Podcast
Questions and Answers
How does the isolation of tissues or organs in Ex-vivo studies impact control over experimental conditions compared to In-vivo studies?
How does the isolation of tissues or organs in Ex-vivo studies impact control over experimental conditions compared to In-vivo studies?
Ex-vivo studies offer greater control over experimental conditions compared to In-vivo studies because the tissue or organ is isolated, allowing for more controlled manipulation and observation.
What key ethical consideration distinguishes In-vivo studies from In-vitro studies?
What key ethical consideration distinguishes In-vivo studies from In-vitro studies?
In-vivo studies often raise more significant ethical concerns, particularly regarding animal welfare and human subject rights, compared to in-vitro studies, which typically involve fewer ethical considerations if using cell lines or non-human cells.
In what way do the ethical considerations typically differ between In-vivo and In-vitro studies, particularly concerning the use of cell lines?
In what way do the ethical considerations typically differ between In-vivo and In-vitro studies, particularly concerning the use of cell lines?
In-vivo studies often involve significant ethical considerations related to animal or human subjects. In-vitro studies typically involve fewer ethical concerns, especially when using cell lines or non-human cells.
How might a researcher adjust data analysis techniques in response to the limitations of an In-vitro model compared to an In-vivo model?
How might a researcher adjust data analysis techniques in response to the limitations of an In-vitro model compared to an In-vivo model?
Explain the trade-off between biological relevance and experimental control when choosing between In-vivo and In-vitro studies.
Explain the trade-off between biological relevance and experimental control when choosing between In-vivo and In-vitro studies.
What are the key reasons for using animal models that are genetically engineered in disease studies?
What are the key reasons for using animal models that are genetically engineered in disease studies?
Explain how transmission studies influence the choice of a research model (In-vivo, In-vitro, or Ex-vivo).
Explain how transmission studies influence the choice of a research model (In-vivo, In-vitro, or Ex-vivo).
How does the potential for ethical concerns in research influence the selection between In-vivo and In-vitro experimental designs?
How does the potential for ethical concerns in research influence the selection between In-vivo and In-vitro experimental designs?
How does the complexity of interactions within a biological system affect the decision to use an In-vivo versus an In-vitro model?
How does the complexity of interactions within a biological system affect the decision to use an In-vivo versus an In-vitro model?
What role does similarity to human physiology and metabolism play in the selection of a species for In-vivo studies?
What role does similarity to human physiology and metabolism play in the selection of a species for In-vivo studies?
What is the primary goal of hypothesis-driven research, and how does it contrast with hypothesis-generating research?
What is the primary goal of hypothesis-driven research, and how does it contrast with hypothesis-generating research?
Explain how the volume and type of data output typically differ between hypothesis-generating and hypothesis-driven research approaches.
Explain how the volume and type of data output typically differ between hypothesis-generating and hypothesis-driven research approaches.
Describe how the ex-vivo model using human lung tissue explants helps researchers to study cell-cell communication during viral infections.
Describe how the ex-vivo model using human lung tissue explants helps researchers to study cell-cell communication during viral infections.
What is a key limitation of cross-sectional studies in establishing causal relationships?
What is a key limitation of cross-sectional studies in establishing causal relationships?
In case-control studies, how does an odds ratio greater than 1 ($OR > 1$) typically inform the relationship between a risk factor and a disease?
In case-control studies, how does an odds ratio greater than 1 ($OR > 1$) typically inform the relationship between a risk factor and a disease?
Describe the key distinction between ecological studies and case-control studies in terms of the level at which data, results, and conclusions are applied.
Describe the key distinction between ecological studies and case-control studies in terms of the level at which data, results, and conclusions are applied.
How does the selection of participants differ between cohort and case-control studies, particularly in relation to the outcome of interest?
How does the selection of participants differ between cohort and case-control studies, particularly in relation to the outcome of interest?
How do cohort studies address the issue of temporality, which is often a limitation in cross-sectional studies?
How do cohort studies address the issue of temporality, which is often a limitation in cross-sectional studies?
What is a key advantage of matched-pair designs in experimental studies, and how does it enhance the validity of the study?
What is a key advantage of matched-pair designs in experimental studies, and how does it enhance the validity of the study?
In the context of experimental design, what differentiates a completely randomized experiment from a randomized block design?
In the context of experimental design, what differentiates a completely randomized experiment from a randomized block design?
What is the main purpose of 'blocking' in an experimental design, and how does it help to reduce experimental error?
What is the main purpose of 'blocking' in an experimental design, and how does it help to reduce experimental error?
