the initial step. The color development was stopped by adding a stop solution and the intensity of the color was measured using a microplate reader capable of measuring absorbance at 450 nm with the correction wavelength set at 540–570 nm. The quantitative concentration results of PDGF and IL10 were plotted and were compared to a standard curve. The signal was proportional to the concentration of the target and detected after the addition of a substrate solution. The detection range for PDGF was 7.8–500 pg/ml whereas for IL10 was 3.12–200 pg/ml. The minimum detectable dose of rat PDGF-AB was typically less than 1.95 pg/ml whereas for IL10 was typically less than 0.78 pg/ml. The sensitivity of this assay or the lower detection was defined as the lowest protein concentration that could be differentiated from zero.

BMSCs-CM and HA hydrogel injection

HA hydrogel and BMSCs-CM were mixed with a ratio of 1:1 and were shaken by vortex until homogenization to form BMSCs-CM/ HA hydrogel 35 . After inducing arthritis, rats of groups III, IV, and V received respectively a single dose of 50 µl HA hydrogel, 50 µl BMSCs-CM, and 50 µl of HA hydrogel combined with BMSCs-CM via IA injection into the arthritic TMJs using a needle after anesthesia 28 . The IA administration of various therapeutics was applied in a similar way to CFA injection and all rats were euthanized after 4 weeks from inducing arthritis.

Histological examination

At the end of the experiment, each rat was euthanized by intraperitoneal injection of 200 mg/kg sodium pentobarbital euthanasia solution. Each rat was decapitated and its TMJs were removed, and processed for paraffin blocks. The specimens were put in 10% formaldehyde for 24 h. Following fixation, the specimens were rinsed under running water to get rid of any residual fixative. Then, they were decalcified in 10% ethylene diamine tetra acetic acid disodium salt solution of pH 7, for 3–4 weeks. The completeness of decalcification endpoint was determined through mechanical testing by needling in a remote part away from TMJs. An automatic tissue processor was used to prepare the tissue sections as the specimens were dried using a series of progressive alcohol baths before clearing them with xylene. The specimens were infiltrated and were embedded with melted paraffin. Using a microtome, cutting was performed at 4–6 μm thickness. Serial sagittal sections of the TMJ were performed with every fifth section stained with hematoxylin and eosin (H&E) as a routine one, a sixth section for Masson’s trichrome as special staining, and a seventh section for immunohistochemical (IHC) staining for nuclear factor-kappa B (NF-κB) and eighth section for toluidine blue staining. Two histopathologists blindly examined each section.

Digital morphometric analyses

The histomorphometry was performed for the stained sections, and 10 images for each group were assessed. 40 X objective, 1/2 X picture adaptor, and Olympus ® digital camera mounted on an Olympus ® microscope (CX23, Wuhan Aliroad Medical Equipment Co., Ltd, Hubei, China) were used for capturing the images. For Masson’s trichrome and NF-κB, the resulting photos were assessed using Video Test Morphology ® software (Russia), which has a particular built-in routine for the area, % area measuring, item counting, and contact angle. A u-tech ® frame grabber was used to acquire images from the camera. Depending on the hue of target areas, the color tones of images were enhanced. The images were thresholded at the level of the desired hue range for creating a binary mask representing target areas. The region of interest (ROI) was established for the binary mask. To calculate the percentage area of ROI in relation to the overall field area, a calculating technique known as percentage area (%) calculating routine was used. While for articular disc thickness (µm), fibrocartilaginous layer thickness (µm) and amount of trabecular bone (mm 2 ), Image J 1.37v (National Institutes of Health, Bethesda, MD; http://rsb.info.nih.gov/ij/ ) was used. Two blinded examiners separately measured thickness of the articular disc for anterior band, intermediate zone, and posterior band 27 , and thickness of the fibrocartilaginous layer was measured as the surface of the condylar cartilage was divided equally into three parts. The thickness of the fibrocartilage was measured at the quartering points of the posterior third of the condylar cartilage 36 and for the trabecular bone, the scale was set as 300 pixels equal to 1 mm 2 , and a separate surface area was bordered using the polygon selection tool for measurement 28 .

