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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

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• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 21 August 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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Drug Absorption

• Oral Administration |
• Parenteral Administration |
• Controlled-Release Forms |

Drug absorption is determined by the drug’s physicochemical properties, formulation, and route of administration. Dosage forms (eg, tablets, capsules, solutions), consisting of the drug plus other ingredients, are formulated to be given by various routes (eg, oral, buccal, sublingual, rectal, parenteral, topical, inhalational). Regardless of the route of administration, drugs must be in solution to be absorbed. Thus, solid forms (eg, tablets) must be able to disintegrate and deaggregate.

Unless given IV, a drug must cross several semipermeable cell membranes before it reaches the systemic circulation. Cell membranes are biologic barriers that selectively inhibit passage of drug molecules. The membranes are composed primarily of a bimolecular lipid matrix, which determines membrane permeability characteristics. Drugs may cross cell membranes by

Passive diffusion

Facilitated passive diffusion, active transport, pinocytosis.

Sometimes various globular proteins embedded in the matrix function as receptors and help transport molecules across the membrane.

(See also Overview of Pharmacokinetics .)

Drugs diffuse across a cell membrane from a region of high concentration (eg, gastrointestinal fluids) to one of low concentration (eg, blood). Diffusion rate is directly proportional to the gradient but also depends on the molecule’s lipid solubility, size, degree of ionization, and the area of absorptive surface. Because the cell membrane is lipoid, lipid-soluble drugs diffuse most rapidly. Small molecules tend to penetrate membranes more rapidly than larger ones.

Most drugs are weak organic acids or bases, existing in un-ionized and ionized forms in an aqueous environment. The un-ionized form is usually lipid soluble (lipophilic) and diffuses readily across cell membranes. The ionized form has low lipid solubility (but high water solubility—ie, hydrophilic) and high electrical resistance and thus cannot penetrate cell membranes easily.

The proportion of the un-ionized form present (and thus the drug’s ability to cross a membrane) is determined by the environmental pH and the drug’s p K a (acid dissociation constant). The p K a is the pH at which concentrations of ionized and un-ionized forms are equal. When the pH is lower than the p K a, the un-ionized form of a weak acid predominates, but the ionized form of a weak base predominates. Thus, in plasma (pH 7.4), the ratio of un-ionized to ionized forms for a weak acid (eg, with a p K a of 4.4) is 1:1000; in gastric fluid (pH 1.4), the ratio is reversed (1000:1). Therefore, when a weak acid is given orally, most of the drug in the stomach is un-ionized, favoring diffusion through the gastric mucosa. For a weak base with a p K a of 4.4, the outcome is reversed; most of the drug in the stomach is ionized.

Oral Administration ).

Certain molecules with low lipid solubility (eg, glucose) penetrate membranes more rapidly than expected. One theory is facilitated passive diffusion: A carrier molecule in the membrane combines reversibly with the substrate molecule outside the cell membrane, and the carrier-substrate complex diffuses rapidly across the membrane, releasing the substrate at the interior surface. In such cases, the membrane transports only substrates with a relatively specific molecular configuration, and the availability of carriers limits the process. The process does not require energy expenditure, and transport against a concentration gradient cannot occur.

Active transport is selective, requires energy expenditure, and may involve transport against a concentration gradient. Active transport seems to be limited to drugs structurally similar to endogenous substances (eg, ions, vitamins, sugars, amino acids). These drugs are usually absorbed from specific sites in the small intestine.

In pinocytosis, fluid or particles are engulfed by a cell. The cell membrane invaginates, encloses the fluid or particles, then fuses again, forming a vesicle that later detaches and moves to the cell interior. Energy expenditure is required. Pinocytosis probably plays a small role in drug transport, except for protein drugs.

Oral Administration

Differences in luminal pH along the GI tract

Surface area per luminal volume

Blood perfusion

Presence of bile and mucus

The nature of epithelial membranes

The oral mucosa has a thin epithelium and rich vascularity, which favor absorption; however, contact is usually too brief for substantial absorption. A drug placed between the gums and cheek (buccal administration) or under the tongue (sublingual administration) is retained longer, enhancing absorption.

The stomach is normally the first organ in which intense contact between a drug given orally and GI fluids occurs (for review, see [ 1

The small intestine has the largest surface area for drug absorption in the GI tract, and its membranes are more permeable than those in the stomach. For these reasons, most drugs are absorbed primarily in the small intestine, and acids, despite their ability as un-ionized drugs to readily cross membranes, are absorbed faster in the intestine than in the stomach (for review, see [ 1 ]). The intraluminal pH is 4 to 5 in the duodenum but becomes progressively more alkaline, approaching 8 in the lower ileum. GI microflora may reduce absorption. Decreased blood flow (eg, in shock) may lower the concentration gradient across the intestinal mucosa and reduce absorption by passive diffusion.

To maximize adherence, clinicians should prescribe oral suspensions and chewable tablets for children < 8 years of age. In adolescents and adults, most drugs are given orally as tablets or capsules primarily for convenience, economy, stability, and patient acceptance. Because solid drug forms must dissolve before absorption can occur, dissolution rate determines availability of the drug for absorption. Dissolution, if slower than absorption, becomes the rate-limiting step. Manipulating the formulation (ie, the drug’s form as salt, crystal, or hydrate) can change the dissolution rate and thus control overall absorption.

General reference

1. Vertzoni M, Augustijns P, Grimm M, et al : Impact of regional differences along the gastrointestinal tract of healthy adults on oral drug absorption: An UNGAP review. Eur J Pharm Sci 134:153-175, 2019. doi:10.1016/j.ejps.2019.04.013

Parenteral Administration

Drugs given IV enter the systemic circulation directly. However, drugs injected IM or subcutaneously must cross one or more biologic membranes to reach the systemic circulation. If protein drugs with a molecular mass > 20,000 g/mol are injected IM or sc, movement across capillary membranes is so slow that most absorption occurs via the lymphatic system. In such cases, drug delivery to systemic circulation is slow and often incomplete because of first-pass metabolism (metabolism of a drug before it reaches systemic circulation) by proteolytic enzymes in the lymphatics.

