World DNA Day and Genome Day, Dalian China 2011
"Possible Solution for Managing the Worlds Genetic Data" given by Alice Rathjen, Founder & President DNA Guide, Inc.
Proposes genetic tests be given a rating for quality of science, medical utility and viewing risk so as to facilitate the flow of genetic information in a responsible manner from the lab to the physician and patient. Explains how technology combined with public policy could enable both privacy and personalized medicine to thrive. Advocates individual ownership over personal genetic data and suggests the genome as a data format could provide the foundation for digital human rights.
tags: DNA, genetic testing, privacy, personalized medicine, FDA regulation
Healthcare Conference 2013 : Genes, Clouds and Cancer - dr. Andrew LittD3 Consutling
Dell Healthcare provides IT services to healthcare organizations worldwide. They serve over 50% of US hospitals, the top 10 pharmaceutical companies, and 100 insurance organizations. Dell Healthcare manages billions of medical images in the cloud, billions of security events daily, and provides genomic sequencing services. They are sponsoring the first FDA-approved clinical trial using whole genome sequencing to provide personalized cancer treatment to children with neuroblastoma. The trial aims to reduce analysis time from weeks to hours using Dell's high performance computing capabilities and improve collaboration using their genomics cloud. The goal is to expand personalized medicine from treating a few children to hundreds and thousands.
The document discusses how IBM's Watson technology can be applied to healthcare to improve clinical decision making and reduce diagnostic errors. It describes Watson's ability to analyze large amounts of structured and unstructured data, generate differential diagnoses, consider various hypotheses, and provide evidence and a confidence level for its responses without making a definitive diagnosis. The document also outlines how electronic health records could be enhanced with Watson to better record assessments, generate checklists to aid decision making, and provide relevant knowledge resources to clinicians.
This document discusses IBM Watson and its potential applications in healthcare in German-speaking countries. It provides an overview of Watson's capabilities, including its ability to understand natural language, generate and evaluate hypotheses, and adapt and learn from interactions. The document also discusses how Watson has been applied to healthcare through projects like creating knowledge bases for cancer care and working with IBM Content Analytics to extract attributes from medical texts. Overall, the document presents Watson as a system that can help address the challenges of analyzing growing amounts of unstructured medical data through its advanced natural language processing and machine learning abilities.
This document discusses the ethical issues raised by pervasive health data sharing from various sources like genetic services, fitness trackers, and online surveys. It notes that health care and medical research could suffer if this data is not properly protected. While some argue it is not the health industry's problem, the document argues they should get involved for two key reasons: 1) health data is at risk of reidentification even when de-identified and 2) the industry could take a proactive role in finding and controlling health data. However, this approach raises ethical concerns about autonomy, surveillance, and data breaches. If done right with transparency and limiting data use, industry control could incentivize better protection against exploitation and equalize use of big
Shiva Amiri, CEO, Biosymetrics at The AI Conference 2017MLconf
The document discusses the future of medical data science and precision medicine. It outlines both the promise and challenges, including the need for a holistic understanding of patients, improved diagnostics, and better treatment. It also discusses the large market opportunity in machine learning for medicine. The author's company, Biosymetrics, provides an integrated analytics and machine learning platform called Augusta that can process and integrate different types of medical data, like images and genomics data, for applications like predicting autism and Alzheimer's disease.
3 Round Stones at the New England Health Datapalooza Oct 3, 20123 Round Stones
3 Round Stones' co-founder Bernadette Hyland discusses a new mobile application that uses federal open government data about weather and healthcare to improve management of chronic health conditions including asthma and COPD.
Bringing scientists to data to accelerate discoveries and improve human healt...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video:
Learn more about H2O.ai: https://www.h2o.ai/.
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Data sharing is fraught with privacy concerns in the biomedical domain. How do we develop insights if data silos are our reality? Stanford is undertaking a “data commons on steroids” approach with a goal to “free the scientist” and make insight sharing possible across the data silos.
Somalee is a computational physicist by training, a biotechnologist by profession and a data analyst by the way of passion. She believes that with the explosion of data in healthcare and with new methods to analyze such large amounts of data, we will see massive changes in how human diseases are addressed via novel drugs, large-scale genomics, wearable sensors, and software to tie it all together. She wants to drive part of this revolution.
