Introduction to 16S rRNA gene multivariate analysisJosh Neufeld
Short introductory talk on multivariate statistics for 16S rRNA gene analysis given at the 2nd Soil Metagenomics conference in Braunschweig Germany, December 2013. A previous talk had discussed quality filtering, chimera detection, and clustering algorithms.
Utilization of NGS to Identify Clinically-Relevant Mutations in cfDNA: Meet t...QIAGEN
Pancreatic cancer is a uniquely lethal malignancy characterized by frequent mutations in KRAS, CDKN2A, SMAD4, TP53 and many others. We have shown that KRAS mutation can be detected in cell-free, circulating tumor DNA (ctDNA) isolated from the plasma in a subset of patients and is associated with poor prognosis. The ability to simultaneously detect multiple pancreatic cancer-specific mutations in ctDNA would open a new avenue for detection of clinically-relevant mutations. In this study, we performed ultra-deep sequencing of ctDNA from advanced pancreatic cancer patients prior to treatment with Gemcitabine and Erlotinib following target enrichment. Somatic, non-synonymous variants were identified in 29 different genes at allele frequencies typically less than 0.5%. Updated results of ultra-deep NGS analysis will be presented.
NGS in Clinical Research: Meet the NGS Experts Series Part 1QIAGEN
Next generation sequencing has revolutionized clinical testing but has also created novel challenges. This presentation will give an overview of state of the art clinical NGS and discuss validation, clinical implementation as well as the migration from gene panels to exome sequencing for inherited disorders with clinical and genetic heterogeneity. In addition, important shortcomings such as difficulties with regions of high sequence homology will be discussed.
A brief introduction to amplicon sequencing of the 16S rRNA gene for the analysis of microbial diversity. This talk was presented originally at the Workshop: Introduction to Systems Biology, Aalborg Denmark. 2013-10-29
A future that integrates LLMs and LAMs (Symposium)Tae Young Lee
Presentation material from the IT graduate school joint event
- Korea University Graduate School of Computer Information and Communication
- Sogang University Graduate School of Information and Communication
- Sungkyunkwan University Graduate School of Information and Communication
- Yonsei University Graduate School of Engineering
- Hanyang University Graduate School of Artificial Intelligence Convergence
Introduction to 16S rRNA gene multivariate analysisJosh Neufeld
Short introductory talk on multivariate statistics for 16S rRNA gene analysis given at the 2nd Soil Metagenomics conference in Braunschweig Germany, December 2013. A previous talk had discussed quality filtering, chimera detection, and clustering algorithms.
Utilization of NGS to Identify Clinically-Relevant Mutations in cfDNA: Meet t...QIAGEN
Pancreatic cancer is a uniquely lethal malignancy characterized by frequent mutations in KRAS, CDKN2A, SMAD4, TP53 and many others. We have shown that KRAS mutation can be detected in cell-free, circulating tumor DNA (ctDNA) isolated from the plasma in a subset of patients and is associated with poor prognosis. The ability to simultaneously detect multiple pancreatic cancer-specific mutations in ctDNA would open a new avenue for detection of clinically-relevant mutations. In this study, we performed ultra-deep sequencing of ctDNA from advanced pancreatic cancer patients prior to treatment with Gemcitabine and Erlotinib following target enrichment. Somatic, non-synonymous variants were identified in 29 different genes at allele frequencies typically less than 0.5%. Updated results of ultra-deep NGS analysis will be presented.
NGS in Clinical Research: Meet the NGS Experts Series Part 1QIAGEN
Next generation sequencing has revolutionized clinical testing but has also created novel challenges. This presentation will give an overview of state of the art clinical NGS and discuss validation, clinical implementation as well as the migration from gene panels to exome sequencing for inherited disorders with clinical and genetic heterogeneity. In addition, important shortcomings such as difficulties with regions of high sequence homology will be discussed.