Statistical analyses

Data were tabulated, coded, and analyzed using the computer program SPSS (statistical package for social research) version 17.0. Descriptive statistics of mean and standard deviation (SD) were created from the data. A one-way analysis of variance (ANOVA) was performed to compare data for more than two sets of numerical parametric data, and the post-hoc Tukey test was employed for pairwise comparison. Statistical significance was defined at 0.05 for alpha and 0.2 for beta. To determine the effect size and clinical relevancy between the different groups, Cohen’s d statistical test was used by calculating the mean difference between each two groups, and then dividing the result by the pooled SD.

ELISA results

The PDGF and IL10 were detected in the pooled MSC-CM. High quantities of PDGF and IL10 were detected, 197.14 pg/ml and 112.22 pg/ml, respectively.

H&E histological results

The TMJ’s articular surfaces of group I (negative control) showed normal architectures as they had rounded intact and smooth contours without any soft tissue damages or clefts. The fibrocartilaginous disc was of normal thickness as it had thick posterior and anterior bands separated by a thinner intermediate zone. The condyle consisted of a hyaline cartilaginous plate covered by a superficial layer of dense fibrous connective tissue that includes fibrocartilaginous areas. The resting zone of hyaline cartilage showed a cellular region of small-flattened chondroprogenitor cells, the proliferative one had chondrocytes arranged in columns that were axially orientated to the surface, the hypertrophic zone contained mature and enlarged chondrocytes of spherical shape and, the zone of calcified cartilage passed into the subchondral cancellous bone. The trabeculae of the cancellous bone were of normal thickness, radiating from the center of the condyle reaching the surface at right angles, and they were surrounded by red bone marrow spaces. The interface between the subchondral bone and the cartilaginous plate was assumed by a continuous and regular osteochondral junction (Fig.  1 A).

H&E stained representative decalcified TMJ sagittal sections showing ( A ) control group (I), ( B ) arthritic group (II), ( C ) group treated with HA hydrogel (III), ( D ) group treated with BMSCs-CM (IV) and ( E ) group treated with the combination therapy (V). DC; articular disc, LJC; lower joint cavity, FC; fibrocartilaginous layer, HC; hyaline cartilage, OCJ; osteochondral junction, SB; subchondral bone, BT; bone trabeculae, BM; bone marrow, scale bar = 100 μm.

By contrast, group II sections displayed diffuse narrowing of the upper temporodiscal and lower condylodiscal cavities, flattening and roughening of the articulating surfaces, erosions in the condylar fibrocartilaginous layer with the exposure of subchondral bone in certain areas and, a significant articular disc thickening with fibrosis. The articular cartilaginous plate was severely destructed and the clear boundaries between cartilage layers were lost. The cartilaginous layer showed cellular disarrangement with chondrocyte damage and loss. The subchondral bone was severely disrupted, and it had widened marrow spaces accompanied by the resorption of trabecular bone. No clear boundaries were observed between the cartilaginous plate and subchondral bone. There was a flattening of the articular eminence, hyperplastic synovial tissue, and inflammatory mononuclear cell infiltration. (Fig.  1 B).

Regarding TMJs of group III, the histological sections revealed a considerable improvement in the structural components of the joint. There was a little decrease in articular disc thickness with a slight increase in the thickness of the condylar fibrocartilaginous layer. The subchondral bone showed more or less normal arrangement and thickness of bone trabeculae ( Fig.  1 C). However, the histological sections of group IV showed marked improvements in the structural components of the joint. The articulating surfaces of both the mandibular condyle and glenoid fossa were regular and smooth. The articular disc was decreased in thickness and became less fibrous, whereas the fibrocartilaginous layer of the condylar head was increased in thickness. The cartilaginous plate showed less damage as the erosion and degeneration were markedly reduced and there was a rise in condylar cartilage thickness with the arrangement of the chondrocytes in a more regular form. By reducing bone resorption, the articular eminence and subchondral bone displayed enhancements in the trabecular connectivity and bone quality. The synovial membrane exhibited a more or less normal histological manner with reduced inflammatory cells (Fig.  1 D).