This document discusses machine learning approaches for detecting healthcare fraud, waste, and abuse. It begins by outlining the scope of fraud in the US healthcare system and the large volume of healthcare data available for analysis. It then describes different types of fraud, waste, and abuse and analytical approaches used, including supervised learning models, unsupervised anomaly detection techniques, and generating provider-level features from claims data. Specific challenges in detecting healthcare fraud like imbalanced data and evolving fraud schemes are also discussed.
Richard Dale gave a presentation on personalized medicine at the MassTLC Big Data Summit in June 2013. He began by noting that the term "personalized medicine" is often misunderstood and proposed expanding our understanding of it. He then sketched a market map showing where personalized medicine is currently focused and its potential future trajectory. Dale argued that, like Amazon and Netflix recommendations, healthcare should aim to deliver individualized treatment plans by analyzing thousands of personal factors for each patient, moving from a model focused on diseases to one centered on each unique patient.
1) The document discusses the promise and potential perils of eHealth technologies like remote monitoring devices, virtual assistants, and personalized health records.
2) Factors driving eHealth include patient demand for convenient access to information, the ability to link separate health services, and using technology to address issues like staff shortages.
3) Potential benefits include improved patient information and choice, better communication between providers, and links to vetted external health resources. However, issues around privacy and control of personal data still need solutions.
Personalized Medicine with IBM-Watson: Future of Cancer carejetweedy
Watson for Genomics uses IBM's Watson cognitive computing system to help personalize cancer care. It analyzes genomic sequencing data and clinical records to provide treatment suggestions and clinical trial matches for patients in minutes, compared to weeks for traditional approaches. Researchers are finalizing the algorithm and testing it in clinical trials. Watson draws from a large corpus of medical literature and patient data to understand questions, generate hypotheses, and provide evidence to support its answers. It could help reduce health professionals' workload and improve access to care, though challenges remain in developing the algorithm and acquiring sufficient data sets.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Using Big Data to Personalize the Healthcare Experience in Cancer, Genomics a...DrBonnie360
1. The document discusses how big data is being used to personalize healthcare experiences through genomics, cancer/clinical trials research, and mobile health applications.
2. It provides examples of companies in each area using big data to analyze genomes, personalize cancer treatments, and develop health-focused mobile apps.
3. The main bottlenecks slowing progress are identified as issues of data interoperability, sharing, and privacy concerns across genomics, clinical research, and mobile health.
Miranda K. Hessler is seeking an MRI Technologist position. She is expected to graduate in May 2016 with an Associate's Degree in MRI Technology from Owens Community College. She has clinical experience screening and preparing patients for MRI scans at both the University of Michigan and Promedica Defiance Hospital. Previously, she worked as an X-ray Technician and Medical Assistant, where she checked patients in, obtained vitals, and administered vaccines. She has extensive skills and training in MRI machines, x-rays, nursing assistance, and various medical software.
Big data is impacting the healthcare industry by enhancing efficiency, increasing productivity, and helping anticipate potential issues. The document outlines how big data plays a role in healthcare through benefits like detecting illnesses early, customized treatment, and reducing waste. It also discusses challenges like privacy concerns, fragmented data from different sources, and ensuring data integrity when sharing information.
Genetic analysis is being transformed by decreasing costs and increasing power of sequencing technologies. While sequencing machines can generate data quickly, there is still too much delay in processing that data into meaningful insights. Spiral Genetics offers a solution to provide the computing power needed to process vast amounts of sequencing data from any source into accurate variants and full human genome reads within 3 hours, on demand through their user-friendly software. Their approach has enabled faster treatment decisions and cancer research at reduced costs compared to other options.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Slides from Spectra Logic’s inaugural BlackPearl Developer Summit, a virtual conference for current and potential Spectra Logic developers. You’ll get product updates from our CEO and BlackPearl product manager, and you will learn how these new features will help customers and developers. You will learn how to build a Spectra S3 client for BlackPearl, our private cloud gateway to our tape and disk storage systems. You will see how one of our partners developed a client and watch it in action. And you will get to ask questions to our BlackPearl Engineering team. Watch the Summit recording at https://www.youtube.com/watch?v=GYoSwrvhVM0
It's the end of the world as we know it, and i feel fineMartin Hamilton
Slides from my closing keynote for the Talis Insight Europe 2016 conference. In this talk I cover global warming and other "extinction level events", and how we might go about preserving the core of human knowledge and culture in an offplanet backup. I show how future technologies like interstellar travel and DNA based data storage are complemented by here and now technologies like cubesats/nanosats and the likes of Project Gutenberg and Wikipedia. All of this is a metaphor for how we should "think big", and looking at the changing role of the librarian.