Controlled-Release Forms

Controlled-release forms are designed to reduce dosing frequency for drugs with a short elimination half-life and duration of effect. These forms also limit fluctuation in plasma drug concentration, providing a more uniform therapeutic effect while minimizing adverse effects. Absorption rate is slowed by coating drug particles with wax or other water-insoluble material, by embedding the drug in a matrix that releases it slowly during transit through the gastrointestinal tract, or by complexing the drug with ion-exchange resins. Most absorption of these forms occurs in the large intestine. Crushing or otherwise disturbing a controlled-release tablet or capsule can often be dangerous.

Transdermal controlled-release forms are designed to release the drug for extended periods, sometimes for several days. Drugs for transdermal delivery must have suitable skin-penetration characteristics and high potency because the penetration rate and area of application are limited.

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Formulating a hypothesis.

1. Variables

Once you've decided on your research questions and completed your background reading, you will select variables to study and a hypothesis to test. This is where you begin to put your problem solving skills into action.

A variable is a characteristic that varies throughout the population as a whole and which can be used to study differences between people and groups. Population variables can include age, class, income level, level of education, race, veteran status, gender, employment status, whether one drives, whether one smokes, country of origin, language, citizen status, region of the country, city or country dweller, or marital status. These variables are different for each individual, but you can batch together large groups of people who all share a certain variable or set of variables. You can also see how variables impact each other by identifying them and sorting the data.

Focusing on particular variables allows you to isolate those characteristics in order to analyze the influence of these characteristics on the population's experience.

IN CONTEXT If you are studying the effects of wealth transfer through generations, you might look at the relationship between your subjects' income and education levels and their parents' education levels. You might also want to know if all levels of parental education and income have the same effect. What might you hypothesize about the relationship between the two? You might hypothesize that if a subject's father was educated, the subject will be as educated or higher. You might also hypothesize that if a subject's father was educated, the subject will be likelier to earn a higher income. But there are other variables at play here too: age, gender, race, location, the presence of other similarly educated family members, and many others. In formulating your hypothesis, you make a statement about how the variable “father's education” is related to the variable “subject’s education level.” Keep in mind that not all variables are created equal. Some are very critical in explaining a subject's education level, and some aren’t, meaning that they don't strongly relate to the outcome that you’re trying to explain.

There are two different kinds of variables:

An independent variable is the factor that causes the change, or the outcome. You can think of it as the cause. In the example above, the independent variable is the subject's father's education level. It is what drives the change. The dependent variable is the effect or the variable that is influenced by the other. In the example, the dependent variable is the subject's education and income level. You are hypothesizing that the father's education level affects their child's education and income level.

terms to know Variable A characteristic such as age, education, income, or marriage status that can vary throughout the population. Independent Variable The cause of the change, or what drives the change in the dependent variable. Dependent Variable The effect of the change; a variable changed by other variables.

2. Formulating a Hypothesis

People commonly try to understand the happenings in their world by finding or creating an explanation for an occurrence, which is what we referred to earlier as common sense. Social scientists may develop a hypothesis for the same reason.

A hypothesis is a testable, informed guess about predicted outcomes between two or more variables; it’s a possible explanation for specific happenings in the social world and allows for testing to determine whether the explanation holds true in many instances, as well as among various groups or in different places. The hypothesis will often predict how one form of human behavior influences another. The independent variable is the cause of the change, or the variable that influences the other variable. The dependent variable is the effect, or variable that is changed. It depends on the independent variable.

big idea The hypothesis is the researcher's guess—based on background research—about the answer to the research question. A hypothesis often concerns how one thing affects another, which is another way of saying how the independent variable impacts the dependent variable.

In putting together their hypotheses, researchers establish one form of human behavior as the independent variable and observe the influence it has on a dependent variable.

hint It is important to note that at this stage we are suggesting relationships between variables, or correlation. We are not yet suggesting that one variable is the cause of another, just that one variable changes when another variable changes in a predictable way.

As the table shows, an independent variable is the one that influences the other variable. Rather than being “right,” sociologists are interested in the relationships between variables. If we were to examine the last example, what other variables might come into play? Would we see similar patterns of income for all college-educated people or are there disparities for racial and ethnic minorities? Gender minorities? First, we must move into the next research steps: designing and conducting a study and drawing conclusions. You’ll learn more about these types of research methods in the next section of the course.

term to know Hypothesis A testable, informed guess about predicted outcomes between two or more variables.

3. Sampling

What happens after you gravitate towards a topic, come up with a hypothesis, and hypothesize a relationship between an independent variable and a dependent variable? Most likely it won't be practical to plan on studying an entire population of a city or country. You need to use a sample of the population as a whole.

A sample is a smaller group of subjects that ideally represents the population as a whole. You use a sample because it is impossible to go and ask everyone in the whole population, so you have to take a slice of the whole population. The goal, then, is to have a representative sample where all facets of interest of the study are included. The only requirement is that the sample be random.

hint When selecting a sample of a population for a study, the goal is to select a sample that is representative of the entire population.

One effective way to get a sample is through a technique called snowball sampling . In snowball sampling, you find your initial respondents or subjects through acquaintances that you already have in your network. You then use those acquaintances to find their acquaintances, and so on, and the process snowballs.

terms to know Sample A smaller group of subjects that ideally represents the larger population as a whole. Snowball Sampling A sampling technique where initial subjects are found through acquaintances, and later subjects are found through acquaintances of acquaintances.

summary In this lesson, you learned about how sociologists go about formulating a hypothesis , including establishing independent and dependent variables . You saw why sampling is a useful approach to making a huge population something small enough to work with but still representative. You also strengthened your problem solving skill by beginning to consider educated solutions to problems in society. Best of luck in your learning!