Moreover, TMJs of group V showed significant improvements in TMJ structural components when compared to arthritic, HA hydrogel-treated, and BMSCs-CM-treated groups. TMJs revealed a regain and clearly defined structural elements of the articular disc, condyle, articular eminence, and synovial membrane. Histologically, the subchondral bone and cartilage layer did not show any signs of degenerative alterations. The typical radiating trabeculae that give the subchondral bone its fan-like appearance were present. (Fig.  1 E).

Histomorphometric results

Regarding disc thickness, the mean value was the highest in group II (606.35 ± 0.67) whereas the lowest was for group I (318.85 ± 0.78). The mean values for the disc thickness for groups III, IV, and V were 381.57 ± 0.76, 354.57 ± 0.53, and 331.79 ± 0.73, respectively. ANOVA test revealed a total significant difference between all groups ( P  = 0.001). Tukey’s post-hoc test revealed significant differences between group I and groups II, III and IV, between group II and groups III, IV and V, between group III and groups IV and V, and between group IV and group V ( P  = 0.001) and non-significant difference between group I and group V ( P  = 0.091). Among the treated groups, the effect size between groups V and I was very small (d = 17.12) and non-significant ( P  = 0.091), very high between groups V and II (d = 391.86) with a significance ( P  = 0.001), moderate between groups V and III (d = 66.80) with a significance and small between groups V and IV (d = 35.71) with a significance ( P  = 0.001) ( Table  1 ; Fig.  2 A ) .

Bar charts showing ( A ) the articular disc thickness, ( B ) fibrocartilaginous layer thickness and ( C ) amount of trabecular bone in the different groups. Data are presented as mean ± SD (one-way analysis of variance with Tukey’s post hoc test). The symbol (*) represents significant difference between different groups.

Meanwhile, the mean value of the fibrocartilaginous layer was the highest in group I (179.66 ± 0.15) whereas the lowest was for group II (36.32 ± 0.04). The mean values for the fibrocartilaginous layer thickness for groups III, IV, and V were 80.45 ± 0.04, 90.71 ± 0. 01, and 96.38 ± 0.04, respectively. ANOVA test revealed a total significant difference between all groups ( P  = 0.001). Tukey’s post-hoc test revealed significant differences between group I and groups II, III and IV, between group II and groups III, IV and V, between group III and groups IV and V, and between group IV and group V ( P  = 0.001) and significant difference between group I and group V ( P  = 0.008). Among the treated groups, the effect size between groups V and I was very small (d = 75.66) and significant ( P  = 0.008), very high between groups V and II (d = 1501.50) with a significance ( P  = 0.001), moderate between groups V and III (d = 398.25) with a significance and small between groups V and IV (d = 194.47) with a significance ( P  = 0.001) (Table  2 ; Fig.  2 B).

Moreover, the mean value of the trabecular bone was the highest in group I (23.56 ± 0.66) whereas the lowest one was for group II (5.68 ± 0.92). The mean values for groups III, IV, and V were 6.79 ± 0.80, 10.89 ± 1.24, and 19.35 ± 1.07, respectively. ANOVA test revealed a total significant difference between all groups ( P  = 0.001). Tukey’s post-hoc test revealed significant differences between group I and groups II, III, and IV, between group II and groups III, IV and V, between group III and groups IV and V, and between group IV and group V ( P  = 0.001) and non-significant difference between group I and group V ( P  = 0.066). Among the treated groups, the effect size between groups V and I was very small (d = 4.73) and non-significant ( P  = 0.066), very high between groups V and II (d = 13.69) with a significance ( P  = 0.001), moderate between groups V and III (d = 10.29) with a significance and small between groups V and IV (d = 7.30) with a significance ( P  = 0.001) (Table  3 ; Fig.  2 C).