Digital DNA. Digital Is Here. India Online Marketing Trends 2015 ResellerClub
Managing Director and Co-Founder of Octane.in, Punit Modhgil talks to us about the trends the online Indian market is going to see in the coming year & what this means for Indian marketers.
Dna the next big thing in data storageOther Mother
Scientists have discovered that DNA can store large amounts of digital data for thousands of years, making it a promising storage medium. DNA can store 300,000 terabytes in a fraction of an ounce, whereas a hard drive of similar size can only store 5 terabytes and may only last 50 years. A researcher successfully stored and retrieved over 1 terabyte of data encoded in DNA. However, the ability to access specific files within the DNA storage remains a challenge that needs to be addressed.
How new economic forces shape the competitive landscape.
The laws of Moore and Metcalf have changed our world at its core. Deregularization, digitalization, globalization and socialization supersede what we learned at school. This presentation shows why copies can be better than originals, why giving away is sometimes is better than getting paid, why small is often better than big and why companies are no longer in control. You will also see why you should care about all these tendencies of the digital age in order to keep your bottom-line black.
Ion Torrent™ Next Generation Sequencing-Oncomine™ Lung cfDNA assay detected 0...Thermo Fisher Scientific
This document summarizes research using the OncomineTM cfDNA assays and Ion Torrent next-generation sequencing to detect low frequency somatic variants in cell-free DNA from plasma. Key findings include:
1) The assays can detect variants at an allelic frequency of 0.1% with high sensitivity and specificity compared to digital PCR.
2) Variants observed in tumor tissue were also detected in matched plasma samples at lower frequencies.
3) The entire workflow from blood sample to results can be completed in 2 days, supporting use in a clinical laboratory setting.
Digital DNA-seq Technology: Targeted Enrichment for Cancer ResearchQIAGEN
Targeted DNA sequencing has become a powerful approach by achieving high coverage of the region of interest while keeping the cost of sequencing and complexity of data interpretation manageable. However, existing PCR-based target enrichment approaches introduce errors due to PCR amplification bias and artifacts, which significantly affects quantification accuracy and limit the ability to confidently detect low-frequency DNA variants. This webinar introduces a new digital sequencing approach that is based on the use of unique molecular indices (UMIs) - QIAseq Targeted DNA Panels. With UMIs, each unique DNA molecule is barcoded before any amplification takes place to correct for PCR errors. Detailed workflow and applications in cancer research will be presented. Join us and learn about this exciting novel digital DNAseq technology
This document describes DNA cryptography techniques. It begins with an acknowledgement section thanking those who helped with the project. It then provides a declaration confirming the work is original. The introduction discusses using DNA to encode messages for encryption and storage. It describes using one-time pads with DNA substitution or XOR operations. The document outlines building one-time pads on DNA chips for random encryption/decryption of messages and images. It concludes by discussing using DNA steganography to hide messages within other DNA strands.
DNA has potential as a long-term data storage medium due to its stability, density, and redundancy. It can store 700 terabytes of data in 1 gram, which is equivalent to 3 million CDs and weighs 151 kilos if stored as hard drives. While DNA sequencing and synthesis speeds are currently slow, the cost per megabase of sequencing has dropped tremendously from $10,000 in 2001 to 10 cents in 2012. Researchers have successfully encoded digital files like images, documents and audio clips in DNA, demonstrating its viability for archiving large volumes of data in a small, stable format.
DNA shows promise as a long-term information storage solution. It is stable, durable, and can store vast amounts of data in a small physical space. The document outlines how DNA can be used to store digital files by encoding the information into DNA sequences using a quaternary coding system. As an example, researchers were able to store several works by Shakespeare, scientific papers, images and audio clips in DNA. While the speed and cost of reading and writing to DNA are currently limitations, the technology is improving rapidly and DNA may become a practical large-scale storage solution within the next 5-10 years.
The document presents an overview of DNA computers. DNA computers use DNA molecules as the data storage medium and enzymes as the processing units. Some key advantages of DNA computers include massive data storage capacity using a small physical space, highly parallel processing, and low cost. However, DNA computers also currently have limitations such as high error rates and the need for human assistance in laboratory procedures. Potential applications of DNA computing include DNA chips, genetic programming, and pharmaceutical analysis. While DNA computers show promise, further work is still needed to develop them into a practical product.