Source: THIS TUTORIAL HAS BEEN ADAPTED FROM "INTRODUCTION TO SOCIOLOGY" BY LUMEN LEARNING. ACCESS FOR FREE AT LUMEN LEARNING . LICENSE: CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL.

The effect of the change. a variable changed by other variables.

A testable educated guess about predicted outcomes between two or more variables.

The cause of the change, or what drives the change in the dependent variable.

A smaller group of subjects that ideally represents the larger population as a whole.

A sampling technique where initial subjects are found through acquaintances, and later subjects are found through acquaintances of acquaintances.

A characteristic such as age, education, income, or marriage status that can vary throughout the population.

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Dissolution rates of over-the-counter painkillers: a comparison among formulations

Affiliation.

• 1 Bayer S.p.A., Milan, Italy - [email protected].
• PMID: 27035158

Background: We wanted to compare the dissolution profile of several over-the-counter analgesics to understand whether the different formulation techniques employed to enhance absorption were associated with variations in the dissolution rate, a parameter known to affect drug absorption.

Methods: We considered 5 formulations currently marketed in Italy: aspirin tablets (Aspirina Dolore e Infiammazione®), ibuprofen tablets and liquid capsules (Moment®), ibuprofen lysine tablets (Nurofenimmedia®) and dexketoprofen trometamol tablets (Enantyum®). Dissolution tests were performed according to the current USP/NF monograph dissolution procedure. Drug dissolution was evaluated at 1, 3, 6, 15, and 30 minutes since the start of the test. Dissolution was evaluated at three different pH: 1.2, 4.5 and 6.8. Every test was repeated 12 times.

Results: The aspirin formulation was by far the most rapid dissolving formulation, among those tested, with more than 80% of the tablet dissolved at 6 minutes for every pH considered. At pH 1.2 and 4.5, only the dexketoprofen formulation was able to reach the dissolution level of aspirin at 30 minutes, but had lower levels of dissolution at the previous time points. Instead, at pH 6.8, most of the formulations approached aspirin dissolution level, but only after 15 minutes. Ibuprofen capsules had the slowest kinetics, with a lag phase the first 6 minutes.

Conclusions: Different formulation strategies can lead to great differences in the dissolution rates even among drugs of the same class, suggesting that enhancements in the formulation of painkillers can lead to improvements in drug absorption, and thus in the onset of analgesia.

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Evaluation of Topical Anti-Inflammatory Potential of Mentha piperita L. Extract by Formulation of Microemulgel

Corresponding author.

The present study is designed to develop a novel dosage form i.e. microemulgel which will enhance the rate of absorption in the systemic circulation and ultimately enhance the pharmacological effect of the Mentha piperita L. extract as anti-inflammatory agent. Its primary components include with constituents including menthol (46.32%), menthofuran (13.18%), menthyl acetate (12.10%), menthone (7.42%) and 1,8-cineole (6.06%).

Materials and Methods

The research aimed to formulate and assess herbal microemulgel containing M. piperita extract, focusing on its in vitro anti-inflammatory properties. M. piperita herb extraction was carried out using a hydro-alcoholic solvent, followed by phytochemical analysis. Four separate sets of herbal microemulgel were crafted and underwent a series of assessments, encompassing pH levels, spreadability, viscosity, consistency, appearance, color and ease of washing. Additionally, the in vitro anti-inflammatory potential of both the extract and the microemulgel formulation was assessed using the HRBC membrane stabilization assay and the protein denaturation assay.

The findings of this study suggest that the newly developed herbal microemulgel, enriched with M. Piperita extract, exhibits promising anti-inflammatory effects.

The M. piperita microemulgel exhibited a remarkable 94.35% drug content with high solubility and compatibility of the drug with the excipients. Permeability studies revealed that the M. piperita microemulgel achieved 94% permeability within 48 hr, showcasing enhanced drug permeability facilitated by the microemulsion-based gel system. Moreover, the formulated microemulgel demonstrated significant anti-inflammatory activity. It can be concluded that topical herbal M. piperita microemulgel has potential for future applications in this regard.

INTRODUCTION

A topical drug delivery system circumvents first-pass metabolism, gastrointestinal degradation and potential irritation linked with oral intake. This method of drug delivery is optimal for targeting skin-related ailments with localized action. The skin serves as a vital barrier for the human body, distinguishing internal biology from the external environment. Nonetheless, it is susceptible to inflammation. Microemulgel represents a dual drug delivery approach achieved by transforming liquid microemulsion into a semi-solid gel. 1 It’s regarded as a highly promising novel drug delivery system owing to its dual functionality through both emulsion and gel phases. Furthermore, research has demonstrated that combining emulsion with gel enhances its stability. The selection of the microemulsion system was based on its exceptional ability to solubilize and penetrate the skin effectively, while the gel component ensures sustained drug release, leading to prolonged drug residence time. The microemulgel drug delivery system emerges as the optimal choice for treating skin-related ailments, offering enhanced efficacy with reduced drug dosage. 2

Treating inflammation often involves the use of plant-based remediesandextracts.Traditionallyrecognizedanti-inflammatory plants include Curcuma longa Linn., Zingiber officinalis Roscoe, Borago officinalis Linn., Oenothera biennis Linn. (Evening primrose), Harpagophytum procumbens (Devil’s claw) and Mentha piperita L. for instance, is indigenous to North India, Pakistan, Afghanistan, Tibet and Nepal. Its primary constituents include menthol (46.32%), menthofuran (13.18%), menthyl acetate (12.10%), menthone (7.42%) and 1,8-cineole (6.06%). It is used in the treatment of inflammation, it is used in the treatment of inflammation, M. piperita is utilized for a multitude of purposes such as addressing Irritable Bowel Syndrome (IBS), indigestion, bed sores, tension headaches, anxiety, insomnia, memory enhancement and various other applications, the research aims to formulate and evaluate a herbal microemulgel infused with M. piperita extract, aiming to scrutinize its in vitro anti-inflammatory attributes. 3