A brief introduction to amplicon sequencing of the 16S rRNA gene for the analysis of microbial diversity. This talk was presented originally at the Workshop: Introduction to Systems Biology, Aalborg Denmark. 2013-10-29
A future that integrates LLMs and LAMs (Symposium)Tae Young Lee
Presentation material from the IT graduate school joint event
- Korea University Graduate School of Computer Information and Communication
- Sogang University Graduate School of Information and Communication
- Sungkyunkwan University Graduate School of Information and Communication
- Yonsei University Graduate School of Engineering
- Hanyang University Graduate School of Artificial Intelligence Convergence
BITS - Comparative genomics on the genome levelBITS
This is the third presentation of the BITS training on 'Comparative genomics'.
It reviews the basic concepts of sequence homology on the gene
Thanks to Klaas Vandepoele of the PSB department.
Metagenomics is the study of genetic material recovered directly from environmental samples. Metagenomics is a molecular tool used to analyse DNA acquired from environmental samples, in order to study the community of microorganisms present, without the necessity of obtaining pure cultures.
In this webinar, we will be covering what exactly an SaMDs, or Software as a Medical Device, and go over some examples with Artificial Intelligence. We will also look at Artificial Intelligence and Machine Learning versus the traditional software. Next, we will go into the regulatory framework for these types of software, then explain how EMMA International can help you get your SaMD to market.
P4 Medicine: A Vision For Your Molecular HealthSachin Rawat
Medicine is undergoing tremendous change. Unlike today, medicine of tomorrow would be pro-active rather than reactive.Medicine would be personalized to individual patient's genome. It would predict, and hence prevent, diseases even before they manifest. Also, this medicine would require active societal participation to bring it from labs to clinics.
Course: Bioinformatics for Biologiacl Researchers (2014).
Session: 3.1- Introduction to Metagenomics. Applications, Approaches and Tools.
Statistics and Bioinformatisc Unit (UEB) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Analysis Analysis Analysis Analysisof the entire entire entire protein protein proteinproteincomplementcomplement complement complement of acell, cell, tissue, tissue, tissue, or organism organism organism under under aspecific, specific, specific, defined defined set of conditions conditions conditions .
• Relies Relies Relies on 3basic technological technological technological technological technological cornerstones cornerstones cornerstones cornerstones
• MethodMethod MethodMethod to fractionatefractionate fractionatefractionate fractionatefractionate complexcomplex complex protein/protein/ protein/ protein/ peptide peptide peptidemixturesmixtures mixtures
• MS to acquire acquire the data data necessary necessary to identify identify identifyidentifyindividual individual individual individualproteins proteins
• Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformaticsto analyze analyze and assemble assemble the MS data
A Distributed Deep Learning Approach for the Mitosis Detection from Big Medic...Databricks
The strongest indicator of a cancer patient's prognosis is the number of mitotic bodies that a pathologist manually counts from the high-resolution whole-slide histopathology images. Obviously, it is not efficient to manually count the mitosis number. But it is still challenging to automate the process of mitosis detection due to the limited training datasets and the intensive computing involved in the model training and inference. This presentation introduces a large-scale deep learning approach to train a two-stage CNN-based model with high accuracy to detect the mitosis locations directly from the high-resolution whole-slide images. In details, we first train a nuclei detection model to remove the background information from the raw whole-slide histopathology images. Second, a customized ResNet-50 model is trained on the cleaned dataset in the first step. The first step saves the training time while improving the model performance in the second step. A false-positive oversampling approach is used to further improve the model performance. With these models, the inference process is conducted to detect the mitosis locations from the large volume of histopathology images in parallel. Meanwhile, the whole pipeline, including data preprocessing, model training, hyperparameter tuning, and inference, is parallelized by utilizing the distributed TensorFlow, Apache Spark, and HDFS. The experiences and techniques in this project can be applied to other large scale deep learning problems as well.
Speaker: Fei Hu
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Single nucleotide polymorphism by kk sahuKAUSHAL SAHU
Single nucleotide polymorphism or SNP (pronounced “snips”), are the most common type of genetic variation among peoples.
Each SNP represents a difference in a single DNA building block, called a nucleotide
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
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.
FDA NGS and Big Data Conference September 2014Warren Kibbe
Presentation for the FDA NGS and Big Data Conference September 2014 held on the NIH campus. NCI initiatives, including Cancer Genomics Data Commons, NCI Cloud Pilots, big data issues for cancer
BITS - Comparative genomics on the genome levelBITS
This is the third presentation of the BITS training on 'Comparative genomics'.
