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Lead identification 
Lead identification Vikram Choudhary
油
The document discusses lead identification in drug development. It defines a lead compound as one that shows desired pharmaceutical activity and could potentially be developed into a drug. The document outlines the content to be presented, including an introduction to lead identification, what a lead is, properties of leads, and methods for identifying leads. Key methods discussed are random screening, non-random screening, high-throughput screening, and structure-based drug design.Ethical considerations in Clinical Research Dr Ankita.pptx
Ethical considerations in Clinical Research Dr Ankita.pptxClinosolIndia
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Ethical considerations are paramount in clinical research to protect the rights, well-being, and confidentiality of participants, as well as to ensure data integrity and maintain public trust. Here are some key ethical considerations in clinical research:
Informed Consent: Obtaining informed consent from participants is crucial. Participants should receive clear, understandable information about the study purpose, procedures, potential risks and benefits, alternatives, confidentiality measures, and their right to withdraw at any time. Informed consent should be voluntary, free from coercion, and obtained prior to participation.
Institutional Review Board (IRB) Approval: All clinical research involving human participants must undergo review by an independent IRB or ethics committee. The IRB evaluates the study's ethical implications, participant protections, study design, and informed consent process to ensure that the benefits of the research outweigh the potential risks.
Participant Safety and Monitoring: Safeguarding participant safety is paramount. Researchers should monitor participants closely, promptly address any adverse events or risks, and have protocols in place for participant safety and medical care. Regular safety monitoring and data analysis should be conducted throughout the study.
Data Privacy and Confidentiality: Protecting participant privacy and ensuring data confidentiality are critical. Researchers must adhere to applicable data protection regulations and establish robust measures to safeguard participant data, including de-identification, encryption, secure data storage, and restricted access. Only authorized personnel should have access to identifiable participant information.
Data Integrity and Transparency: Maintaining data integrity is essential for reliable research outcomes. Researchers should accurately collect, record, and report data, adhering to Good Clinical Practice (GCP) guidelines. Data should be analyzed objectively and reported transparently, avoiding any selective or biased reporting that could compromise the scientific integrity of the study.
Equity and Fairness: Clinical research should be conducted in a manner that promotes equity and fairness. Participants should be recruited and selected based on scientifically justified criteria, without discrimination or bias. Efforts should be made to ensure diverse representation in research studies to avoid underrepresentation of certain populations.
Post-trial Access: Participants should have the opportunity to access the study intervention or any relevant follow-up care once the trial is completed, particularly if the intervention has demonstrated significant benefit. Researchers should consider post-trial access in the study design and communicate the availability of such access to participants during the informed consent process.
Dual index adapters with UMIs resolve index hopping and increase sensitivity ...
Dual index adapters with UMIs resolve index hopping and increase sensitivity ...Integrated DNA Technologies
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The rapid increase in throughput of next generation sequencing (NGS) platforms is changing the genomics landscape. Typically, adapters containing sample indexes are added during library construction to allow multiple samples to be sequenced in parallel. Some strategies also introduce a unique molecular identifier (UMI) within the adapter to correct for PCR and sequencing errors. When a UMI is added, reads are assigned to each sample based on their associated sample index, and the UMI is used for error correction during data analysis. For simplicity, a single adapter that is suitable for a variety of applications would be ideal.
xGen速 Dual Index UMI Adapters take the guesswork out of adapter design and ordering. These adapters, created for Illumina sequencers, are compatible with standard library preparation methods and may be sequenced in different modes depending on your application. In addition to unique, dual indexes, the adapters contain a molecular barcode in an optional read position. We will discuss how unique, dual indexes mitigate sample index hopping for multiplexed sequencing and demonstrate how UMIs reduce false positives to improve detection of low-frequency variants. Phylogenetic analysis
Phylogenetic analysisNational Institute of Biologics
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This document provides an overview of phylogenetic analysis, including:
1) Phylogenetic analysis involves inferring evolutionary relationships between taxa by building phylogenetic trees and analyzing character evolution.