Trichrome histochemical results

The collagen fibers in the fibrocartilage stained blue and the matrix in the hyaline cartilage appeared pink or light purple. Unmineralized new bone and mineralized old trabecular bone were stained blue and red, respectively. Group I sections showed that the collagen fibers were homogeneously distributed within the articular disc. The collagen fibers and chondrocytes of the condylar cartilage were regularly arranged with an ordered trabecular bone structure (Fig.  3 A). Meanwhile, in group II sections, the amount of collagen fibers was decreased and their distribution was organized in haphazardly. The diminishing and abnormalities in the trabecular bone structures were accompanied by the small and erratic amount of collagen fibers, indicating remodeling. (Fig.  3 B). Group III sections exhibited a relative increase for fibers accompanied with a moderately smooth trabecular bone morphology and a decline in bone marrow cavities (Fig.  3 C). Relative to group III, group IV sections showed more improvement in the quantity and arrangement of collagen fibers with a smooth surface of trabecular bone and more shrinkage of bone marrow cavities (Fig.  3 D). Whereas group V sections showed a normal pattern of collagen fibers and trabecular bone (Fig.  3 E).

Masson’s trichrome stained representative decalcified TMJ sagittal sections showing ( A ) control group (I), ( B ) arthritic group (II), ( C ) group treated with HA hydrogel (III), ( D ) group treated with BMSCs-CM (IV), ( E ) group treated with the combination therapy (V), scale bar = 100 μm. ( F ) bar chart histogram for the amount of collagen in the different groups, data are presented as mean ± SD (one-way analysis of variance with Tukey’s post hoc test). The symbols (*) represent significant difference between different groups.

The mean value of collagen amount (%) was the highest (18.83 ± 2.30) in group I whereas the lowest one was for group II (4.87 ± 0.25). The mean values for the collagen amount for groups III, IV, and V were 6.14 ± 1.05, 12.21 ± 1.32, and 12.29 ± 1.38, respectively. ANOVA test revealed a total significant difference between all groups ( P  = 0.001). Tukey’s post-hoc test revealed significant differences between group I and groups II, III and IV, between group II and groups IV and V, between group III and groups IV and V ( P  = 0.001), and non-significant difference between group I and group V ( P  = 0.091), between group II and group III ( P  = 0.053) and between group IV and V ( P  = 0.09) (Table  4 ; Fig.  3 F).

Toluidine blue histochemical results

Toluidine blue (TBO) assessed metachromatic staining of cartilage matrix for increased content of both proteoglycan and glycosaminoglycan production. The polysaccharides of the cartilage stained purple and nuclei were stained blue. Proteoglycan and glycosaminoglycan as part of the extracellular cartilage matrix were evenly distributed in group I (Fig.  4 A). Arthritic TMJs of group II showed a marked decrease in proteoglycan and glycosaminoglycan deposition with a pronounced decrease of TBO staining in the cartilaginous areas (Fig.  4 B). Meanwhile, arthritic TMJs of group III showed a moderate increase in the amount of proteoglycan and glycosaminoglycan in the disc and fibrous layer covering the articulating surfaces (Fig.  4 C) whereas those of group IV revealed a mild increase in the amount of glycosaminoglycan (Fig.  4 D). Group V showed an intense TBO staining in the cartilaginous matrix, indicating abundant proteoglycan and glycosaminoglycan deposition similar to group I (Fig.  4 E). Assessing 10 images for each group, the OARSI score was 100% grade 0, stage 0 for group I indicating the absence of any arthritic features. For group II, the arthritic score was 90% grade 6, stage 4 and 10% grade 5, stage 4, that is the highest among the studied groups. Meanwhile group III showed arthritic score of 80% grade 2, stage 4 and 20% grade 2, stage 3. Group IV showed arthritic score of 70% grade 3, stage 4 and 30% grade 2, stage 4. Group V recorded a lower level of arthritic score than that recorded in groups III and IV as 90% was grade 1, stage 2 and 10% grade 1, stage 1.