Bio computing uses DNA and biochemical processes to store and manipulate data similarly to human biology. DNA can store vast amounts of data densely due to its structure of paired chemical bases. A DNA computer operates massively in parallel and with extraordinary energy efficiency compared to conventional computers. While DNA computing shows potential for medical and data applications, it still requires further development to overcome challenges such as reduced accuracy compared to conventional computing.
Here are the key points about why each step is important in DNA extraction:
- Blending breaks open the cell walls and membranes to release the DNA inside. This physically separates the DNA from other cell contents.
- Salt helps strip away proteins that are attached to the DNA. The positive and negative charges on salt ions disrupt the electrostatic interactions between DNA and proteins.
- Detergent works similarly to salt by disrupting membranes and "poking holes" in them. This allows the contents of the cell to be released. Detergents have molecules with both water-loving and water-fearing parts, allowing them to penetrate and disrupt membranes.
- Enzymes (like meat tenderizer) further break
The document discusses how genomics and blockchain technologies will transform healthcare by making genomic data and insights more accessible and affordable globally. It describes Shivom's vision of creating a genomic data ecosystem where individuals own and can choose to share their genomic data securely via blockchain, and how this could benefit research, precision medicine and personalized healthcare. Key features of the Shivom platform include genome sequencing and storage, a marketplace for healthcare services, and tools to incentivize data sharing and collaboration between individuals, researchers and organizations.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
1) The document discusses the promise and potential perils of eHealth technologies like remote monitoring devices, virtual assistants, and personalized health records.
2) Factors driving eHealth include patient demand for convenient access to information, the ability to link separate health services, and using technology to address staffing shortages. However, eHealth may also shift costs to patients and change the role of healthcare providers.
3) Potential benefits of eHealth include improved patient information and choice, better communication between providers, and links to vetted external health resources. However, ensuring privacy and appropriate access to personal health data is also discussed.
Use of Genetic Databases to Advance Diagnostic Test DevelopmentEMMAIntl
In December 2018, the U.S. Food and Drug Administration formally recognized a public database that contains information about genes, genetic variants, and their relationship to disease. This blog discusses the motivation for creating such public databases and the implications for developers of genetic tests...
- The traditional business model of personal genomics companies sees individuals pay to sequence their genomes and receive analysis results, while the companies keep the genomic data and sell it to pharmaceutical companies. However, this model has limitations in addressing high sequencing costs for individuals, lack of individual control over their data, and lack of incentives.
- The proposed Nebula model uses blockchain technology to connect individuals directly with data buyers, eliminating personal genomics companies as middlemen. This is intended to reduce sequencing costs for individuals, give them control over their genomic data and how it is used, and provide incentives.
- The model aims to satisfy both individuals, by addressing the above issues, and data buyers' needs around data availability, acquisition, and
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...ICEGOV
This document discusses challenges and opportunities related to e-health standards. It begins with an overview of why e-health is important and the complexity of healthcare. It then discusses the need for interoperable health information and progress that has been made, as well as challenges that remain. The document uses examples like the H1N1 outbreak and physician reimbursement to illustrate issues. It outlines major types of e-health standards and examples of standards in use. It concludes by discussing the ongoing challenge of implementing standards and the journey ahead to make e-health and standards easier to use.
This document includes three blog posts recently featured in PharmaVOICE.
The blogs focus on how enhanced access to in-depth health data is impacting an understanding of personhood, the environment around us, and the pharma operating model.
BLOG 1 (Pages 2-7)
Waves of Real Life Data Are Inundating Pharma...Can They Keep Up?
BLOG 2 (Pages 8-13)
Better understanding where and how we live will vastly improve remote patient
monitoring approaches
BLOG 3 (Pages 14-18)
5 Ways Pharma Can Be More Patient-Centered & Usher in Digital Transformation
Send me a note with your comments and feedback. Thanks for reading!
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Direct to consumer genetic testing provides ancestry and health risk information directly to consumers but has significant limitations. While it may promote health awareness, unexpected results can be stressful and consumers may make important medical decisions based on inaccurate or incomplete information from unregulated tests. The high rate of false positives seen in confirmatory testing suggests many consumers are receiving incorrect information from these tests. Regulatory bodies have concerns about oversight, accuracy, and inappropriate use of genetic data that could impact consumers.