While the oral route is commonly preferred for drug administration, both oral and intramuscular injections can lead to severe toxicity and adverse effects in patients. These effects may include gastrointestinal toxicity, renal failure and elevated liver enzymes at high doses. To address the challenges associated with these invasive delivery methods, a novel transdermal drug delivery system has been introduced. This system offers several advantages over oral and intramuscular injections, such as high absorption rates, ease of preparation, thermodynamic stability, avoidance of first-pass metabolism and ease of application. Additionally, its non-invasive nature and local effectiveness make it more patient-compliant compared to conventional delivery systems. The transdermal microemulgel, an enhanced form of emulsion, improves the solubility of poorly water-soluble drugs. 4

Numerous research endeavors have shed light on the multifaceted biochemical impacts of M. piperita through both in vivo and in vitro investigations. Studies have showcased that various constituents of M. piperita exhibit a spectrum of beneficial effects, including anti-inflammatory, analgesic, immunomodulatory, antispasmodic, antihyperglycemic, anticancer, molluscicide, insecticidal, antiapoptotic, antibacterial, anti-sarcoptic, anxiolytic, anticonvulsant, antiulcer and antigastric properties. 5 , 6

We can improve drug solubility and skin penetration by creating a microemulgel containing M. piperita . This approach facilitates the development of an optimized formulation. Transforming a basic M. piperita microemulsion into a microemulgel can address challenges encountered with conventional topical drug delivery systems, offering a larger interfacial area for efficient drug absorption.

MATERIALS AND METHODS

Collection and authentication of plant materials.

The M. piperita herb’s raw material was sourced from a nearby local nursery in Nagpur. Authentication of the plant material was carried out with the guidance of Dr. N. M. Dongarwar, associated with the Department of Botany at Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, utilizing a botanical specimen sheet. The authentication number for the M. piperita specimen sheet is 10429.

Processing of the plant material

Following authentication, the M. piperita was dried, powdered and subsequently utilized to extract its essence.

Extraction of plant material

The M. piperita powder underwent maceration with 70% alcohol for 48 hr. Afterward, the residue was subjected to extraction with 70% ethanol using a Soxhlet apparatus. The extracts obtained from both processes were then combined, followed by further concentration of the combined extract.

Formulation of microemulgel

Different batches of microemulgel were formulated by utilizing diverse gelling agents and adjusting their concentrations. 2% M. piperita extract was incorporated into the emulsion. 7 The gelling agents were hydrated in water and pH adjustment was carried out by adding triethanolamine. Afterward, the emulsion was incorporated into the gel and thoroughly stirred to form the microemulgel. 8 Isopropyl myristate, Kolliphor PS 80: Transcutol P was gifted by Gattefosse India Pvt. Ltd., Mumbai Tween 80, Isopropyl Myristate (IPM), n-butanol, Carbopol 940 and triethanolamine were also purchased from Sigma-chemicals Nagpur. All solvents and materials employed were of analytical grade. Throughout the formulation processes, distilled water was consistently utilized. 9

Construction of microemulsion phase diagrams

Pseudo-ternary phase diagrams were generated employing the aqueous titration method for four weight ratios (1:0, 1:1, 1:2 and 2:1) of Labrafac CC Myristate (S mix ). Within each phase diagram, M. piperita oil and the designated S mix ratio were meticulously combined in varying weight ratios spanning from 1:9 to 9:1, each within separate glass vials. A total of 9 combinations of oil and S mix (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1) were prepared to accurately delineate the boundaries of phases formed in the phase diagrams. A gradual titration with the aqueous phase was conducted for every combination of oil and S mix , succeeded by visual scrutiny to distinguish transparent and effortlessly flowable Oil-in-Water (O/W) microemulsions. 10

Preparation of microemulsion

Phase diagram with S mix 1:1 showed maximum microemulgel region, so from that, concentrations of oil (3-5%w/w) and S mix (85-87%w/w) were pooled and several combinations of ME were formed using Box Behnken Design. To each combination of oil and S mix (1:1), water was added dropwise with stirring at ambient temperature. 11

Thermodynamic stability study

Heating-cooling cycles: The formulations underwent a series of six heating-cooling cycles, alternating between 4°C and 45°C. Each temperature was maintained for a minimum of 48 hr during storage. The formulations were thoroughly examined at both temperatures for signs of significant instability, such as phase separation, precipitation, cracking and similar issues. 12

Centrifugation test: A centrifugation test was conducted on the formulations at 3,500 rpm for 30 min, with careful observation for any indications of phase separation.

Freeze-thaw cycle: The formulations were subjected to three freeze-thaw cycles, oscillating between temperatures of -21°C and +25°C, with each temperature held constant for at least 48 hr during storage. 13

Globule size and zeta potential

MEs were characterized for globule size and zeta potential using Zeta Sizer Litesizer DLS 500 Dadasaheb Balpande College of Pharmacy, Besa Nagpur.