2) Phylogenetic trees show the branching patterns and relationships between taxa, with internal nodes representing hypothetical ancestors.
3) Phylogenetic analysis can provide insights into questions like human evolution, disease transmission, and the origins of genetic elements.Gmp & glp
Gmp & glpMUSKANKr
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GLP and GMP are quality systems concerned with organizing and documenting the testing and manufacturing of products like pharmaceuticals, pesticides, and chemicals.
GLP applies to nonclinical safety studies and helps ensure data submitted to regulators is valid. It originated in the 1970s in response to cases like Industrial Bio Test, which falsified lab results. GLP principles cover the organization, facilities, equipment, standard operating procedures, performance, reporting, and record keeping of studies.
GMP aims to consistently produce quality products by having quality control systems in place and following manufacturing procedures. It is designed to minimize risks that cannot be eliminated through testing. The ten GMP principles include facility design, written procedures, documentation, validation, monitoringPhylogenetics: Tree building
Phylogenetics: Tree buildingBioinformatics and Computational Biosciences Branch
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This document provides an overview of phylogenetic analysis concepts and methods. It begins with an introduction to phylogenetic trees and their components. It then covers two main approaches to building trees - using distance methods like neighbor-joining and using optimality criteria like maximum parsimony. Key steps in both approaches like multiple sequence alignment and tree-building algorithms are described. The document concludes with discussing tools for evaluating tree reliability through bootstrapping and exploring available phylogenetics programs.Ethics in Clinical Research: Challenges and Solutions
Ethics in Clinical Research: Challenges and SolutionsClinosolIndia
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Ethics in clinical research is of paramount importance to protect the rights, safety, and well-being of human participants involved in studies. However, there are several challenges that researchers and regulatory bodies face in ensuring ethical practices. Let's discuss some of these challenges and potential solutionsToxicogenomics
ToxicogenomicsGoutham Sarovar
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Toxicogenomics is a field that combines genomics, transcriptomics, proteomics, and metabolomics with toxicology to understand how the genome responds to environmental stressors and toxicants. It aims to understand the relationship between environmental toxins and disease, identify biomarkers of exposure and toxicity, and elucidate molecular toxicity mechanisms. While it provides useful databases of environmental and genomic data, toxicogenomics analysis faces challenges in integrating different types of large datasets and linking "omics" data to specific health effects.Sequence alignment 1
Sequence alignment 1SumatiHajela
油
Sequence alignment involves identifying corresponding portions of biological sequences, such as DNA, RNA, and proteins, in order to analyze similarities and differences at the level of individual bases or amino acids. This can provide insights into structural, functional, and evolutionary relationships. Sequence alignment has many applications, including searching databases for similar sequences, constructing phylogenetic trees, and predicting protein structure. It works by designing an optimal correspondence between sequences that preserves the order of residues while maximizing matches and minimizing mismatches. Quantitative measures of sequence similarity, such as Hamming distance and Levenshtein distance, calculate the number of differences between aligned sequences.Molecular modelling 
Molecular modelling Pharmaceutical
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MM is the excellent strategical tool in the world of computational Chemistry. I tried my best to
compile overall aspects. Student's t test and variations
Student's t test and variationsRizwan S A
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This document discusses the t test and its variations. It describes the t test as a statistical significance test introduced in 1908. The t test can be used to test single or double sample means and the significance of observed values. There are different types of t tests, including one sample t tests, independent samples t tests, and paired t tests. The document reviews the assumptions, formulas, and steps to perform the different t tests, noting they can now be easily conducted with computers.Standard error 
Standard error Satyaki Mishra
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Satyaki Aparajit Mishra presented on the topic of standard error and predictability limits. Standard error is used to estimate the standard deviation from a sample. It is calculated by dividing the standard deviation by the square root of the sample size. A larger standard error means the sample mean is less reliable at estimating the population mean. Standard error helps determine how far sample estimates may be from the true population values. Mishra discussed estimating standard error from a single sample and how standard error is used to test hypotheses. He provided an example of testing if a coin flip was unbiased using the standard error of the proportion of heads observed.Pub med
Pub medAbid Fakhre Alam