How does 'randomization' help to eliminate bias in experimental designs?
How does 'randomization' help to eliminate bias in experimental designs?
Describe a scenario where a quasi-experimental study design would be more appropriate than a randomized controlled trial.
Describe a scenario where a quasi-experimental study design would be more appropriate than a randomized controlled trial.
Explain how the application of statistical analyses and data handling techniques is influenced by the choice of experimental design.
Explain how the application of statistical analyses and data handling techniques is influenced by the choice of experimental design.
What is the primary goal of replication in experimental design, and how does it contribute to the reliability of results?
What is the primary goal of replication in experimental design, and how does it contribute to the reliability of results?
How does the presence of confounders affect the interpretation of results in observational studies compared to designed experiments?
How does the presence of confounders affect the interpretation of results in observational studies compared to designed experiments?
Describe the key characteristics of single and double-blinded experiments. How do those characteristics relate to minimized bias?
Describe the key characteristics of single and double-blinded experiments. How do those characteristics relate to minimized bias?
For what kind of experiment would a 'washout' period be most critical? What purpose does the washout serve?
For what kind of experiment would a 'washout' period be most critical? What purpose does the washout serve?
Describe how 'factorial design' allows researchers to examine multiple independent variables at the same time.
Describe how 'factorial design' allows researchers to examine multiple independent variables at the same time.
In a study testing a new vaccine, explain why researchers might choose to use a randomized controlled trial instead of simply observing the health outcomes of people who voluntarily get vaccinated.
In a study testing a new vaccine, explain why researchers might choose to use a randomized controlled trial instead of simply observing the health outcomes of people who voluntarily get vaccinated.
In brief, explain the goal of statistical analysis.
In brief, explain the goal of statistical analysis.
What are the typical 'levels' for the variable 'Drug' in a study that compares a drug to a placebo?
What are the typical 'levels' for the variable 'Drug' in a study that compares a drug to a placebo?
Explain how a 'control' group is typically used in a designed experiment.
Explain how a 'control' group is typically used in a designed experiment.
If you were designing a study to see if a new fertilizer can improve crop yield, what would be a good 'control' treatment?
If you were designing a study to see if a new fertilizer can improve crop yield, what would be a good 'control' treatment?
Thinking about the 'COVID-19 vaccine trial' scenario in the slides, why should the scientists ensure proper labeling of the samples collected?
Thinking about the 'COVID-19 vaccine trial' scenario in the slides, why should the scientists ensure proper labeling of the samples collected?
Thinking about the 'COVID-19 vaccine trial' scenario in the slides, why is it important for the number of individuals in each experimental group to be approximately equal?
Thinking about the 'COVID-19 vaccine trial' scenario in the slides, why is it important for the number of individuals in each experimental group to be approximately equal?
Name two study designs that do not feature randomization.
Name two study designs that do not feature randomization.
What kind of studies are best addressed with animal models and what are their limitations?
What kind of studies are best addressed with animal models and what are their limitations?
What are the components of the experiment?
What are the components of the experiment?
Flashcards
Research
Research
A process to investigate a question or solve a problem in a structured and systematic way.
Hypothesis
Hypothesis
A tentative explanation for a phenomenon, used as a basis for further investigation.
In-vivo
In-vivo
Research conducted within a whole, living organism, allowing complex interactions to be observed.
In-vitro
In-vitro
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Ex-vivo
Ex-vivo
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In-silico
In-silico
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Observational Study
Observational Study
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Designed Experiment
Designed Experiment
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Ecological studies
Ecological studies
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Cross-Section Study
Cross-Section Study
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Case Report
Case Report
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Case-Control Study
Case-Control Study
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Cohort study
Cohort study
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Completely Randomized Experiment
Completely Randomized Experiment
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Randomized block design
Randomized block design
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Study Notes
- BIOT 1012 is a course on Research Methods and Data Management
- The lecturer is Dr. Mohamed Samir, and his email is [email protected] Office location: G218
- Lecture 1 Date: 23.01.2025
Topics covered:
- Experimental design
- Types of hypotheses
- Models in research
- Experiments versus studies
- Components of experiments/study
- Types of experiments
- Types of studies
- How data analysis, management, and statistics are affected by experimental design
- A Research cycle flows from identifying an idea through identifying biomarkers as the goal.