Gravimetric method

0.17 g of monobasic potassium phosphate were accurately weighed and dissolved in 625 mL of distilled water. Similarly, 0.22 g of sodium hydroxide were weighed and dissolved in 2.8 mL of distilled water. The solutions were combined and diluted to a final volume of 25 mL to prepare a buffer solution with a pH of 6.8. An empty test tube was weighed before the experiment. The drug M. piperita microemulgel was dissolved in 10 mL of pH 6.8 phosphate buffer until precipitation occurred. The solution was then filtered and the filtrate was subjected to evaporation. After heating, the test tube was weighed again. The difference in weights before and after heating was calculated to determine the drug’s solubility in this buffer. 14 , 15

Partition coefficient

The pH 6.8 buffer solution was prepared and 10 mL of it were transferred to a separating funnel. To this, 10 mL of methanol were added. Then, 100 mg of M. piperita were accurately weighed and added to the separating funnel. The mixture was vigorously shaken for 30 min and then allowed to stand for 15 min to allow the layers to separate. Both layers were carefully extracted and filtered into separate beakers. The buffer extract was diluted with distilled water, while the methanol extract was diluted with methanol. Subsequently, both extracts were analyzed using a spectrophotometer. 16

Organoleptic evaluation

The physical appearance evaluation included analyzing color, homogeneity, consistency and overall appearance. Color assessment was conducted visually, while homogeneity was determined by rubbing the microemulgel between fingers. The appearance of the microemulgel was evaluated visually. Furthermore, the consistency of the microemulgel was tested by applying it to the skin. 17

Spreadability

To assess spreadability, the microemulgel was placed between two petri plates and the diameter of the spread microemulgel ring was measured. A gram of microemulgel was weighed and deposited onto a petri plate, with another petri plate positioned on top. A weight of 50 g was applied to the upper petri plate for 60 sec. After this duration, the diameters of the circles formed by the spread microemulgel were measured three times and the average reading was calculated. This average was then utilized in the following formula. 18

Where, S=Spreadability, M=Mass, L=Diameter and T=Time.

The pH of the formulated microemulgel batches was assessed using a digital pH meter. A quantity of 0.5 g from each batch was dissolved in 10 mL of distilled water. The electrode was then immersed in the solution to measure the pH. 19

Stability studies

The stability studies of the microemulgel included storing samples under different conditions: 5°C, 25°C/60% RH, 30°C/65% RH and 40°C/75% RH, for 3 months. Assessments were carried out at 15-day intervals, examining the appearance, pH, viscosity and spreadability of the samples. 20

Particle size analysis

The droplet size of the microemulgel was examined using a scanning electron microscope. A calibrated micrometer slide with a droplet of microemulsion was observed under the eyepiece and the droplet size was measured and determined.

Rheological studies

The viscosity of the microemulgel preparation was measured using the Brookfield Viscometer (DV-E Brookfield Viscometer Model-LVDVE). This viscometer consists of a stationary cup and a rotating spindle. The rotating spindle was immersed in the sample of microemulsion gel. Due to the higher viscosity of the gel preparation, a smaller spindle with a reduced diameter and surface area was employed. The spindle was rotated within the gel preparation until a consistent reading was achieved on the viscometer dial. This procedure was repeated three times to ensure the reproducibility of the results. 21

Electrical conductivity

The electrical conductivity test was conducted to ascertain whether the microemulgel was an oil-in-water or water-in-oil microemulsion. The conductivity (σ) of the formulated sample was determined using a conductivity meter, which involved using a probe and meter. Voltage was applied across two electrodes within a probe immersed in the microemulsion. The decrease in voltage, attributed to the microemulsion’s resistance, was utilized to calculate the conductivity.

Differential Scanning Calorimeter (DSC)

Utilizing a DSC-60 calorimeter by SHIMADZU, samples underwent heating under liquid nitrogen from room temperature to 400°C at a rate of 10°C/min. Approximately 5 mg of the sample in a sealed aluminium pan was subjected to a DSC scan. Pure M. piperita , the vehicle and the microemulgel were individually tested and the resulting thermograms were compared.

Dye solubility test

To ascertain the type of emulsion, whether it was Oil-in-Water (O/W) or Water-in-Oil (W/O), 10 μL of methylene blue, a water-soluble dye, was introduced into the emulsion. This addition aimed to observe whether the dye dispersed uniformly throughout the emulsion or formed clusters.

Fourier Transform Infrared Spectra analysis (FT-IR)

The functional groups present in the preparation were identified through FT-IR scanning microscopy. As IR rays passed through the sample, the absorbed radiations were transformed into vibrations or stretching, revealing spectrum signals ranging from 4000 cm -1 to 400 cm -1 . These signals provided insights into the functional groups and the types of bonds present, forming a molecular fingerprint of the sample

Release study

In testing the release rate of M. piperita from various microemulgel formulations, a Franz diffusion cell with a rabbit membrane was employed. The abdominal skin of the rabbit was shaved and excised and the integrity of the subcutaneous fat was assessed. The membrane was positioned with the mucous membrane facing upward and the epidermis downward, then secured the Franz diffusion cell facilitated the transfer of substances The transfer took place between the donor and receiver compartments of the cell. The cell was loaded with 9 mL of phosphate buffer at a pH of 6.8 and throughout the experiment, the receptor fluid was continuously stirred using externally driven magnetic bars at a speed of 300 rpm. M. piperita microemulgel was introduced into the donor chamber at intervals ranging from 0.5 to 48 hr, 0.5 mL samples were extracted from the receiver chamber for spectrophotometric analysis. These samples were promptly replaced with an equivalent volume of buffer solution. UV-visible spectrophotometry at 354 nm was employed for the analysis of the samples. 22

Drug content

A 1 mL aliquot of the emulsion was mixed with 9 mL of buffer solution at pH 6.8 and stirred for 30 min. The mixture was left at room temperature for 24 hr before undergoing an additional 30 min of stirring. Subsequently, the solution underwent centrifugation at 4000 rpm for 30 min to yield a clear supernatant. From this supernatant, 1 mL was extracted and combined with 9 mL of buffer solution. This resulting solution was then analyzed using a UV spectrophotometer at 354 nm and the absorbance was recorded. The concentration of M. piperita was determined by referencing a standard calibrated curve of the drug.