It reviews the basic concepts of sequence homology on the gene
Thanks to Klaas Vandepoele of the PSB department.
Metagenomics is the study of genetic material recovered directly from environmental samples. Metagenomics is a molecular tool used to analyse DNA acquired from environmental samples, in order to study the community of microorganisms present, without the necessity of obtaining pure cultures.
In this webinar, we will be covering what exactly an SaMDs, or Software as a Medical Device, and go over some examples with Artificial Intelligence. We will also look at Artificial Intelligence and Machine Learning versus the traditional software. Next, we will go into the regulatory framework for these types of software, then explain how EMMA International can help you get your SaMD to market.
P4 Medicine: A Vision For Your Molecular HealthSachin Rawat
Medicine is undergoing tremendous change. Unlike today, medicine of tomorrow would be pro-active rather than reactive.Medicine would be personalized to individual patient's genome. It would predict, and hence prevent, diseases even before they manifest. Also, this medicine would require active societal participation to bring it from labs to clinics.
Course: Bioinformatics for Biologiacl Researchers (2014).
Session: 3.1- Introduction to Metagenomics. Applications, Approaches and Tools.
Statistics and Bioinformatisc Unit (UEB) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Analysis Analysis Analysis Analysisof the entire entire entire protein protein proteinproteincomplementcomplement complement complement of acell, cell, tissue, tissue, tissue, or organism organism organism under under aspecific, specific, specific, defined defined set of conditions conditions conditions .
• Relies Relies Relies on 3basic technological technological technological technological technological cornerstones cornerstones cornerstones cornerstones
• MethodMethod MethodMethod to fractionatefractionate fractionatefractionate fractionatefractionate complexcomplex complex protein/protein/ protein/ protein/ peptide peptide peptidemixturesmixtures mixtures
• MS to acquire acquire the data data necessary necessary to identify identify identifyidentifyindividual individual individual individualproteins proteins
• Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformaticsto analyze analyze and assemble assemble the MS data
A Distributed Deep Learning Approach for the Mitosis Detection from Big Medic...Databricks
The strongest indicator of a cancer patient's prognosis is the number of mitotic bodies that a pathologist manually counts from the high-resolution whole-slide histopathology images. Obviously, it is not efficient to manually count the mitosis number. But it is still challenging to automate the process of mitosis detection due to the limited training datasets and the intensive computing involved in the model training and inference. This presentation introduces a large-scale deep learning approach to train a two-stage CNN-based model with high accuracy to detect the mitosis locations directly from the high-resolution whole-slide images. In details, we first train a nuclei detection model to remove the background information from the raw whole-slide histopathology images. Second, a customized ResNet-50 model is trained on the cleaned dataset in the first step. The first step saves the training time while improving the model performance in the second step. A false-positive oversampling approach is used to further improve the model performance. With these models, the inference process is conducted to detect the mitosis locations from the large volume of histopathology images in parallel. Meanwhile, the whole pipeline, including data preprocessing, model training, hyperparameter tuning, and inference, is parallelized by utilizing the distributed TensorFlow, Apache Spark, and HDFS. The experiences and techniques in this project can be applied to other large scale deep learning problems as well.
Speaker: Fei Hu
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Single nucleotide polymorphism by kk sahuKAUSHAL SAHU
Single nucleotide polymorphism or SNP (pronounced “snips”), are the most common type of genetic variation among peoples.
Each SNP represents a difference in a single DNA building block, called a nucleotide
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
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.
FDA NGS and Big Data Conference September 2014Warren Kibbe
Presentation for the FDA NGS and Big Data Conference September 2014 held on the NIH campus. NCI initiatives, including Cancer Genomics Data Commons, NCI Cloud Pilots, big data issues for cancer
우리 몸은 체중, 체온, 혈압, 혈당, 전해질, 체액양 등을 일정하게 유지하는 항상성 조절 기능이 있다. 특히 특정 체중 조절점 (body weight set point)으로 체중을 일정하게 유지하는 기능은 비만의 병인에도 중요한 역할을 하고, 비만 치료에 있어서 문제가 되는 요요현상에서도 중요한 역할을 한다. 체중 조절점은 어떤 생물학적 기전으로 이루어지며, 잘 변하지 않는 체중 조절점이 어떤 기전으로 가변적이 될 수 있는지 알아보고자 한다. 또한, 이런 기전을 실제 비만클리닉에서 어떻게 적용할 수 있을지 살펴보고자 한다.
