An introduction to the tools and methods used for the bioinformatics analysis of ChIP-Seq data.
Written and delivered for the "Epigenetics and its applications in clinical research" course at the Karolinska Institute in Stockholm, Sweden.
Next Generation Sequencing & DNA Synthesis: Technology, Consumables Manufactu...Yole Developpement
With the sequencer installed base doubling, the sequencing consumables market will reach $7.8B by 2024.
More information on that report at https://www.i-micronews.com/products/next-generation-sequencing-dna-synthesis-technology-consumables-manufacturing-and-market-trends-2019/
This presentation gives an easy introduction to ChIP-seq analyses and is part of a bioinformatics workshop. The accompanying websites are available at http://sschmeier.github.io/bioinf-workshop/#!galaxy-chipseq/
An introduction to the tools and methods used for the bioinformatics analysis of ChIP-Seq data.
Written and delivered for the "Epigenetics and its applications in clinical research" course at the Karolinska Institute in Stockholm, Sweden.
Next Generation Sequencing & DNA Synthesis: Technology, Consumables Manufactu...Yole Developpement
With the sequencer installed base doubling, the sequencing consumables market will reach $7.8B by 2024.
More information on that report at https://www.i-micronews.com/products/next-generation-sequencing-dna-synthesis-technology-consumables-manufacturing-and-market-trends-2019/
This presentation gives an easy introduction to ChIP-seq analyses and is part of a bioinformatics workshop. The accompanying websites are available at http://sschmeier.github.io/bioinf-workshop/#!galaxy-chipseq/
Knowing Your NGS Upstream: Alignment and VariantsGolden Helix Inc
Alignment algorithms are not just about placing reads in best-matching locations to a reference genome. They are now being expected to handle small insertions, deletions, gapped alignment of reads across intron boundaries and even span breakpoints of structural variations, fusions and copy number changes. At the same time, variant-calling algorithms can only reach their full potential by being intimately matched to the aligner's output or by doing local assemblies themselves. Knowing when these tools can be expected to perform well and when they will produce technical artifacts or be incapable of detecting features is critical when interpreting any analysis based on their output.
This presentation will compare the performance of the alignment and variant calling tools used by sequencing service providers including Illumina Genome Network, Complete Genomics and The Broad Institute. Using public samples analyzed by each pipeline, we will look at the level of concordance and dive into investigating problematic variants and regions of the genome.
Journal club slides for "Detection of structural DNA variation from next generation sequencing data: a review of informatic approaches" and a description of the software pipeline digit
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
In plant and animal biotechnology, we used marker genes as selection of our GOI in host organism, but there are some problems related o these marker genes. Here we discussed about some marker free mathedologies.
Genomic Big Data Management, Integration and Mining - Emanuel WeitschekData Driven Innovation
Thanks to Next Generation Sequencing (NGS), a technology that is lowering the cost and time of reading DNA, we are faced with huge amounts of biomedical data. These data are continuously collected by research laboratories, and often organized through world-wide consortia, which are releasing many public data bases. One of the main aims of bioinformatics is to solve fundamental issues in biomedicine research (e.g., how cancer occurs) starting from big genomic data and their analysis. In this talk I will give an overview of big genomic data management, integration, and mining.
RNA Sequence data analysis,Transcriptome sequencing, Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required.
RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms.
Data set produced is large and complex; interpretation is not straight forward.
Metatranscriptomics is the science that studies gene expression of microbes within natural environments, i.e., the metatranscriptome. It also allows to obtain whole gene expression profiling of complex microbial communities.
Presentation carried out by Sergi Beltran Agulló, from the CNAG, at the course: Identification and analysis of sequence variants in sequencing projects: fundamentals and tools .