Toluidine blue stained representative decalcified TMJ sagittal sections showing ( A ) control group (I), ( B ) arthritic group (II), (C) group treated with HA hydrogel (III), ( D ) group treated with BMSCs-CM (IV), ( E ) group treated with the combination therapy (V), scale bar = 200 μm.

NF-κB immunohistochemical results

Brown deposits in the cytoplasm were an indication of an immunohistochemical positive reaction. An immune stain was applied to better examine the inflammatory impact of NF-κB. The immune reaction was very mild in group I (Fig.  5 A) and severe in group II (Fig.  5 B) and it was decreased to be moderate in group III (Fig.  5 C) and mild in groups IV (Fig.  5 D) and V (Fig.  5 E). The mean value of NF-κB expression was the highest (5.29 ± 0.35) in group II whereas the lowest one was for group I (0.36 ± 0.18). The mean values for the NF-κB expression for groups III, IV, and V were 3.68 ± 0.24, 2.11 ± 0.19, and 0.62 ± 0.15, respectively. ANOVA test revealed a total significant difference between all groups ( P  = 0.001). Tukey’s post-hoc test revealed significant differences between group I and groups II, III and IV, between group II and groups III, IV, and V, between group III and groups IV and V ( P  = 0.001) and non-significant difference between group I and group V ( P  = 0.648) (Table  4 ; Fig.  5 F).

Representative decalcified TMJ sagittal sections showing ( A ) control group (I), ( B ) arthritic group (II), ( C ) group treated with HA hydrogel (III), ( D ) group treated with BMSCs-CM (IV), ( E ) group treated with the combination therapy (V), DAB, scale bar = 400 μm. ( F ) bar chart histogram for the amount of NF-κB expression in the different groups, data are presented as mean ± SD (one-way analysis of variance with Tukey’s post hoc test). The symbol (*) represents significant difference between different groups.

Adjuvant-induced arthritis of animal models has been established for decades to study the pathogenesis of arthritis, including RA, gout, and OA, and to evaluate the effectiveness of certain anti-arthritic drugs 37 . Various CFA doses were tested to establish the arthritic models in earlier literature via IA, intradermal, or subcutaneous routes. In the present study, IA route was used as a reliable model for inducing arthritis and in line with ethical recommendations on reducing the severity of animal models of human disease. 50 µg CFA in 50 µl; on day 1 of the experiment was used to induce persistent inflammation and at day 14 to induce arthritis. This comes with Wang et al. 27 who reported a novel model with double CFA injections into the upper compartment of the TMJ to induce structural abnormalities and degenerative changes in the disc and synovium. This contradicts other previous works that utilized only single CFA injections 38 , 39 In addition, IA injection was the method used to deliver the therapeutic agents as it offers a localized and direct access to the joint space, thus facilitating the bioavailability of therapeutic agents at the affected site while reducing potential side effects, systemic exposure, and overall cost 40 . The authors speculated that local injection of the BMSCs-CM or its combination with HA will activate the endogenous repair mechanism by targeting the resident stem cells in the joint 41 . Therefore a single IA injection of BMSCs-CM or the combination could be considered as innovative formulation, well tolerated, safe, and effective in the treatment of TMJ OA.