An overview of the current regulatory environment. *This is not legal advice and is written by a non-attorney. This is general information from a student in law school (MSJ program).
This is Part 1 of a presentation on Genetic Toxicology that was given by Dr. David Kirkland to scientific staff at Health Canada in Sept. 2010. Part 2 is availabile in ppt
DNA Roulette: Understanding genetics and genetic testing through gamingcarrie.heeter
DNA Roulette was developed by Michigan State University professor Carrie Heeter and Stanford geneticist Barry Starr to help players understand what Direct to Consumer (DTC) testing does and does not tell you about your health. This presentation at Games for Health 2012 in Boston introduces the game.
Regulatory oversight of genetic testing in Canada: Health Canada perspectiveMaRS Discovery District
Speaker: Patrice Sarrazin, PhD, Senior Scientific Evaluator, In Vitro Diagnostic Devices, Medical Devices Bureau, Therapeutic Product Directorate, Health Canada. Patrice discusses Health Canada's perspective on genetic testing as well as policy and regulation in Canada.
Part of Dx2010, a workshop at MaRS focused on best practices and regulatory considerations for developing gene-based diagnostic and prognostic tests.
Direct To Consumer Genomics and the Future of HealthcareRyan Squire
Richard Sharp, Ph.D., Director of Bioethics Research at the Cleveland Clinic presents on direct-to-consumer genomics and the future of health care.
Dr. Sharp received his training in philosophy and medical ethics at Michigan State University.
Prior to joining the Cleveland Clinic in 2007, Dr. Sharp taught bioethics at Baylor College of Medicine and the National Institute of Environmental Health Sciences, one of the National Institutes of Health (NIH), where he directed the Program in Environmental Health Policy and Ethics.
His research examines the promotion of informed patient decision-making in clinical research, particularly research that involves genetic analyses.
Medical Utopias: The Promise of Emerging TechnologiesAlex Tang
Medical utopias are often about good health, absence of suffering, and even delaying of the aging process. The last two decades have seen a tremendous increase in emerging medical technologies to achieve these utopias. The completion of the sequencing of the human genome sets the stage for the next step of genetic and molecular advances. The increase in computing power, storage capacity, connectivity, and the Internet has opened avenues of new diagnostic and therapeutic modalities. The perfecting of sustaining cell growth in vitro and cell nucleus transfer has opened the way to cloning, stem cell harvesting, and a new field of regenerative medicine. However, these emerging technologies bring with them a large number of bioethical concerns that need to be addressed. These concerns involving tissue engineering, bioelectronics, new genetics, cloning, gene therapy, germ-line genome modifications are only the tip of the iceberg. In this paper I will reflect on three areas of concern. Firstly, the emergence of the digital patient will be considered. This digital patient will be deeply formed and informed by health information technology (IT), the social media, and issues involving privacy, confidentiality and data security. Secondly, the direct to customers (DTC) genetic screening tests will be discussed. The ethical issue of buccal swabs taken at home and be tested for genetic diseases and future prediction of other illnesses which is marketed directly to the consumers will be examined. Finally, the development of new pharmaco-therapeutics will be explored. There have been changes in the way new drugs are tested and these changes do raise some ethical concerns. The examination of these ethical issues will be done in the framework of respect for autonomy, beneficence, non-maleficence, and justice.
A look at future directions for biology. Personalized genomics is a key step in moving towards individualized medicine and preventative interventions. The traditional trial and error approach of molecular biology is being replaced by the direct design of synthetic biology. Synthetically developed energy solutions could have a substantial impact on natural resource demand.
You're driving down the highway, when suddenly you spot a police officer or a state trooper in your rearview mirror. You're driving the speed limit; your tags are up-to-date, yet you suddenly feel guilty – like you've done something wrong.