ClinVar: Aggregating Data to Improve Variant Interpretation - Melissa LandrumHuman Variome Project
The rate of variant discovery continues to surpass the rate of clinicalgrade interpretation. This is a challenge for precision medicine, because fast, reliable access to variant interpretations is necessary to provide well-informed and timely interpretations of test results to patients. ClinVar is a public repository for interpretations of clinical significance and functional effects of variants in any gene and for any disease. Interpretations are submitted by many sources, including clinical testing laboratories, research laboratories, locus-specific databases, expert panels, practice guidelines, as well as OMIM® and GeneReviews™. Collecting variant interpretations in ClinVar depends on integrating data from these different sources, which has several benefits. First, data integration requires standardizing the data from each source. This improves the quality of the data in ClinVar as well as in each of the individual datasets. ClinVar staff validate HGVS expressions as a routine part of ClinVar submission processing. Submitters are encouraged to use standard terms in MedGen for diseases and phenotypes. Standard terms for clinical significance are used in ClinVar when available; for example, ClinVar uses the terms recommended by ACMG to classify variants for Mendelian diseases. Secondly, ClinVar aggregates all data for a variant defined by its genomic location. Therefore, HGVS descriptions on different transcripts or on different genomic sequences can be recognized as the same variant. Thirdly, integrating data from multiple submitters allows the evidence from all sources to be pooled together. This larger collection of evidence aids the re-evaluation of variant classifications, and is especially valuable for rare variants and novel gene-disease relationships. Fourthly, data integration means that variant interpretations from different sources can be viewed together and compared. Thus a ClinVar user has access to interpretations outside any internal system and knows when there is consensus in the interpretation or not. Submitting laboratories use reports of conflicting interpretations in ClinVar to prioritize variants that they should re-evaluate. ClinVar receives data from many data providers, and therefore provides clear attribution to each contributing group, including links to records in LSDBs. Each source may update their submission to ClinVar at any time. For example, a record may be updated when a variant is re-classified or when additional evidence is available to support the interpretation. Submitters may consider providing regular updates to ClinVar to prevent their interpretations from becoming out of date. Submissions to ClinVar describe variants that range in complexity from simple alleles with explicit sequence locations through copy number changes and cytogenetic rearrangements with fuzzy boundaries.
Knowing Your NGS Upstream: Alignment and VariantsGolden Helix Inc
Alignment algorithms are not just about placing reads in best-matching locations to a reference genome. They are now being expected to handle small insertions, deletions, gapped alignment of reads across intron boundaries and even span breakpoints of structural variations, fusions and copy number changes. At the same time, variant-calling algorithms can only reach their full potential by being intimately matched to the aligner's output or by doing local assemblies themselves. Knowing when these tools can be expected to perform well and when they will produce technical artifacts or be incapable of detecting features is critical when interpreting any analysis based on their output.
This presentation will compare the performance of the alignment and variant calling tools used by sequencing service providers including Illumina Genome Network, Complete Genomics and The Broad Institute. Using public samples analyzed by each pipeline, we will look at the level of concordance and dive into investigating problematic variants and regions of the genome.
Journal club slides for "Detection of structural DNA variation from next generation sequencing data: a review of informatic approaches" and a description of the software pipeline digit
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
In plant and animal biotechnology, we used marker genes as selection of our GOI in host organism, but there are some problems related o these marker genes. Here we discussed about some marker free mathedologies.
Genomic Big Data Management, Integration and Mining - Emanuel WeitschekData Driven Innovation
Thanks to Next Generation Sequencing (NGS), a technology that is lowering the cost and time of reading DNA, we are faced with huge amounts of biomedical data. These data are continuously collected by research laboratories, and often organized through world-wide consortia, which are releasing many public data bases. One of the main aims of bioinformatics is to solve fundamental issues in biomedicine research (e.g., how cancer occurs) starting from big genomic data and their analysis. In this talk I will give an overview of big genomic data management, integration, and mining.
RNA Sequence data analysis,Transcriptome sequencing, Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required.
RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms.
Data set produced is large and complex; interpretation is not straight forward.
Metatranscriptomics is the science that studies gene expression of microbes within natural environments, i.e., the metatranscriptome. It also allows to obtain whole gene expression profiling of complex microbial communities.
Presentation carried out by Sergi Beltran Agulló, from the CNAG, at the course: Identification and analysis of sequence variants in sequencing projects: fundamentals and tools .