In vitro anti-inflammatory Human Red Blood Cell (HRBC) membrane stabilization method

To evaluate the anti-inflammatory potential of the M. piperita extract microemulgel, the HRBC membrane stabilization method was utilized. Around 2-3 mL of blood was obtained from a healthy donor and combined in equal proportions with Alsever’s solution. Iso-saline was then added, followed by centrifugation for 5 min to isolate the HRBC suspension. Equal amounts of the sample were added to the HRBC suspension, with concentrations of 100, 200 and 300 μg/mL prepared. The mixtures were then incubated at 37°C for 30 min and subsequently centrifuged for 5 min. Negative controls included Alsever’s solution and blood, while aspirin served as the standard. UV spectroscopy at a wavelength of 560 nm was utilized to analyze the supernatant for estimation. 23

Inhibition of protein denaturation

A volume of approximately 0.02 mL of the sample was measured and combined with 0.2 mL of 1% Bovine Albumin. To this mixture, 4.78 mL of PBS (Phosphate Buffer Saline) was added and thoroughly mixed, followed by an incubation period of 15 min. Afterward, the mixture was heated in a water bath at 70°C for 5 min and then allowed to cool down. UV spectrophotometry was employed to measure the absorbance at a wavelength of 660 nm. Phosphate buffer served as the control, while A was utilized as the standard. Next, the percentage of inhibition of protein denaturation was determined through calculation. 24

M. piperita was harvested and air-dried before being crushed into coarse powder. One kilogram of this powder was then used for extraction with petroleum ether, employing a Soxhlet apparatus at temperatures ranging from 50°C to 60°C for 72 hr. Following the petroleum ether extraction, the remaining material underwent a second extraction with 95% ethanol in a Soxhlet apparatus, at temperatures between 60°C and 70°C for up to 72 hr. The concentrated alcoholic extract obtained was stored in a desiccator, while the residue was retained for further analysis.

Pre-formulation studies and formulation of microemulgel

Surfactants and co-surfactants were evaluated based on their emulsification ability and percentage transmittance. For the surfactant screening, 300 mg each of oil and surfactant were heated at 40-45°C for 30 sec. Subsequently, a 50 mg mixture was diluted to 50 mL with distilled water and left to stand for 2 hr. The percentage transmittance was then measured at 688 nm. Labrafac CC was chosen as the surfactant as it gave higher transmittance.

For the screening of Cosurfactant, a mixture of co-surfactant (100 mg), selected surfactant (200 mg) and oil (300 mg) was heated at 40-45°C for 30 sec and proceeded similarly as for the screening of surfactant. Isopropyl myristate was found to be better ( Table 1 ).

Construction of pseudo-ternary phase diagrams

Pseudo-ternary phase diagrams were generated utilizing the aqueous titration technique for four different weight ratios (1:0, 1:1, 1:2 and 2:1) of Kolliphor PS 80:Transcutol P (S mix ) In every phase diagram M. piperita oil and a specific S mix ratio were meticulously combined in various weight ratios ranging from 1:9 to 9:1. A total of 9 different combinations of oil and S mix were prepared, including 1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2 and 9:1 were made to Precisely outlining To delineate the boundaries of the phases formed in the phase diagrams, a gradual titration with the aqueous phase was performed for each oil and S mix combination. Visual inspection was subsequently employed to identify transparent and effortlessly flowable Oil-in-Water (O/W) microemulsions ( Figure 1 ).

M. piperita oil-3%-5%, Water- 52%-55%, S mix -45%-40% (Kolliphor PS 80: Transcutol P (1:1) ( Table 2 ).

Thermodynamic stability study of microemulgel

After subjecting the formulations to a series of stability tests, including heating-cooling cycles, centrifugation tests and freeze-thaw cycles, no cracking or phase separation was observed in the microemulgel, indicating its favourable thermodynamic stability.

Measurement of globule size and zeta potential

This optimum size of M. piperita microemulgel showed that surfactant, co-surfactant and oil phases were highly compatible with each other as the surface tension increases the globule size decreases ( Figure 2 ).

Figure 1: Ternary phase diagram of concentration for S mix 1:1.

Determination of gravimetric method and partition coefficient

The solubility of M. piperita microemulgel was assessed across various oils (IPM, Propylene glycol, benzyl alcohol and oleic acid), surfactants (Tween 60, Tween 80 and Span 20) and cosurfactants (n-butanol, polyethylene glycol and diethylene glycol). Among these, the highest solubility was observed in IPM oil, Tween 80 surfactant and n-butanol co-surfactant compared to the other oils and surfactants. Microemulsions formed with IPM, Tween 80 and n-butanol was noted to be clear and stable due to the superior compatibility of these surfactant-co-surfactant pairs with the oil.

The microemulsion of M. piperita exhibited a transparent appearance with a light yellow and emitted an aroma reminiscent of alcohol. Notably, the microemulsion remained stable without undergoing phase separation upon the addition of gel to the formulation, confirming its stability.

Measurement of spreadibility of M. piperita microemulgel

Using a drag and slip device, spreadability was assessed. The device was made up of a wooden block with a glass slide installed over it and a pulley fastened to one edge of the block. 2 g of microemulgel was placed over the glass slide over the wooden block and another glass slide with the same measurements was placed on top of it. The weight of around 80 g was suspended by attaching a thread to the upper glass slide with the aid of a hook and passed above the pulley. The time in sec taken by the slide to cover 7.5 cm on the fixed glass slide was recorded. The spreadability was then calculated by using the following formula:

Figure 2: Measurement of globule size and zeta potential.

Figure 3: Particle Size of M. piperita microemulgel (117 nm).

Where, M=weight tied to upper glass slide, L=length of glass slides and T=time taken to separate the glass slides from each other.