The document discusses how big data and technology are revolutionizing medicine by enabling more individualized diagnosis and treatment through building predictive models of disease using multiple scales of biological data. It provides examples of how wearable devices can longitudinally monitor patient health and how an data-driven analysis of Alzheimer's disease implicated the immune system rather than plaques and tangles. It argues that this evolution will benefit patients through more proactive care, payers through reduced costs from preventative measures and targeted therapies, and pharmaceutical companies through improved drug effectiveness.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Genomic data and the electronic health record (ehr)nagipradeep
The document discusses the need for innovation to integrate genomic data into electronic health records (EHRs). It notes that without such integration, patient care suffers as clinicians lack access to important genetic information. The author argues innovation is required to address this problem through disruptive changes that allow seamless inclusion of genomic test results and family histories in EHRs. Examples are provided of potential innovations like clinical decision support alerts and training medical students in genomic data interpretation. The Vanderbilt University PREDICT program is highlighted as it uses EHRs and decision tools to help clinicians avoid adverse drug reactions based on patients' genetic risks. The conclusion stresses that innovation is urgently needed to avoid poor patient outcomes from a lack of accessible genomic data in
Welcome to the age of cognitive computing: where intelligent machines have
moved from the realms of science fiction to the present day. This groundbreaking
technology is driving advanced discoveries and allowing improved decision-making –
resulting in better patient care
A look at the key trends and challenges in applying Big Data to transform healthcare by supporting research, self care, providers and building ecosystems. Purchase the report here: https://gumroad.com/l/PlXP
Precision medicine is a rapidly evolving approach to healthcare that uses patient-specific data to tailor medical treatment and therapies to an individual’s unique needs.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
This document discusses big data analytics for the healthcare industry. It describes how big data is being generated at an alarming rate in healthcare for purposes like patient care and regulatory compliance. The four V's of big data - volume, velocity, variety and veracity - are discussed. The document outlines how big data analytics can improve patient outcomes through pathways like right living, right care, right provider, right innovation and right value. Hadoop applications that can help the healthcare sector manage and analyze large amounts of unstructured data are also presented.
Redesigning the healthcare with artificial intelligence, genomics & neuroscienceArtivatic.ai
HEALTHCARE WHITEPAPER BY ARTIVATIC DATA LABS PRIVATE LIMITED
Healthcare in today’s world has not changed in terms of method of diagnosis where the doctor analyses the patient’s history along with historical records of symptoms to their diagnosis, keeping in mind the current practices involved in the treatment. Usually going through multiple tests and a process of elimination, the process is hectic and more often than not prone to human error. It is not possible for any doctor to analyse every bit of data available in relation to a patient which may include the genetic code etc. Nor is it possible for them to keep track of all historical cases where similar symptoms may have been shown. This is where the application of AI and ML are crucial. They streamline the process and reduce human error while considering all the data available. With the use of AI, the doctor could automatically get recommendations on what kind of diseases could be causing the symptoms shown. Or the patients could be suggested the correct doctor based on their personal preferences and symptoms shown.
Artificial Intelligence, Machine Learning, Genomic, Neuroscience, Diseases
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Similar to Possible Solution for Managing the Worlds Personal Genetic Data - DNA Guide, Inc. (20)
Hepatocarcinoma today between guidelines and medical therapy. The role of sur...Gian Luca Grazi
Today more than ever, hepatocellular carcinoma therapy is experiencing profound and substantial changes.
The association atezolizumab (ATEZO) plus bevacizumab (BEVA) has demonstrated its effectiveness in the post-operative treatment of patients, improving the results that can be achieved with liver resections. This after the failure of the use of sorafenib in the already historic STORM study.
On the other hand, the prognostic classification of BCLC is now widely questioned. It is now well recognized that the indications for surgery for patients with hepatocellular carcinoma are certainly narrow in BCLC and no longer reflect what is common everyday clinical practice.
Today, the concept of multiparametric therapeutic hierarchy, which makes the management of patients with hepatocellular carcinoma much more flexible and allows the best therapy for the individual patient to be identified based on their clinical characteristics, is gaining more and more importance.
The presentation traces these profound changes that are taking place in recent years and offers a modern vision of the management of patients with hepatocellular carcinoma.
Pharmacotherapy of Asthma and Chronic Obstructive Pulmonary Disease (COPD)HRITHIK DEY
This PowerPoint presentation provides an in-depth overview of the pharmacotherapy approaches for managing asthma and Chronic Obstructive Pulmonary Disease (COPD). It covers the pathophysiology of these respiratory conditions, the various classes of medications used, their mechanisms of action, indications, side effects, and the latest treatment guidelines. Designed for students, healthcare professionals, and anyone interested in respiratory pharmacology, this presentation offers a comprehensive understanding of current therapeutic strategies and advancements in the field.