ClinVar: Aggregating Data to Improve Variant Interpretation - Melissa LandrumHuman Variome Project
The rate of variant discovery continues to surpass the rate of clinicalgrade interpretation. This is a challenge for precision medicine, because fast, reliable access to variant interpretations is necessary to provide well-informed and timely interpretations of test results to patients. ClinVar is a public repository for interpretations of clinical significance and functional effects of variants in any gene and for any disease. Interpretations are submitted by many sources, including clinical testing laboratories, research laboratories, locus-specific databases, expert panels, practice guidelines, as well as OMIM® and GeneReviews™. Collecting variant interpretations in ClinVar depends on integrating data from these different sources, which has several benefits. First, data integration requires standardizing the data from each source. This improves the quality of the data in ClinVar as well as in each of the individual datasets. ClinVar staff validate HGVS expressions as a routine part of ClinVar submission processing. Submitters are encouraged to use standard terms in MedGen for diseases and phenotypes. Standard terms for clinical significance are used in ClinVar when available; for example, ClinVar uses the terms recommended by ACMG to classify variants for Mendelian diseases. Secondly, ClinVar aggregates all data for a variant defined by its genomic location. Therefore, HGVS descriptions on different transcripts or on different genomic sequences can be recognized as the same variant. Thirdly, integrating data from multiple submitters allows the evidence from all sources to be pooled together. This larger collection of evidence aids the re-evaluation of variant classifications, and is especially valuable for rare variants and novel gene-disease relationships. Fourthly, data integration means that variant interpretations from different sources can be viewed together and compared. Thus a ClinVar user has access to interpretations outside any internal system and knows when there is consensus in the interpretation or not. Submitting laboratories use reports of conflicting interpretations in ClinVar to prioritize variants that they should re-evaluate. ClinVar receives data from many data providers, and therefore provides clear attribution to each contributing group, including links to records in LSDBs. Each source may update their submission to ClinVar at any time. For example, a record may be updated when a variant is re-classified or when additional evidence is available to support the interpretation. Submitters may consider providing regular updates to ClinVar to prevent their interpretations from becoming out of date. Submissions to ClinVar describe variants that range in complexity from simple alleles with explicit sequence locations through copy number changes and cytogenetic rearrangements with fuzzy boundaries.
Presentation by Justin Zook at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on benchmarks for indels and structural variants.
Golden Helix’s SNP & Variation Suite (SVS) has been used by researchers around the world to do trait analysis and association testing on large cohorts of samples in both humans and other species. As Next-Generation Sequencing of whole genomes becomes more affordable, large cohorts of Whole Genome Sequencing (WGS) samples are available to search for additional trait association signals that were not found in array-based testing. In fact, recent papers have shown that WGS analysis using advanced GREML (Genomic Relatedness Restricted Maximum Likelihood) techniques is able to outperform micro-array based GWAS methods in the analysis of complex traits and proportion of the trait heritability explained.
Our latest update release of SVS has expanded the exiting maximum likelihood and GRM methods to support these new techniques. We have also enhanced various other association testing and prediction methodologies. This webcast showcases:
- Newly supported analysis workflow for whole genome variants using LD binning and enhanced GBLUP analysis
- Enhanced gender correction using REML
- Additional capabilities for genomic prediction and phenotype prediction
We are continually improving our products based on our customer’s feedback. We hope you enjoy this recording highlighting the exciting new features and select enhancements we have made.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...
Genome in a Bottle- reference materials to benchmark challenging variants and regions of the human genome 210930
1. Genome in a Bottle: Reference
Materials to Benchmark
Challenging Variants and
Regions of the Human Genome
Justin Zook, on behalf of the Genome in a Bottle Consortium
National Institute of Standards and Technology (NIST)
Human Genomics Team
Sept 30, 2021
2. Motivation for Genome in a Bottle: Sequencing and analysis methods can give
different answers, particularly in challenging, repetitive regions
O’Rawe et al, Genome Medicine, 2013
https://doi.org/10.1186/gm432
3. GIAB has characterized variants in 7
human genomes
National I nstituteof S tandards & Te
c
hnology
Re
port of I nve
stigation
Reference Material 8391
Human DNA for Whole-Genome Variant Assessment
(Son of Eastern European Ashkenazim Jewish Ancestry)
This Reference Material (RM) is intended for validation, optimization, and process evaluation purposes. It consists
of a male whole human genome sample of Eastern European Ashkenazim Jewish ancestry, and it can be used to assess
performance of variant calling from genome sequencing. A unit of RM 8391 consists of a vial containing human
genomic DNA extracted from a single large growth of human lymphoblastoid cell line GM24385 from the Coriell
Institute for Medical Research (Camden, NJ). The vial contains approximately 10 µg of genomic DNA, with the peak
of the nominal length distribution longer than 48.5 kb, as referenced by Lambda DNA, and the DNA is in TE buffer
(10 mM TRIS, 1 mM EDTA, pH 8.0).
This material is intended for assessing performance of human genome sequencing variant calling by obtaining
estimates of true positives, false positives, true negatives, and false negatives. Sequencing applications could include
whole genome sequencing, whole exome sequencing, and more targeted sequencing such as gene panels. This
genomic DNA is intended to be analyzed in the same way as any other sample a lab would process and analyze
extracted DNA. Because the RM is extracted DNA, it is not useful for assessing pre-analytical steps such as DNA
extraction, but it does challenge sequencing library preparation, sequencing machines, and the bioinformatics steps of
mapping, alignment, and variant calling. This RM is not intended to assess subsequent bioinformatics steps such as
functional or clinical interpretation.