This is how the FDA makes us feel when it comes to running direct-to-consumer (DTC) advertising for medical devices and pharmaceuticals. More often than not, you aren't doing anything wrong. But your anxiety levels run high as soon as you step on the gas.
Doctors’ Views of Direct-to-Consumer Drug AdvertisingCMI_Compas
CMI/Compas Study on Doctors’ Views of Direct-to-Consumer Drug Advertising, April 2013, In Preparation for the FDA Survey of Clinicians on Direct-to-Consumer Drug Advertising
These slides use concepts from my (Jeff Funk) course entitled Biz Models for Hi-Tech Products to analyze the business model for 23andMe’s personal genomics service. 23andMe provides personal health care analysis and ancestry information to individuals that are based on a partial analysis of an individual’s DNA data. Driven by the falling cost of DNA sequencing, 23 and Me provides this information for $99 and a small amount of saliva. While this business model may succeed particularly if the FDA eventually approves the health care portion of the service, we recommend that 23andMe develop a new business model. It should offer the service for free to individuals, particularly those who frequent gyms and nutrition stores and sell information about potential athletes to sports teams.
Biomedical big data and research clinical application for obesityHyung Jin Choi
1. What is Biomedical Big Data?
2. Biomedical Big Data
1) Genetic Data
2) Electrical Health Records
3) National Healthcare Data
4) Medical Images
5) Sensor/Mobile Data
6) Data Integration
3. Biomedical Big Data + Artificial Intelligence
4. Research/Clinical Application for Obesity
"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human ...Hyper Wellbeing
"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human Logevity, Inc.)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
- 의료 빅데이터 - 개념 입문과 임상의사가 할 수 있는 일
빅데이터 개념 입문과
의료와 관계된 빅데이터 종류와 활용 방법
진료와 연구에 활용할 수 있는 방법을
임상의사의 관점에서 다루었습니다.
1. What is Big Data?
2. Healthcare Big Data
1) Electrical Health Records (EHR) Structured/Unstructured Data
2) Medical Images
3) National Healthcare Data
4) Behavior/Sensor Data
5) Genetic Data
3. Clinical and Research Applications
Presenter: Marina Sirota, UCSF
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.
SILS 2015 - Connecting Precision Medicine to Precision Wellness Sherbrooke Innopole
By: Joel Dudley, Mount Sinai School of Medicine
At Sherbrooke International Life Sciences Summit - 2nd edition | September 28/29/30 2015
www.sils-sherbrooke.com
The Foundation of P4 Medicine Keynote Presentation as presented by Leroy Hood, M.D., PhD, at the Ohio State University Personalized Health Care National Conference 2010.
1. What is Stress?
2. Mechanism
Neuro-Anatomy of Stress
Neuro-Endocrine of Stress
Inflammation and Stress
3. Stress and Disease
Stress and Food Addiction
4. Stress and Central Regulation of Metabolism
The Present and Future of Personal Health Record and Artificial Intelligence ...Hyung Jin Choi
1. Why Personal Health Record and Artificial Intelligence ?
2. Obesity Example
3. Personal Health Record
1) Genetic Data
2) Electrical Health Records
3) National Healthcare Data
4) Medical Images
5) Sensor/Mobile Data
6) Data Integration
4. PHR+AI Applications
Obesity PHR Big Data - Research and Clinical ApplicationsHyung Jin Choi
1. Obesity
2. Obesity Big Data
1) Genetics
2) Environment
3) Electrical Health Record
4) Imaging
5) Mobile/Sensor
6) National Health Record
7) Social Network Service
3. Research
4. Clinical Application
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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.
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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.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
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TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
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ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
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
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
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
2. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
3. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
10. 며칠 전 유전자 검사를 받은 40대 남성입니다.
혈액세포의 DNA 상태를 분석해 앞으로 암에 걸릴 위험이 있는지 여부를 판단할 수 있다
고 합니다.
2013.4.25. KBS 9시 뉴스
11. 2013.4.25. KBS 9시 뉴스
60년전 DNA의 구조가 밝혀진 이래 2003년 인간 유전자 지도가 완성됐고, 현재는 어떤 유
전자가 어떤 질병을 일으키는지 분석도 80% 정도 끝난 상태입니다.