- Key steps in a research cycle:
- Identifying a promising biomarker to study, which is collected from subject groups from blood samples
- Send sample to Mass Spectrometry
- Analyze data with machine learning
- Identify biomarker
Hypothesis
- Hypothesis generating/free:
- Aims to identify potential metabolite biomarkers for COVID-19 severity
- Collect blood plasma samples from participants with COVID-19 and perform untargeted metabolomic analysis
- Hypothesis is not required
- Data output is large
- Aims at patterns/relations
- Novelty with novel molecules
- Examples such as Omics
- Hypothesis-driven:
- Aims to test the role of specific lipid metabolites as biomarkers for inflammatory response in COVID-19
- Conducts blood samples from COVID-19 patients and non-infected controls for lipid molecules confirmation
- Hypothesis is required
- Data output is limited
- Aims to confirm/reject hypothesis
- Novelty is known
- Examples like is Drug A effective than Drug B
Models in Research
- Common models:
- In-vivo in a whole living biological system
- In-vitro in a laboratory
- Ex-vivo on parts/tissue from living biological systems
- In-silico on a computer
- In-vivo:
- Is carried out in a whole living biological system
- Examples of models are CAM, drosophila, zebrafish, rodent, dog, pig, primates
- Mice, rat, human, pet, monkeys, rabbits, and camels can be used
- In-vitro:
- Is carried out in a laboratory.
- Uses primary cells in cell culture (2D) and a test tube
- Ex-vivo:
- Is carried out on parts/tissue from a living biological system.
- Uses organs in a complete tissue
- An example is human lungs transferred to a lab
- In-silico:
- Is carried out on a computer
- Examples include molecular docking, bioinformatics, and machine learning.
In Vivo, In Vitro, Ex Vivo
- In-Vivo:
- Research conducted on living organisms.
- High complexity that includes interactions between various biological systems.
- Limited control over variables due to being a complex system.
- Involves ethical concerns.
- Aims to translate to human biology directly.
- Applications include drug testing, disease modeling, genetic/behavioral studies.
- Time scale is long
- Animal and human clinical trials
- Ex-Vivo:
- Research using tissues, organs, or cells taken from a living organisms and maintained outside the body.
- Moderate complexity
- Greater control over variables allowing for more regulated conditions.
- Involves less ethical issues
- May lose functional complexity
- Application for tissue function studies, drug testing, metabolic studies and transplants
- Moderate time scale
- Isolated human or animal tissues
- In-Vitro:
- Research using isolated cells or cell cultures in a laboratory setting.
- Low to moderate simplified model with individual cells or cell cultures.
- Highest control over variables.
- Ethical concerns are low
- May not fully replicate in-vivo environments
- Application for drug screening, genetic and cellular response studies
- Time Scale is short
- Cell culture models
- Choice of Models Depends on:
- Research place (resources or funds)
- Ethical considerations
- Research question (transmission studies need in-vivo systems)
- Level of biological complexity
- Time needed
- Confounders
- In-Vivo is best for whole-organism systemic processes and complex interactions
- Ex-Vivo is best for isolated organs or tissues with more control but preserves biological complexity
- In-Vitro is best for cellular or molecular mechanisms in a simplified environment with maximum control
- Considerations when using In-vivo models:
- Biological relevance and transmissibility - for example, the use of ferrets vs monkeys
- Relevance to disease - for example, ducks and fish
- Size and practical considerations
- Genetic and Physiological Features
- Species-specific characteristics like the immune system and metabolism
Experiments vs Studies
- Estrogen treatment in post-menopausal women was studied.
- Observational study:
- 93,676 enrollees started in 1991 and were tracked for 8 years.
- The input variables were whether they used estrogen treatments and factors like age, race, and diet.
- The output variable was coronary heart disease and breast cancer
- Positive results: increased likelihood of good health associated with estrogen treatments
- Experimental study:
- The Women's Health Initiative(WHI) randomized a controlled trial.
- 373,092 were eligible, 18,845 consented, and 16,608 enrolled
- Used 16,608 women randomized to estrogen or no estrogen.
- Women blocked together by age (3 groups; 50-59, 60-69, and 70-79), and by clinic attended and the treatments randomly assigned
- Positive results that increased CHD and breast cancer association with estrogen treatment
- Observational Experiment is different:
- Due to its subjects already deciding to take estrogen before the study started
- Its cohort being more health conscious
- A correlation being due to participants taking better care of themselves
- Observation vs Experiment:
- Observational can establish association but not causation
- Experiment can establish both.