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PubMed is a free database of over 23 million citations and abstracts for biomedical literature from Medline, life sciences journals, and online books. It is maintained by the National Center for Biotechnology Information (NCBI) at the U.S. National Library of Medicine (NLM). PubMed provides bibliographic information and links to full-text content from publishers and libraries. Users can search PubMed using keywords, controlled vocabularies like MeSH, or advanced search techniques and filters to find relevant journal articles and resources.Signal Management and Risk Assessment in Pharmacovigilance
Signal Management and Risk Assessment in PharmacovigilanceClinosolIndia
油
Signal management and risk assessment are critical components of pharmacovigilance, aimed at identifying and evaluating potential safety concerns associated with medicinal products. These processes help ensure the ongoing monitoring of product safety and the implementation of appropriate risk minimization strategies. Here's an overview of signal management and risk assessment in pharmacovigilance:
Signal Management:
A signal in pharmacovigilance refers to information that suggests a potential causal relationship between a medicinal product and an adverse event, either previously unrecognized or incompletely understood. Signal management involves the systematic detection, evaluation, and response to these potential safety signals. The goal is to determine whether further investigation is warranted and to take appropriate actions to mitigate risks if necessary.
Signal Management Process:
Signal Detection: Signals can be detected through various methods, including spontaneous reporting databases, literature review, data mining techniques, and cumulative analysis of safety data. Unusual patterns, unexpected associations, or an increase in the frequency of specific adverse events may trigger the need for further investigation.
Signal Evaluation: Once a signal is detected, it undergoes a comprehensive evaluation. This involves analyzing available data, conducting causality assessments, reviewing clinical trial results, and considering factors such as patient demographics, medical history, and concomitant medications.
Signal Validation: Validating a signal involves confirming its credibility and clinical relevance. This step may require additional data collection, case validation, and collaboration between various stakeholders, including regulatory authorities.
Signal Prioritization: Not all signals warrant the same level of attention. Signals are prioritized based on factors such as the severity of the adverse event, the strength of the association, and the potential impact on patient safety.
Risk-Benefit Assessment: A thorough benefit-risk assessment is conducted to weigh the potential risks associated with the signal against the known benefits of the medicinal product. This assessment informs regulatory decisions and the need for further actions.
Risk Minimization: If the signal is deemed credible and significant, risk minimization strategies may be implemented. These strategies could include changes to product labeling, communication to healthcare professionals and patients, and other measures to ensure the safe use of the product.Correlation - Biostatistics
Correlation - BiostatisticsFahmida Swati
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This document discusses correlation and different aspects of studying correlation. It defines correlation as the association or relationship between two variables that do not cause each other. It describes different types of correlation including positive, negative, linear, non-linear, simple, multiple and partial correlation. It also discusses various methods of studying correlation including graphic methods like scattered diagrams and correlation graphs, and algebraic methods like Karl Pearson's correlation coefficient and Spearman's rank correlation coefficient. The document explains concepts like coefficient of determination and hypothesis testing in correlation. It emphasizes that correlation indicates association but does not necessarily imply causation between variables.Bioinformatics, its application main
Bioinformatics, its application mainKAUSHAL SAHU
油
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
FDA Guidelines for Drug Development & Approval
FDA Guidelines for Drug Development & Approvalrahimbrave
油
The document discusses the drug development and approval process in the United States. It describes the roles and responsibilities of the Food and Drug Administration (FDA) in regulating drugs, medical devices, and other products. It then outlines the various phases of clinical trials (Phases I-IV) that drugs must go through to test for safety and efficacy before FDA approval. It also discusses the processes for approving generic drugs, biological products, and modifications to approved drugs.Degrees of freedom
Degrees of freedomMacVasquez
油
The document discusses the history and definition of degrees of freedom. It states that the earliest concept of degrees of freedom was noted in the 1800s in the works of mathematician Carl Friedrich Gauss. The modern understanding was developed by statistician William Sealy Gosset in 1908, though he did not use the term. The term "degrees of freedom" became popular after English biologist Ronald Fisher began using it in 1922 when publishing reports on his work developing chi squares. Degrees of freedom represent the number of values in a study that can vary freely. They are important for understanding chi-square tests and the validity of the null hypothesis.Automating the ACMG Guidelines with VSClinical
Automating the ACMG Guidelines with VSClinicalGolden Helix
油
Clinical Genetic testing requires a complex analysis using the totality of our knowledge about the clinical relevance of a variant and a gene. This includes bioinformatic evidence as well as clinical evidence. The ACMG Guidelines provided a framework in which to score variants based on this evidence, and while some of those scoring criteria require close consultation of the clinical context for a given patient, much of it can be automated.