예를들어 13번 염색체의 BRCA2 유전자에 이상이 생기면 유방암에 걸릴 확률이 높습니
다. 또 17번 염색체 유전자는 난소암, 7번 염색체 유전자는 비만을 일으킵니다.
15. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
17. Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(1926 publications and 13410 SNPs)
Disease-related variations
Functional elements
2014-06-29
17
18. All Major Tissues/Organs
All Proteins + All RNAs
2015 Science Tissue-based map of the human proteome
1. Immunohistochemistry (IHC)
24,028 antibodies
(16,975 proteins)
>13 million IHC images
2. RNA-sequencing
(N=44)
18
19. 111 Reference Human Epigenomes
2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes19
29. 1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study
32. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
35. Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted
Cancer Treatment
(EGFR)
Causal
Variant
Targeted Drug
(MODY-SU)
Drug Efficacy/Side Effect
Related Genotype
(CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular
Classification
- Prognosis
(Leukemia)
Hereditary
Cancer
(BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction
(Complex disease,
Diabetes)
Germline Variants
Fetal DNA
36. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
41. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
46. Tissue Specific Gene Expression
2015 Nature Genetic studies of body mass index yield new insights for obesity biology
- Hypothalamus, Pituitary gland
(appetite regulation)
- Hippocampus, Limbic system
(learning, cognition, emotion,
memory)
47. Gene Set Enrichment Analysis
2015 Nature Genetic studies of body mass index yield new insights for obesity biology
48. Per-allele effect of BMI-associated
loci on body weight
2012 Genetic determinants of common obesity and their value in prediction
49. 2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic
50. 2015 Nature Genetic studies of
body mass index yield new
insights for obesity biology
Blue: Previous
Red: Novel
51. 2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic
TCF7L2
52. ◇◆ ‘parental obesity’
as a test to predict obesity
in adult life
•Dark blue 1–2 yrs
•Green 3–5 yrs
•Red 6–9 yrs
•Light blue 10–14 yrs
•Grey , 15–17 yrs
Genetic Prediction of Obesity Risk
The predictive ability of
the currently
established BMI-
associated loci is poor
2012 Genetic determinants of common obesity and their value in prediction
53. Influence of Genetics on Human Disease
For any condition the overall balance of g
enetic and environmental determinants ca
n be represented by a point somewhere w
ithin the triangle.
53
Single
Locus /
Mendelian
Multiple
Loci or multi-
chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet
Carcinogens
Infections
Stress
Radiation
Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer
57. Diabetes ≠ Genetic Disease?
• Familial aggregation
– Genetic influences?
– Epigenetic influences
• Intrauterine environment
– Shared family environment?
• Socioeconomic status
• Dietary preferences
• Food availability
• Gut microbiome content
• Overestimated heritability
– Phantom heritability
2012. Drong AW, Lindgren CM, McCarthy MI. Clin Pharmacol Ther. The genetic and epigenetic basis of type 2 diabetes and obesity.
2012. PNAS The mystery of missing heritability- Genetic interactions create phantom heritability57
58. Rare Variant in a Specific Population
• 3756 Latino: whole exome sequencing
A rare functional variant in candidate gene
14276: replication Not found in other ethnic group
2014 JAMA Association of a Low-Frequency Variant in HNF1A With Type 2 Diabetes in a Latino Population
59. 2014 NEJM Null Mutation in Hormone-Sensitive Lipase Gene and Risk of Type 2 Diabetes
Rare Functional Variant
= Monogenic Heritable Disease
All Amish
60. Variants and Disease Susceptibility
2008 NRG Genome-wide association studies for complex traits- consensus, uncertainty and challenges
61.
62. Genotype Based Diabetes Therapy
Diabetes due to KATP channel mutations sulphonylurea
2007 American Journal of Physiology - Endocrinology and Metabolism. ATP-sensitive K+ channels and disease- from molecule to malady
67. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
68. 2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec
68
69. 69
Pharmacogenomic Biomarkers
in Drug Labeling (N=166)
2015.9.14.