The combination therapy in the present study had a better regenerative ability compared to HA hydrogel- or BMSCs-CM treated groups and we could attribute our results to the sustained release of the growth factors and cytokines found in the BMSCs-CM from the cross-linked HA hydrogel. Yang et al. 42 achieved long-term retention of MSC-derived small extracellular vesicles in the joint area and controlled release by an injectable Diels-Alder cross-linked HA/PEG hydrogel for OA improvement. Similar to the findings of this study, Arifka et al. 43 found that the embedding of MSC-CM in the HA hydrogel causes the discharge of cytokines/chemokines as well as anti- and/or pro-inflammatory molecules, and also overcomes the rapid removal of MSC-CM from the target tissue. The bioactivity of the CM contained in the gel is supported by in-vitro research allowing for a controlled and extended-release mechanism. In addition, Köhnke et al. 44 reported that the addition of HA to mesenchymal stromal cells improve cartilage repair in a rabbit model of TMJ OA. In a beagle-dog model, Li et al. 45 showed that BMSCs-CM in conjunction with HA had a more effective therapeutic impact than HA alone. Chiang et al. 46 found that IA injections of allogeneic MSCs suspended in HA significantly slowed the course of osteoarthritis in rabbits compared to HA injections given singly. Huang et al. 47 stated that MSCs-CM possess the capacity for both immune control and tissue healing. The therapeutic potential of MSCs-CM is improved by hydrogel encapsulation by boosting survival, retention, and targeting. The therapeutic effectiveness of hydrogel-loaded MSCs-CM has been established in a variety of illnesses, primarily in the regeneration of bone and cartilage. In addition, Shoma Suresh et al. 48 reported that MSC- CM’s characteristics can be retained even after being encapsulated into nanoparticles (NP) and merged into a hydrogel to create a composite. Biocompatibility was shown by the MSC-CM hydrogel composite. Moreover, the NP-hydrogel composite’s regulated release of MSC-CM increased metabolic activity, highlighting the potential for regenerative medicine. Fabricating hydrogel encapsulation of MSCs-CM will have several advantages likely as sustained drug release effect, higher biocompatibility, ability to simulate extracellular-like conditions and can be scaled up in bulk not needing MSC expansion each time 49 .

HA hydrogel and BMSCs-CM have been well-identified as promising therapeutic agents to treat symptoms of arthritis. Local delivery of cross-linked HA hydrogel incorporated with BMSCs-CM was the method of administration in the present study. Localized route is considered more clinically relevant as it has several advantages over the systemic one. Local delivery increases bioavailability, reduce toxicity and side effects, and minimize off-target activity. As repetitive injections of arthritic TMJ with the medications could induce damage to the joint, cross-linked HA hydrogel was used to achieve a more sustained release of BMSCs-CM over a longer period of time. However developing a new medication in the form of HA hydrogel combined with BMSCs-CM still faces several challenges. On the one hand, is to determine the pharmacokinetic, pharmacodynamic properties and target identification of HA hydrogel when combined with BMSCs-CM. On the other hand, validation and optimization of this new medication requires more researches in the form of preclinical studies before human testing. Preclinical validation in the form of in-vitro and in-vivo trials are required to determine a starting, safe dose for first-in-human study and assess potential toxicity of the medication. After that randomized, placebo-controlled clinical trials should be designed to learn if this new medication is more effective or has less harmful side effects than existing treatments.

It could be concluded that to repair degenerative changes in rats’ TMJs associated with CFA-induced arthritis, combination therapy of HA hydrogel and BMSCs-CM is better than using HA hydrogel or BMSCs-CM, separately.

Data availability

The data produced and analyzed for this study are available from the corresponding author upon reasonable request.

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Mai M. Assi, Mohammed E. Grawish, Heba Mahmoud Elsabaa, Mohamad E. Helal & Samah K. Ezzat

Department of Oral Biology, Faculty of Oral and Dental Medicine, Delta University for Science and Technology, Dakahlia, Egypt

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M. A. and S. E. contributed to the conception, design, data acquisition, and manuscript drafting and revision. H. E. and M. H. contributed to the design. , M. G. contributed to data acquisition and analysis. All authors gave final approval of the manuscript.

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Assi, M.M., Grawish, M.E., Elsabaa, H.M. et al. Therapeutic potential of hyaluronic acid hydrogel combined with bone marrow stem cells-conditioned medium on arthritic rats’ TMJs. Sci Rep 14 , 26828 (2024). https://doi.org/10.1038/s41598-024-77325-6

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DOI : https://doi.org/10.1038/s41598-024-77325-6

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