In this webcast, we review how VSClinical automates the ACMG scoring guidelines while integrating the collective lab expertise from previously classified variants and preferences about genes. We will cover:
Using the ACMG Auto Classifier as part the filtering strategy for gene panels and trio workflows
How gene preferences such as the default transcript, inheritance model, and disorder are updated and saved from VSClinical and used in all future analysis
How the per-variant recommendation engine builds on the auto-classification with descriptive reasons for answering each criterion yes or no
Using the auto-interpretation to present the evidence for all scored criteria in a human-readable paragraph
Working with VSClinicals self-learning knowledgebase that incorporates previously classified variants and genes inform the interpretation of new variants!Hypothesis
HypothesisMmedsc Hahm
油
The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and the process of hypothesis testing. Some key points:
- The null hypothesis states that a population parameter is equal to a specific value. The alternative hypothesis is paired with the null and states inequality.
- Type I errors occur when the null hypothesis is rejected when it is true. Type II errors occur when the null is not rejected when it is false.
- A test statistic is calculated based on sample data and compared to critical values to determine if the null hypothesis can be rejected.
- Hypothesis testing follows steps of stating hypotheses, choosing a significance level, collecting/analyzing data,Database in bioinformatics
Database in bioinformaticsVinaKhan1
油
A database is a structured collection of data that can be easily accessed, managed, and updated. It consists of files or tables containing records with fields. Database management systems provide functions like controlling access, maintaining integrity, and allowing non-procedural queries. Major databases include GenBank, EMBL, and DDBJ for nucleotide sequences and UniProt, PDB, and Swiss-Prot for proteins. The NCBI maintains many biological databases and provides tools for analysis.crispr cas 9
crispr cas 9Asif Nawaz Haral
油
CRISPR-Cas9 is a gene editing technology that uses the bacterial immune system to cut DNA at specific locations. It allows researchers to understand, characterize, and control DNA. CRISPR-Cas9 uses an RNA-guided DNA endonuclease enzyme called Cas9 that is directed by guide RNA to cleave target DNA. It has numerous applications including modifying genes in plants and animals, developing disease resistant crops, and potentially curing genetic diseases in humans by precisely editing genes. While revolutionary, it also raises ethical concerns that must be considered and addressed.Test of significance in Statistics
Test of significance in StatisticsVikash Keshri
油
This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.DECLARATION OF HELSINKI 
DECLARATION OF HELSINKI ClinosolIndia
油
The Declaration of Helsinki is a set of ethical principles and guidelines for medical research involving human subjects. It was first adopted by the World Medical Association (WMA) in 1964 and has been revised multiple times, with the most recent version released in 2013. The Declaration provides a framework to protect the rights, safety, and well-being of individuals participating in research studies. Here are the key elements of the Declaration of Helsinki:
Respect for Autonomy and Informed Consent: The Declaration emphasizes the importance of respecting the autonomy of individuals and their right to make informed decisions about participating in research. It requires researchers to obtain informed consent from participants or their legally authorized representatives, ensuring they have been adequately informed about the study's purpose, procedures, potential risks and benefits, and their right to withdraw at any time.
Beneficence and Risk Assessment: Researchers have a responsibility to maximize potential benefits and minimize potential harm to research participants. The Declaration states that research protocols should be based on a thorough scientific assessment of risks and benefits and should prioritize the well-being of participants.