- Experiment vs Study:
- Data source
- Designed Experiment: Researchers
- Observational Study: Exists beforehand
- Randomized treatments
- Designed Experiment: Yes
- Observational Study: No
- Surrounding conditions
- Designed Experiment: Controlled conditions (matched groups); and no error/bias or confounders
- Observational Study: not controlled and has a confounder effect
- Conclusions
- Designed Experiment: Association & Causation
- Observational Study: Association only
- Within unit variation
- Designed Experiment: Low
- Observational Study: High
- Solid conclusions
- Designed Experiment: Need for few units
- Observational Study: Needs high number of units
- Disadvantage
- Designed Experiment: Tight control; Not a relevant model and non-applicable
- Observational Study: More variation
- Advantage
- Designed Experiment: Needs repetition
- Observational Study: Real data applicable
- Type of tests
- Designed Experiment: Univariate or bivariate
- Observational Study: Multivariate
- Data source
Aspirin Experiment
- Aspirin significantly prevents heart diseases, which is an experiment
- S1 through S10
- Myocardial infarction, stroke and deaths were measured
Components of Experiments/Study
- Research question "Which drug has more effect on blood pressure?"
- Three sets for each test
- Control w/o drugs
- Drug A
- Drug B
- The experiment can be measured at the end of Day 0, Week 1, Week 2, Week 3, and Week 4
- The above blood pressure results are an example of experimental units or subjects; factors, features or variable and treatment/factor/intervention.
- Components:
- Hypothesis:
- is drug A is different from drug B or their are no difference?
- Control:
- Two or more treatments should be compared is to Ctr, drug A drug B
- Randomization:
- The experimental units should be randomly divided into groups can matched age/gender is done
- Replication:
- An efficient amount of experimental units is 6-biological replicates
- Blocking:
- Results can depend on Compare results within blocks is potential confounder
- Hypothesis:
Correct experimental design:
- Hypothesis:
- Drug A is different from drug B.
- Control:
- Two or more treatments should be compared: Group Ctr, drug A and drug B.
- Randomization:
- The experimental units divide randomly into groups to avoid selection bias.
- Including matched the age and gender
- Replication:
- A sufficient number of experimental units: Six biological replicates
Types of Experiments
- Completely Randomized Experiment:
- Each unit randomly assignd to different groups to revive treatments
- The unit in the some group will receive the same treatment
- Compares Results of each treatment betwwen groups
- Advantage: simple to implement and reduces the risk of contamination between experimental conditions.
- Limitations: Requires more participants, possible variability between groups can confound results and individual differences.
- Randomized blocking design:
- The whole cohort divides based on the blocker into "blocks".
- The units in each block compares internally. and takes results of recording
- Matched paired design
- individual and variation experiment with effect
- Matched pair in two treatments we compares in paired unit
- In-randomized experiment, individual variation has an effect
- In Matched paired, we compare the two treatments in the same unit {paired}
- Grace period in drug studies, are to be considered
- Advantages requires fewer participants, and controls for individual differs
- Limitations potential of carryover effect treatment from previous affecting next.
- Factorial design
- Involves studying the effects of two or more independent variables (factors) simultaneously.
- Multiple levels per factor that can multiple combinations
- Factor 1 for drugs Factor A and B
- Factor 2 for Low and High Doses
- Advsantages for individual and combined effects from multiple factors and efficient for participant usage
- Limitation is more complex
- Quasi experimental design
- A non-random unites assignment compared to groups
- a pre-existing and naturally groups for the non-independent varibales
- Useful not non-possibility of randomness
Types of Studies
- Ecological studies
- group of popualtion used for feature or Phenonom on for Sociocoty systems.
- Data results and the other includes are at popaulaitons level
- Used with highpthersis use with free researches
- Examples like different rate or disease with different county
- Also Generates highpthersis by questions/generatoin for future Investigation and Comparing population that are over-timed .
- Cross-Section study
- Is to asses a population where exmaiatn to a variables reailsihnsiop
- repersnttaive population should study undertaken should
- Is quick
- Is Inexpnsive
- Cant do caausilty
- Examples COVID-19 in the UK in 2024 in the study
- Case report and Case Series, with a Detailed observaoty indviuaks reports and the serie reports multiple
Case-Control studies
- Compare inidvaiuas with coinditona of case and used for idnfeitcaotp of risk and predict like case factors
- the factors like high risks used the conditonal reports
- Cohort Study
- Following group for rsk that has determineto find reuslats
- EXAPMPLE stduys that follows from to non compare with over lung c acner time by two periods years
Experiments
- Testing for efficiency of new vaccine again COVID-19 infection -Randomized block study: - Antibody tiers by Continuous Data: and viral tiers by contnousu Data. -The system should track of wasout by periods and should ensures the labeling is porpoer - Full Factory Design -Antibody titres by Continous data and Virul Teris used by continous by data to a timing effects
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