Atorvastatin, Azathioprine,
Carbamazepine, Carvediolol,
Clopidogrel, Codein,
Diazepam…..
70. Large Effect Size Variant?
Disease susceptibility variant Pharmacogenetic variant
Environmental
Exposure
Drug
Exposure
83. Personalized Medicine
Pharmacogenomics
Nutrigenomics
IRS1 SNP GA/AA
High fat/
Low carb
IRS1 SNP GG
Standard
Higher
effect
Similar
effect
2013 Diabetes Care. IRS1 Genotype Modulates Metabolic Syndrome Reversion in Response to 2-Year Weight-Loss Diet Intervention - The POUNDS LOST trial
86. Genotype Guided Personalized Treatment
Baseline
Genotyping
- Drug metabolism
- DM etiology
- DM complication
1 week 3 month Long term
Genotype based treatment strategy
- Drug choice
- Drug dose
- Lifestyle modification
- Complication evaluation
New
T2DM
87. Pharmacogenetic Tests: 최형진
No
Drug
(N= 10)
Gene
(6 genes=8 bioma
rkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation Clinical Interpretation
1 Clopidogrel CYP2C19
rs4244285 (G>A) GG
*1/*1
(EM)
Use standard dosers4986893 (G>A) GG
rs12248560 (C>T) CC
2 Warfarin
VKORC1 rs9923231 (C>T) TT
Low dose
(higher risk of bleeding)
Warfarin dose=0.5~2 mg/day
CYP2C9
rs1799853 (C>T) CC
rs1057910 (A>C) AC
3 Simvastatin SLCO1B1 rs4149056 (T>C) TT Normal
4
Azathioprine (AP),
MP, or TG
TPMT rs1142345 (A>G) AA Normal
5
Carbamazepine
or Phenytoin
HLA-B*1502
rs2844682 (C>T) CT
Normal
rs3909184 (C>G) CC
6 Abacavir HLA-B*5701 rs2395029 (T>G) TT Normal
7 Allopurinol HLA-B*5801 rs9263726 (G>A) GG Normal
Clopidogrel1)
: UM/EM=standard dose, IM/PM= consider alternative antiplatelet agent (eg. prasugrel/ticagrelor)
Warfarin2)
: high dose=5~7 mg/day, medium dose=3~4 mg/day, low dose=0.5~2 mg/day
=0
최형진
+1,000,000
?
88. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
96. Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence
97. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
121. 121 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits
November 3, 2015
122. 122 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits
123. Personalized Nutrition by
Prediction of Glycemic Responses
123
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
Received: October 5, 2015;
Received in revised form:
October 29, 2015;
Accepted: October 30, 2015;
127. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
128. Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
128
132. 밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의 저혈
당 에 저혈당 이 있을 즉알려주도록 밤사이 특
이호소 수면유지상처와 통증 상처부위 출혈
oozing, severe pain 알리도록 고혈당 처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위
oozing Rt.foot rolling keep드레싱 상태를 고혈
당 고혈당 의식변화 BST 387 checked.고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 발관리 방법에 .