Ethical Review and Approval: The Declaration highlights the necessity of independent ethical review of research protocols by an appropriate research ethics committee or institutional review board (IRB). The committee should ensure that the study is scientifically valid, ethically sound, and compliant with relevant regulations and guidelines.
Privacy and Confidentiality: The Declaration emphasizes the importance of protecting the privacy and confidentiality of research participants. Researchers should ensure that participants' personal information is kept confidential, and data should be anonymized or pseudonymized whenever possible to protect participant identities.
Data and Safety Monitoring: The Declaration emphasizes the importance of ongoing data monitoring and safety assessments during the research study. Researchers should have plans in place to detect and manage any adverse events or unanticipated risks that may arise during the study.
Vulnerable Populations: Special protections are outlined for vulnerable populations, such as children, pregnant women, prisoners, and individuals with impaired decision-making capacity. Researchers should take extra precautions to ensure their well-being, and their involvement in research should be justified based on the potential benefits to their own population.
Publication and Dissemination of Results: The Declaration emphasizes the responsibility of researchers to publish and share the results of their research in a timely manner. The results should be accurately reported, and negative or inconclusive results should also be disseminated to prevent publication bias.
Dose conversion from animal to human 
Dose conversion from animal to human khaterehz
油
Dose translation from animals to humans requires consideration of factors beyond simple body weight scaling. Body surface area, pharmacokinetics, and species differences must be accounted for to safely determine starting doses in clinical trials. The NOAEL method is commonly used, identifying the no observed adverse effect level from animal studies and applying safety factors to determine a maximum recommended starting dose for humans. Proper selection of animal species and consideration of pharmacologically active doses can also inform translation of doses from animals to humans.Immunoinformatics
Immunoinformaticshanifalpha
油
This document discusses T-cell epitope vaccine design through immunoinformatics. It first defines immunoinformatics as the computational study of the immune response. It then discusses types of vaccines and properties of epitopes, focusing on T-cell epitopes which are recognized by T-cells. The document explains how vaccines provide protection by stimulating memory B and T cells to quickly respond when a pathogen is encountered. It also discusses reverse vaccinology and methods for predicting epitopes, including analyzing binding affinity to MHC molecules using structure-based or sequence-based methods.Probability Biostatics and Research Methodology
Probability Biostatics and Research MethodologyNigar Kadar Mujawar,Womens College of Pharmacy,Peth Vadgaon,Kolhapur,416112
油
The document discusses key probability concepts including probability, binomial distribution, normal distribution, and Poisson distribution. It provides examples of how each concept is applied in pharmaceutical research and drug development, such as calculating the probability of adverse drug events, modeling drug response rates, and analyzing the number of medication errors at a pharmacy.High throughput screening 
High throughput screening RewariBhavya
油
This document discusses high throughput screening and cell-based assays. It begins by defining high throughput screening as a process used in drug discovery to quickly assay a large number of compounds against a biological target to identify hits or leads. It then describes some key aspects of high throughput screening methodology including detection methods like spectroscopy, chromatography, and microscopy. The document outlines the advantages of cell-based assays compared to biochemical assays, noting they provide a more accurate representation using live cells. Finally, it defines the key elements of a cell-based assay as having a cellular component, a target molecule, an instrument, and informatics for data analysis.