Measurement of viscosity of M. piperita microemulgel

One-gram microemulgel was placed between the plate and cone (no.3) of the viscometer (Brookfield viscometer Cap 2000+) and viscosity was measured at 10 rpm for 30 sec. Viscosity was also measured at increasing shear rate. Measurement was done for 30 sec at each rate of shear.

The viscosity of the M. piperita microemulgel measured at 86.7±0.01 cps. The formulation displayed Newtonian flow behaviour, signified by its viscosity showing minimal variation when subjected to external forces such as stirring. This suggests that the microemulgel could endure slight stress and maintain consistent viscosity during both handling and storage ( Table 3 ).

Measurement of pH of M. piperita microemulgel

The pH of the microemulgel was determined to be 6.2, indicating that the elevated S/CO ratio contributed to the increase in pH of the microemulgel.

The stability study suggested that the formulation was physically and chemically stable when stored at 5°C, 25°C with 60% Relative Humidity (RH), 30°C with 65% RH and 40°C with 75% RH over a period of 3 months.

Measurement of particle/droplets size distribution

The size of the droplets was 117 nm. This optimum size of M. piperita microemulgel showed that surfactant, co-surfactant and oil phases were highly compatible with each other ( Figure 3 ).

Measurement of electrical conductivity of M. piperita microemulgel

The conductivity of the M. piperita microemulgel registered at 1.4 μS/cm, confirming its classification as Oil in Water (O/W) microemulgel. Notably, pure water typically exhibits a conductivity. The elevated conductivity observed in the microemulsion was attributed to the presence of dissolved salts and oils. As the amount of dissolved salts and oils in the microemulsion increased, so did its conductivity. The higher conductivity of the M. piperita microemulgel stemmed from the enhanced solubility of oil, surfactant, co-surfactant and water within the formulation.

M. piperita exhibited a sharp endothermic peak at around 204.73°C indicating its pure crystalline cubic form.

Since the dye dispersed evenly within the microemulgel, it was inferred that the continuous phase consisted of water. Consequently, the M. piperita microemulgel was classified as Oil in Water (o/w) type of microemulsion.

FT-IR studies

The distinctive peaks or FT-IR bands of pure M. piperita were identified at 2955.18 cm -1 (C-H) and 1165.99 cm -1 (C=O), as depicted in ( Figure 4 ). These quality peaks of M. piperita were also evident in the M. piperita microemulgel, without any additional auxiliary peaks or significant peak shifts noted. This absence of chemical deterioration or incompatibilities was further confirmed through DSC analysis, which revealed no incompatibilities between M. piperita and the various excipients used. Both M. piperita and the excipients exhibited peaks in the microemulgel thermograms, affirming their compatibility.

Release studies

The maximum release of M. piperita microemulgel was 94% at 48 hr which indicated that M. piperita microemulgel has good topical release properties ( Table 4 ).

Kinetics studies

The distinct peaks or FT-IR bands characteristic of pure M. piperita were detected at 2955.18 cm -1 (C-H) and 1165.99 cm -1 (C=O). These key peaks of M. piperita were similarly observed in the M. piperita microemulgel, with no additional auxiliary peaks or significant shifts noted. This absence of chemical deterioration or incompatibilities was further confirmed by DSC analysis, which demonstrated no incompatibilities between M. piperita and the various excipients utilized. Both M. piperita and the excipients displayed peaks in the microemulgel thermograms, confirming their compatibility.

The M. piperita microemulgel exhibited a drug content of 94.35%. This significant drug content indicates that the drug is highly soluble and exhibits compatibility with the excipients used in the formulation.

In vitro anti-inflammatory activity

The anti-inflammatory activity of the extract and the optimized formulation was evaluated using two methods: the HRBC membrane stabilization method and the protein denaturation method ( Table 5 ). Both the extract and the formulated microemulgel demonstrated significant anti-inflammatory activity. The protein denaturation caused by the extract was also examined and presented ( Table 6 ).

Figure 4: FT-IR Measurement of Percentage Transmittance (%T), Corresponding to M. piperita Peak at 1509.18 cm -1 .

In this study, a microemulgel was developed using an herbal extract of M. piperita to target inflammation. Through experimentation with various surfactants and co-surfactants in preformulation studies revels that M. piperita extract exhibited superior solubility in IPM oil and Tween 80 surfactant, with n-butanol acting as the cosurfactant, surpassing other alternatives. The combination of IPM, Tween 80 and n-butanol yielded a microemulgel that was transparent and remained stable due to their excellent compatibility, which outperformed other surfactant-cosurfactant-oil combinations tested. The formulation demonstrated both physical and thermodynamic stability even under accelerated conditions. The study of Pseudo ternary phase diagram demonstrates that S mix ratio 1:1 provide more stable microemulgel formulation as compared with other S mix ratios. In thermodynamic stability studies, it was observed that there no any separation as well as no cracking of microemulgel. Top of FormBottom of FormThe microemulsion containing M. piperita appeared clear and light yellow, emitting a fragrance reminiscent of alcohol. The spreadability of the formulation varied between 27.21±0.98 and 37.51±0.28. Droplet size averaged approximately 117 nm, while viscosity measured at 86.7±0.01 cps. The dye dispersed uniformly throughout the microemulgel.

The microemulgel containing M. piperita had a pH of 6.2, indicating that the higher ratio of surfactant to cosurfactant contributed to its elevated pH compared to pure water, which typically exhibits a conductivity of 1.4 μS/cm. In the FT-IR spectra, peaks were observed at 2955.18 cm -1 (C-H) and 1165.99 cm -1 (C=O). The maximum release of M. piperita from the microemulgel reached 94% within 48 hr, demonstrating excellent topical release properties. The M. piperita microemulgel showed a drug content of 94.35%. Both the extract and the formulated microemulgel displayed promising anti-inflammatory activity by preventing cell lysis and inhibiting protein denaturation.