목표 혈당, 목표 당화혈색소에 .식사를 거르거
나 지연하지 않도록 .식사요법, 운동요법, 약물
요법을 정확히 지키는 것이 중요을 .처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .혈당 정상 범위임rt foot rolling
중으로 pain호소 밤사이 수면양호걱정신경 예
민감정변화 중임감정을 표현하도록 지지하고
경청기분상태 condition 조금 나은 듯 하다고 혈
당 조절과 관련하여 신경쓰는 모습 보이며 혈당
self로 측정하는 모습 보임혈당 조절에 안내하
고 불편감 지속알리도록고혈당 고혈당 의식변
화 고혈당 허약감 지남력 혈당조절 안됨고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안 저
혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현
기증, 공복감, 두통, 피로감등의 저혈당 에 저
혈당 이 있을 즉알려주도록 pain 및 불편감 호
소 WA 잘고혈당 고혈당 의식변화 고혈당 허
약감 지남력 혈당조절 안됨식사요법, 운동요법,
약물요법을 정확히 지키는 것이 중요을 .저혈당
/고혈당 과 대처법에 .혈당정상화, 표준체중의
유지, 정상 혈중지질의 유지에 .고혈당 ,,관리
방법 .혈당측정법,인슐린 자가 투여법, 경구투
약,수분 섭취량,대체 탄수화물,의료진의 도움이
필요한 사항에 교혈당 정상 범위임수술부위
oozing Rt.foot rolling keep수술 부위 (출혈, 통
증, 부종)수술부위 출혈 상처부위 oozing
Wound 당겨지지 않도록 적절한 체위 취하기
설명감염 발생 위험 요인 수술부위 출혈 밤동안
간호기록지 Word Cloud
Natural Language Processing (NLP)132
133. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
138. Korean Society for
Bone and Mineral
Research
Anti-hypertensive
prescriptions
(2008-2011)
N = 8,315,709
New users
N = 2,357,908
Age ≥ 50 yrs
Monotherapy
Compliant user (MPR≥80%)
No previous fracture
N = 528,522
Prevalent users
N = 5,957,801
Excluded
Age <50
Combination therapy
Inadequate compliance
Previous fracture
N = 1,829,386
Final study population
심평원 빅데이터 연구
고혈압약과 골절
Choi et al., 2015 International Journal of Cardiology138
139. Compare Fracture Risk
Comparator?Hypertension
CCB
High
Blood Pressure
Fracture
Risk
BB
Non-
user
Healthy
Non-
user
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
2007 20112008
Choi et al., 2015 International Journal of Cardiology139
141. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
147. 147
2013 Science Functional interactions as big data in the human brain
2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns
149. Quantitative nuclear morphometry
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis
of whole slide images
150. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence
157. 157
In a scan of 3,000 images, IBM
technology was able to spot
melanoma with an accuracy
of about 95 percent, much
better than the 75 percent to
84 percent average of today's
largely manual methods.
IBM Research will continue to
work with Sloan Kettering to
develop additional
measurements and
approaches to further refine
diagnosis, as well as refine
their approach through larger
sets of data.
Dec 17, 2014
158. 158
Aug. 11, 2015
IBM is betting that the same technology that
recognizes cats can identify tumors and other signs of
diseases.
In the long run, IBM and others in the field hope such
systems can become reliable advisers to
radiologists, dermatologists and other practitioners
who analyze images—especially in parts of the world
where health-care providers are scarce.
While IBM hopes Watson will learn to interpret
Merge’s images, it also expects the combination of
imagery, medical records and other data to reveal
patterns relevant to diagnosis and treatment that a
human physician may miss, ushering in an era of
computer-assisted care. Two other recent IBM
acquisitions, Phytel Inc. and Explorys Inc., yielded 50
million electronic medical records.
Current results are not sufficient to isolate specific brain regions important
in regulating BMI. However, we observe enrichment not only in
the hypothalamus and pituitary gland—key sites of central appetite
regulation—but even more strongly in the hippocampus and limbic system,
tissues that have a role in learning, cognition, emotion and memory.
The strongest enrichment was observed with promoter (histone 3
Lys 4 trimethylation (H3K4me3), histone 3 Lys 9 acetylation (H3K9ac))
and enhancer (H3K4me1, HeK27ac) marks detected in mid-frontal
lobe, anterior caudate, astrocytes and substantia nigra, supporting neuronal
tissues in BMI regulation.
synaptic function, long-term potentiation and neurotransmitter
signalling (glutamate signalling in particular, but also noradrenaline,
dopamine and serotonin release cycles, and GABA (c-aminobutyric acid)
receptor activity
"mid-frontal lobe, anterior caudate, astrocytes and substantia nigra, supporting neuronal tissues in BMI regulation"
"provides the strongest genetic evidence so far for a role of particular
biological and CNS processes in the regulation of human body mass."
Extreme Phenotype Group in Amish (high TG, N=24)
Sequencing- Candidate Gene Approach (lipid pathway 24 genes)
One subject – deletion detected
Genotyping in 2738 Amish
140 heterozygoe, 1 homozygote
5.1% of Amish persons carry the D allele, as compared with 0.2% of non-Amish persons of European descent