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Sequence alignment 1
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油
Sequence alignment involves identifying corresponding portions of biological sequences, such as DNA, RNA, and proteins, in order to analyze similarities and differences at the level of individual bases or amino acids. This can provide insights into structural, functional, and evolutionary relationships. Sequence alignment has many applications, including searching databases for similar sequences, constructing phylogenetic trees, and predicting protein structure. It works by designing an optimal correspondence between sequences that preserves the order of residues while maximizing matches and minimizing mismatches. Quantitative measures of sequence similarity, such as Hamming distance and Levenshtein distance, calculate the number of differences between aligned sequences.Molecular modelling
Molecular modelling Pharmaceutical
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MM is the excellent strategical tool in the world of computational Chemistry. I tried my best to
compile overall aspects. Student's t test and variations
Student's t test and variationsRizwan S A
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This document discusses the t test and its variations. It describes the t test as a statistical significance test introduced in 1908. The t test can be used to test single or double sample means and the significance of observed values. There are different types of t tests, including one sample t tests, independent samples t tests, and paired t tests. The document reviews the assumptions, formulas, and steps to perform the different t tests, noting they can now be easily conducted with computers.Standard error
Standard error Satyaki Mishra
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Satyaki Aparajit Mishra presented on the topic of standard error and predictability limits. Standard error is used to estimate the standard deviation from a sample. It is calculated by dividing the standard deviation by the square root of the sample size. A larger standard error means the sample mean is less reliable at estimating the population mean. Standard error helps determine how far sample estimates may be from the true population values. Mishra discussed estimating standard error from a single sample and how standard error is used to test hypotheses. He provided an example of testing if a coin flip was unbiased using the standard error of the proportion of heads observed.Pub med
Pub medAbid Fakhre Alam
油
PubMed is a free database of over 23 million citations and abstracts for biomedical literature from Medline, life sciences journals, and online books. It is maintained by the National Center for Biotechnology Information (NCBI) at the U.S. National Library of Medicine (NLM). PubMed provides bibliographic information and links to full-text content from publishers and libraries. Users can search PubMed using keywords, controlled vocabularies like MeSH, or advanced search techniques and filters to find relevant journal articles and resources.Signal Management and Risk Assessment in Pharmacovigilance
Signal Management and Risk Assessment in PharmacovigilanceClinosolIndia
油
Signal management and risk assessment are critical components of pharmacovigilance, aimed at identifying and evaluating potential safety concerns associated with medicinal products. These processes help ensure the ongoing monitoring of product safety and the implementation of appropriate risk minimization strategies. Here's an overview of signal management and risk assessment in pharmacovigilance:
Signal Management:
A signal in pharmacovigilance refers to information that suggests a potential causal relationship between a medicinal product and an adverse event, either previously unrecognized or incompletely understood. Signal management involves the systematic detection, evaluation, and response to these potential safety signals. The goal is to determine whether further investigation is warranted and to take appropriate actions to mitigate risks if necessary.
Signal Management Process:
Signal Detection: Signals can be detected through various methods, including spontaneous reporting databases, literature review, data mining techniques, and cumulative analysis of safety data. Unusual patterns, unexpected associations, or an increase in the frequency of specific adverse events may trigger the need for further investigation.
Signal Evaluation: Once a signal is detected, it undergoes a comprehensive evaluation. This involves analyzing available data, conducting causality assessments, reviewing clinical trial results, and considering factors such as patient demographics, medical history, and concomitant medications.
Signal Validation: Validating a signal involves confirming its credibility and clinical relevance. This step may require additional data collection, case validation, and collaboration between various stakeholders, including regulatory authorities.
Signal Prioritization: Not all signals warrant the same level of attention. Signals are prioritized based on factors such as the severity of the adverse event, the strength of the association, and the potential impact on patient safety.
Risk-Benefit Assessment: A thorough benefit-risk assessment is conducted to weigh the potential risks associated with the signal against the known benefits of the medicinal product. This assessment informs regulatory decisions and the need for further actions.