Information Values: Information values are provided for single nucleotide polymorphisms (SNPs), small insertions
and deletions (indels), and homozygous reference genotypes for approximately 88 % of the genome, using methods
similar to described in reference 1. An information value is considered to be a value that will be of interest and use to
the RM user, but insufficient information is available to assess the uncertainty associated with the value. We describe
and disseminate our best, most confident, estimate of the genotypes using the data and methods currently available.
These data and genomic characterizations will be maintained over time as new data accrue and measurement and
informatics methods become available. The information values are given as a variant call file (vcf) that contains the
high-confidence SNPs and small indels, as well as a tab-delimited “bed” file that describes the regions that are called
high-confidence. Information values cannot be used to establish metrological traceability. The files referenced in this
report are available at the Genome in a Bottle ftp site hosted by the National Center for Biotechnology Information
(NCBI). The Genome in a Bottle ftp site for the high-confidence vcf and high confidence regions is:
HG001
HG002
HG003 HG004
HG006 HG007
HG005
AJ Trio
Chinese
Trio
Pilot Genome
NA12878
4. GIAB “Open Science” Virtuous Cycle
Users
analyze
GIAB
Samples
Benchmark
vs. GIAB
data
Critical
feedback to
GIAB
Integrate
new
methods
New
benchmark
data
Method
development,
optimization, and
demonstration
Part of assay
validation
GIAB/NIST
expands to
more difficult
regions
5. Design of our human genome reference values
Benchmark
Variant
Calls
6. Benchmark
Regions –
regions in which
the benchmark
contains (almost)
all the variants
Benchmark
Variant
Calls
Design of our human genome reference values
8. Variants from
any method
being evaluated
Design of our human genome reference values
Benchmark
Regions
Benchmark
Variant
Calls
9. Benchmark
Regions
Variants
outside
benchmark
regions are
not assessed
Majority of
variants unique
to method should
be false positives
(FPs)
Majority of
variants
unique to
benchmark
should be
false
negatives
(FNs)
Matching
variants
assumed to be
true positives
Variants from
any method
being evaluated
Benchmark
Variant
Calls
Design of our human genome reference values
10. In 2019, GIAB and GA4GH Published
Resources for “Easier” Small Variants
15. Collaborating with FDA to use GIAB
benchmark to inspire new methods
https://precision.fda.gov/challenges/10
16. The best-performing submissions were from new sequencing
technologies and bioinformatics methods
Olson et al, https://doi.org/10.1101/2020.11.13.380
17. Expanding the benchmark was important to demonstrate improved
technologies and analysis methods for difficult genome regions
Olson et al, https://doi.org/10.1101/2020.11.13.380
18. INDELs SNVs
Stratification helps understand strengths of each technology/method
Olson et al, https://doi.org/10.1101/2020.11.13.380
19. Shortcomings in Medical Genes for v4.2.1 benchmark
● Mandelker et al. in 2016
created a list of medical
genes with at least one
exon that is difficult to map
with short reads
● v4.2.1 improved coverage
of these genes but many
are still not fully covered
20. Why Create a Medical Gene Benchmark for Genome in a
Bottle?
● HG002 v4.2.1 benchmark still excludes >10% of 395 medically relevant
genes on chromosomes 1-22 on GRCh37 or GRCh38 due to structural
variants, large segmental duplications, or other difficult regions
● Advances in diploid assembly enabled us to develop phased small
variant and structural variant benchmarks in 273 of these 395 genes on
both GRCh37 and GRCh38 for HG002
Wagner et al, https://doi.org/10.1101/2021.06.07.444885
Justin Wagner
Jason Chin
Fritz Sedlazeck
GIAB CMRG Team
22. Diploid Assembly Using PacBio HiFi reads
● Trio-hifiasm
○ Illumina reads for parents and
PacBio HiFi reads for HG002
○ Best performance in Human
Pangenome Reference Consortium
diploid assembly bakeoff
● Called variants with dipcall
○ Outputs variant calls and confident
regions
○ Confident regions: covered by
exactly one contig from each
haplotype
https://github.com/lh3/dipcall
https://doi.org/10.1038/s41592-020-01056-5
23. New benchmark
includes 273 challenging
genes
● Curated each gene for
accurate resolution by
assembly in IGV
● Manually curated >1000
variant discrepancies and
excluded errors in benchmark
● Most errors in homopolymers
and/or highly homozygous
regions
24. The new CMRG small variant benchmark includes more
challenging variants and identifies more false negatives
26. GRCh37 and GRCh38 contain different false duplications
• GRCh38 has an extra copy of some medically relevant genes
like CBS, KCNE1, and CRYAA, causing mis-mapped reads
26
https://gnomad.broadinstitute.org/gene/ENSG00000160200?dataset=gnomad_r2_1
gnomAD coverage of CBS on GRCh38 decreases for genome sequencing due to mapping ambiguity
gnomAD coverage of CBS on GRCh37 is generally normal for genome (green) and exome (blue) samples
28. T2T identified and fixed additional false duplications
● 12 regions affecting ~1.2 Mbp and 74 genes (including 22 protein coding genes)
● Most medically relevant genes included in 11 pairs of genes in 5 large duplicated
regions on chr21
https://doi.org/10.1101/2021.07.12.452063
29. Genes found to be falsely
duplicated in CMRG and
T2T work
30. T2T also identified collapsed
duplications in GRCh38
● 203 regions affecting ~8 Mbp and 308 genes
(including 48 protein coding genes)
● Includes several medically-relevant genes:
○ KCNJ18/KCNJ12
○ KMT2C
○ MAP2K3
https://doi.org/10.1101/2021.07.12.452063
31. What medical genes do we still not include >90%?
● 110 on GRCh37 and 100 on GRCh38 + all genes on chrX/chrY
Progressively categorizing all 100 on GRCh38:
● 20 affected by gaps in the reference
● 38 had evidence of duplications in HG002 relative to GRCh38
○ Collapsed duplications in GRCh38 (e.g., KCNJ18)
○ Population copy number variability (e.g., LPA, KIR)
● 2 resolved on GRCh38 but not GRCh37
● 18 were >90% included by the dip.bed but had multiple contigs or a break in the
assembly-assembly alignment
● 7 have a large deletion of part or all of the gene on one haplotype
● 4 have breaks or false duplications in the hifiasm assembly (e.g., SMN2)
● 2 are in the structurally variable immunoglobulin locus
● 6 resolved but excluded due to being previously assembled in the MHC
● one (TNNT3) has a structural error in GRCh38
32. Plans for future assembly-based benchmarks
● Long-read assembly-based variants are reaching/surpassing the accuracy of
our benchmarks (with some exceptions)
● Use T2T-HPRC’s assembly of HG002 chrX (and chrY?) to develop small
variant and structural variant benchmark for genic and non-genic regions
● Use diploid assemblies of children in trios
33. Exploring if AI can be used for Genomic Reference Material
Development
● Exploring deep learning to assign
uncertainty to genomic reference
materials
● Exploring transparency for genomics AI
(e.g., "model cards")
● Exploring explainability for AI-based
reference materials
35. 21st Century Cell Lines: Fully Consented and
Characterized Cancer Tumor/Normal Cell Lines as
Reference Materials
● Developing matched tumor/normal cell lines
pairs and donor normal tissue analyzed at early
passages
○ Initial collaboration with Andrew Liss at MGH for
pancreatic ductal adenocarcinoma (PDAC) cell
lines
● Broadly consented for public release of
genomic data and commercial use and
redistribution
● Path to Cancer Genome in a Bottle
Seeking
collaborations
for additional
broadly-
consented
tumor/normal
cell lines
36. Take-home messages
● Ongoing improvement of benchmarks has been needed to
drive technology and bioinformatics innovations
● Assembly methods have advanced rapidly and are
enabling characterization of increasingly challenging
genome regions
● More work is needed to develop better benchmarks and
benchmarking tools, particularly for tumor genomes
37. Acknowledgment of many GIAB contributors
Government
Clinical Laboratories Academic Laboratories
Bioinformatics developers
NGS technology developers
Reference samples
* Funders
*
*
38. Interesting in getting involved?
www.genomeinabottle.org - sign up for general GIAB and Analysis Team google groups
GIAB slides: www.slideshare.net/genomeinabottle
Public, Unembargoed Data:
• http://www.nature.com/articles/sdata201625
• ftp://ftp-trace.ncbi.nlm.nih.gov/giab/
• github.com/genome-in-a-bottle
Global Alliance Benchmarking Team
• https://github.com/ga4gh/benchmarking-tools
• Web-based implementation at precision.fda.gov
• Best Practices at https://rdcu.be/bqpDT
GIAB Analysis Team Calls
• Sign up for the google group to attend biweekly calls Justin Zook: jzook@nist.gov
We are hiring!
Machine learning,
diploid assembly,
cancer genomes,
data science,
other ‘omics, …