In this study, advanced methods is adapted to formulate the microemulgel, this technique can be employed to enhance the solubility and skin permeability. An effective microemulgel was developed using 1% Kolliphor PS 80 as a gelling agent, resulting in sustained release properties and prolonged residence time. The M. piperita microemulgel exhibited a remarkable 94.35% drug content, indicating high solubility and compatibility of the drug with the excipients. Permeability studies revealed that the M. piperita microemulgel achieved 94% permeability within 48 hr, showcasing enhanced drug permeability facilitated by the microemulsion-based gel system. These findings suggest that this formulation holds promise as a topical delivery vehicle for M. piperita . Moreover, the formulated microemulgel demonstrated significant anti-inflammatory activity.

Cite this article:

Machewar K, Kakde R, Sabale P. Evaluation of Topical Anti-Inflammatory Potential of Mentha piperita L. Extract by Formulation of Microemulgel. J Young Pharm. 2024;16(3):488-97.

ACKNOWLEDGEMENT

The author expresses deep gratitude to the management and the Department of Pharmaceutical Sciences at Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, for their invaluable support in facilitating the successful completion of the research work.

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Use of integral forage palm flour as an innovative ingredient in new fettuccine-type pasta: thermomechanical and technological properties, and sensory acceptance.

1. Introduction

2. materials and methods, 2.1. materials, 2.2. methods, 2.2.1. receiving, sanitizing, and processing cladodes for analysis and flour preparation, 2.2.2. proximate composition of integral forage palm cladodes, 2.2.3. microwave radiation drying process, 2.2.4. instrumental color of integral forage palm puree and flour, 2.2.5. total soluble phenolic compounds of integral forage palm puree and flour, 2.2.6. experimental design for fresh and dry extruded fettuccine-type pasta, 2.2.7. thermomechanical properties of flour blends, 2.2.8. water absorption index and water solubility index, 2.2.9. preparation of fresh and dried extruded fettuccine-type pasta, 2.2.10. moisture content and cooking characteristics of fettuccine-type pasta, 2.2.11. texture characteristics of fettuccine-type pasta, 2.2.12. sensory analysis of unflavored and flavored (garlic and oil) fettuccine-type pasta, 2.2.13. statistical analysis, 3. results and discussion, 3.1. proximate composition of integral forage palm puree, 3.2. color parameters and total soluble phenolic compounds in integral forage palm puree and flour, 3.3. thermomechanical characteristics of pre-mixes by mixolab2, 3.4. water absorption index and water solubility index, 3.5. technological evaluation of fettuccine-type pasta quality, 3.5.1. moisture content and cooking characteristics, 3.5.2. texture characteristics, 3.6. sensory evaluation of fettuccine-type pasta quality, 3.6.1. cooked fresh and dry fettuccine-type pasta, 3.6.2. ready-to-eat garlic and oil fettuccine-type pasta, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Silva, L.E.P.d.; Moreira, S.R.; Neves, N.d.A.; Aguiar, E.V.d.; Caprilles, V.D.; Amaral, T.N.; Schmiele, M. Use of Integral Forage Palm Flour as an Innovative Ingredient in New Fettuccine-Type Pasta: Thermomechanical and Technological Properties, and Sensory Acceptance. Foods 2024 , 13 , 2683. https://doi.org/10.3390/foods13172683

Silva LEPd, Moreira SR, Neves NdA, Aguiar EVd, Caprilles VD, Amaral TN, Schmiele M. Use of Integral Forage Palm Flour as an Innovative Ingredient in New Fettuccine-Type Pasta: Thermomechanical and Technological Properties, and Sensory Acceptance. Foods . 2024; 13(17):2683. https://doi.org/10.3390/foods13172683

Silva, Luiz Eliel Pinheiro da, Sander Rodrigues Moreira, Nathalia de Andrade Neves, Etiene Valéria de Aguiar, Vanessa Dias Caprilles, Tatiana Nunes Amaral, and Marcio Schmiele. 2024. "Use of Integral Forage Palm Flour as an Innovative Ingredient in New Fettuccine-Type Pasta: Thermomechanical and Technological Properties, and Sensory Acceptance" Foods 13, no. 17: 2683. https://doi.org/10.3390/foods13172683

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The Impact of Digitalization on Technological Structure of China’s Exports: An Empirical Test Based on the Panel Threshold Effect Model

• Published: 24 August 2024

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• Jia Sun 1 ,
• Sasa Yang 2 &
• Jincheng Li 3  

In the trend of digitalization reshaping the global value chain system, optimizing export technological structure is crucial for coping with complex international environments and enhancing core competitiveness. Based on panel data from 30 provinces and cities in China from 2002 to 2019, this study explores the impact of digitalization on the technological structure of exports. Our findings reveal that (1) digitalization development significantly optimizes the technological structure of exports, and this conclusion holds after a series of robustness tests. (2) The structural optimization effect of digitalization development is constrained by its own level of development, exhibiting nonlinear characteristics in its influence on export technological structure. (3) Enhancing the development level of technology markets, improving technology absorption capacity, and consolidating traditional infrastructure can strengthen the structural optimization effect of digitalization development on exports. Our study enhances understanding of the effectiveness and limitations of digitalization development and provides valuable insights for formulating complementary measures to foster digitalization advancement.

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Foreign Language College, Jilin University of Finance and Economics, Changchun, 130117, China

China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, China

School of Economics, Jilin University, Changchun, 130012, China

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Sun, J., Yang, S. & Li, J. The Impact of Digitalization on Technological Structure of China’s Exports: An Empirical Test Based on the Panel Threshold Effect Model. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02223-1

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Received : 18 May 2023

Accepted : 16 July 2024

Published : 24 August 2024

DOI : https://doi.org/10.1007/s13132-024-02223-1

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