Risk Minimization: If the signal is deemed credible and significant, risk minimization strategies may be implemented. These strategies could include changes to product labeling, communication to healthcare professionals and patients, and other measures to ensure the safe use of the product.Correlation - Biostatistics
Correlation - BiostatisticsFahmida Swati
油
This document discusses correlation and different aspects of studying correlation. It defines correlation as the association or relationship between two variables that do not cause each other. It describes different types of correlation including positive, negative, linear, non-linear, simple, multiple and partial correlation. It also discusses various methods of studying correlation including graphic methods like scattered diagrams and correlation graphs, and algebraic methods like Karl Pearson's correlation coefficient and Spearman's rank correlation coefficient. The document explains concepts like coefficient of determination and hypothesis testing in correlation. It emphasizes that correlation indicates association but does not necessarily imply causation between variables.Bioinformatics, its application main
Bioinformatics, its application mainKAUSHAL SAHU
油
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
FDA Guidelines for Drug Development & Approval
FDA Guidelines for Drug Development & Approvalrahimbrave
油
The document discusses the drug development and approval process in the United States. It describes the roles and responsibilities of the Food and Drug Administration (FDA) in regulating drugs, medical devices, and other products. It then outlines the various phases of clinical trials (Phases I-IV) that drugs must go through to test for safety and efficacy before FDA approval. It also discusses the processes for approving generic drugs, biological products, and modifications to approved drugs.Degrees of freedom
Degrees of freedomMacVasquez
油
The document discusses the history and definition of degrees of freedom. It states that the earliest concept of degrees of freedom was noted in the 1800s in the works of mathematician Carl Friedrich Gauss. The modern understanding was developed by statistician William Sealy Gosset in 1908, though he did not use the term. The term "degrees of freedom" became popular after English biologist Ronald Fisher began using it in 1922 when publishing reports on his work developing chi squares. Degrees of freedom represent the number of values in a study that can vary freely. They are important for understanding chi-square tests and the validity of the null hypothesis.Automating the ACMG Guidelines with VSClinical
Automating the ACMG Guidelines with VSClinicalGolden Helix
油
Clinical Genetic testing requires a complex analysis using the totality of our knowledge about the clinical relevance of a variant and a gene. This includes bioinformatic evidence as well as clinical evidence. The ACMG Guidelines provided a framework in which to score variants based on this evidence, and while some of those scoring criteria require close consultation of the clinical context for a given patient, much of it can be automated.
In this webcast, we review how VSClinical automates the ACMG scoring guidelines while integrating the collective lab expertise from previously classified variants and preferences about genes. We will cover:
Using the ACMG Auto Classifier as part the filtering strategy for gene panels and trio workflows
How gene preferences such as the default transcript, inheritance model, and disorder are updated and saved from VSClinical and used in all future analysis
How the per-variant recommendation engine builds on the auto-classification with descriptive reasons for answering each criterion yes or no
Using the auto-interpretation to present the evidence for all scored criteria in a human-readable paragraph
Working with VSClinicals self-learning knowledgebase that incorporates previously classified variants and genes inform the interpretation of new variants!Hypothesis
HypothesisMmedsc Hahm
油
The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and the process of hypothesis testing. Some key points:
- The null hypothesis states that a population parameter is equal to a specific value. The alternative hypothesis is paired with the null and states inequality.
- Type I errors occur when the null hypothesis is rejected when it is true. Type II errors occur when the null is not rejected when it is false.
- A test statistic is calculated based on sample data and compared to critical values to determine if the null hypothesis can be rejected.
- Hypothesis testing follows steps of stating hypotheses, choosing a significance level, collecting/analyzing data,Database in bioinformatics
Database in bioinformaticsVinaKhan1
油
A database is a structured collection of data that can be easily accessed, managed, and updated. It consists of files or tables containing records with fields. Database management systems provide functions like controlling access, maintaining integrity, and allowing non-procedural queries. Major databases include GenBank, EMBL, and DDBJ for nucleotide sequences and UniProt, PDB, and Swiss-Prot for proteins. The NCBI maintains many biological databases and provides tools for analysis.crispr cas 9
crispr cas 9Asif Nawaz Haral
油
CRISPR-Cas9 is a gene editing technology that uses the bacterial immune system to cut DNA at specific locations. It allows researchers to understand, characterize, and control DNA. CRISPR-Cas9 uses an RNA-guided DNA endonuclease enzyme called Cas9 that is directed by guide RNA to cleave target DNA. It has numerous applications including modifying genes in plants and animals, developing disease resistant crops, and potentially curing genetic diseases in humans by precisely editing genes. While revolutionary, it also raises ethical concerns that must be considered and addressed.Test of significance in Statistics
Test of significance in StatisticsVikash Keshri
油
This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.DECLARATION OF HELSINKI
DECLARATION OF HELSINKI ClinosolIndia
油
The Declaration of Helsinki is a set of ethical principles and guidelines for medical research involving human subjects. It was first adopted by the World Medical Association (WMA) in 1964 and has been revised multiple times, with the most recent version released in 2013. The Declaration provides a framework to protect the rights, safety, and well-being of individuals participating in research studies. Here are the key elements of the Declaration of Helsinki:
Respect for Autonomy and Informed Consent: The Declaration emphasizes the importance of respecting the autonomy of individuals and their right to make informed decisions about participating in research. It requires researchers to obtain informed consent from participants or their legally authorized representatives, ensuring they have been adequately informed about the study's purpose, procedures, potential risks and benefits, and their right to withdraw at any time.
Beneficence and Risk Assessment: Researchers have a responsibility to maximize potential benefits and minimize potential harm to research participants. The Declaration states that research protocols should be based on a thorough scientific assessment of risks and benefits and should prioritize the well-being of participants.
Ethical Review and Approval: The Declaration highlights the necessity of independent ethical review of research protocols by an appropriate research ethics committee or institutional review board (IRB). The committee should ensure that the study is scientifically valid, ethically sound, and compliant with relevant regulations and guidelines.
Privacy and Confidentiality: The Declaration emphasizes the importance of protecting the privacy and confidentiality of research participants. Researchers should ensure that participants' personal information is kept confidential, and data should be anonymized or pseudonymized whenever possible to protect participant identities.
Data and Safety Monitoring: The Declaration emphasizes the importance of ongoing data monitoring and safety assessments during the research study. Researchers should have plans in place to detect and manage any adverse events or unanticipated risks that may arise during the study.
Vulnerable Populations: Special protections are outlined for vulnerable populations, such as children, pregnant women, prisoners, and individuals with impaired decision-making capacity. Researchers should take extra precautions to ensure their well-being, and their involvement in research should be justified based on the potential benefits to their own population.
Publication and Dissemination of Results: The Declaration emphasizes the responsibility of researchers to publish and share the results of their research in a timely manner. The results should be accurately reported, and negative or inconclusive results should also be disseminated to prevent publication bias.
Dose conversion from animal to human
Dose conversion from animal to human khaterehz
油
Dose translation from animals to humans requires consideration of factors beyond simple body weight scaling. Body surface area, pharmacokinetics, and species differences must be accounted for to safely determine starting doses in clinical trials. The NOAEL method is commonly used, identifying the no observed adverse effect level from animal studies and applying safety factors to determine a maximum recommended starting dose for humans. Proper selection of animal species and consideration of pharmacologically active doses can also inform translation of doses from animals to humans.Immunoinformatics
Immunoinformaticshanifalpha
油
This document discusses T-cell epitope vaccine design through immunoinformatics. It first defines immunoinformatics as the computational study of the immune response. It then discusses types of vaccines and properties of epitopes, focusing on T-cell epitopes which are recognized by T-cells. The document explains how vaccines provide protection by stimulating memory B and T cells to quickly respond when a pathogen is encountered. It also discusses reverse vaccinology and methods for predicting epitopes, including analyzing binding affinity to MHC molecules using structure-based or sequence-based methods.Probability Biostatics and Research Methodology
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The document discusses key probability concepts including probability, binomial distribution, normal distribution, and Poisson distribution. It provides examples of how each concept is applied in pharmaceutical research and drug development, such as calculating the probability of adverse drug events, modeling drug response rates, and analyzing the number of medication errors at a pharmacy.High throughput screening
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This document discusses high throughput screening and cell-based assays. It begins by defining high throughput screening as a process used in drug discovery to quickly assay a large number of compounds against a biological target to identify hits or leads. It then describes some key aspects of high throughput screening methodology including detection methods like spectroscopy, chromatography, and microscopy. The document outlines the advantages of cell-based assays compared to biochemical assays, noting they provide a more accurate representation using live cells. Finally, it defines the key elements of a cell-based assay as having a cellular component, a target molecule, an instrument, and informatics for data analysis.Probability Biostatics and Research Methodology
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