Why Does Seminal Vesiculitis Causes Jelly-like Sperm.pptxAmandaChou9
Seminal vesiculitis can cause jelly-like sperm. Fortunately, herbal medicine Diuretic and Anti-inflammatory Pill can eliminate symptoms and cure the disease.
Ventilation Perfusion Ratio, Physiological dead space and physiological shuntMedicoseAcademics
In this insightful lecture, Dr. Faiza, an esteemed Assistant Professor of Physiology, delves into the essential concept of the ventilation-perfusion ratio (V˙/Q˙), which is fundamental to understanding pulmonary physiology. Dr. Faiza brings a wealth of knowledge and experience to the table, with qualifications including MBBS, FCPS in Physiology, and multiple postgraduate degrees in public health and healthcare education.
The lecture begins by laying the groundwork with basic concepts, explaining the definitions of ventilation (V˙) and perfusion (Q˙), and highlighting the significance of the ventilation-perfusion ratio (V˙/Q˙). Dr. Faiza explains the normal value of this ratio and its critical role in ensuring efficient gas exchange in the lungs.
Next, the discussion moves to the impact of different V˙/Q˙ ratios on alveolar gas concentrations. Participants will learn how a normal, zero, or infinite V˙/Q˙ ratio affects the partial pressures of oxygen and carbon dioxide in the alveoli. Dr. Faiza provides a detailed comparison of alveolar gas concentrations in these varying scenarios, offering a clear understanding of the physiological changes that occur.
The lecture also covers the concepts of physiological shunt and dead space. Dr. Faiza defines physiological shunt and explains its causes and effects on gas exchange, distinguishing it from anatomical dead space. She also discusses physiological dead space in detail, including how it is calculated using the Bohr equation. The components and significance of the Bohr equation are thoroughly explained, and practical examples of its application are provided.
Further, the lecture examines the variations in V˙/Q˙ ratios in different regions of the lung and under different conditions, such as lying versus supine and resting versus exercise. Dr. Faiza analyzes how these variations affect pulmonary function and discusses the abnormal V˙/Q˙ ratios seen in chronic obstructive lung disease (COPD) and their clinical implications.
Finally, Dr. Faiza explores the clinical implications of abnormal V˙/Q˙ ratios. She identifies clinical conditions associated with these abnormalities, such as COPD and emphysema, and discusses the physiological and clinical consequences on respiratory function. The lecture emphasizes the importance of understanding these concepts for medical professionals and students, highlighting their relevance in diagnosing and managing respiratory conditions.
This comprehensive lecture provides valuable insights for medical students, healthcare professionals, and anyone interested in respiratory physiology. Participants will gain a deep understanding of how ventilation and perfusion work together to optimize gas exchange in the lungs and how deviations from the norm can lead to significant clinical issues.
Case presentation of a 14-year-old female presenting as unilateral breast enlargement and found to have a giant breast lipoma. The tumour was successfully excised with the result that the presumed unilateral breast enlargement reverting back to normal. A review of management including a photo of the removed Giant Lipoma is presented.
Mainstreaming #CleanLanguage in healthcare.pptxJudy Rees
In healthcare, every day, millions of conversations fail. They fail to cover what’s really important, fail to resolve key issues, miss the point and lead to misunderstandings and disagreements.
Clean Language is one approach that can improve things. It’s a set of precise questions – and a way of asking them – which help us all get clear on what matters, what we’d like to have happen, and what’s needed.
Around 1000 people working in healthcare have trained in Clean Language skills over the past 20+ years. People are using what they’ve learnt, in their own spheres, and share anecdotes of significant successes. But the various local initiatives have not scaled, nor connected with each other, and learning has not been widely shared.
This project, which emerged from work done by the NHS England South-West End-Of-Life Network, with help from the Q Community and especially Hesham Abdalla, aims to fix that.
POTENTIAL TARGET DISEASES FOR GENE THERAPY SOURAV.pptxsouravpaul769171
Theoretically, gene therapy is the permanent solution for genetic diseases. But it has several complexities. At its current stage, it is not accessible to most people due to its huge cost. A breakthrough may come anytime and a day may come when almost every disease will have a gene therapy Gene therapy have the potential to revolutionize the practice of medicine.
Chair, Benjamin M. Greenberg, MD, MHS, discusses neuromyelitis optica spectrum disorder in this CME activity titled “Mastering Diagnosis and Navigating the Sea of Targeted Treatments in NMOSD: Practical Guidance on Optimizing Patient Care.” For the full presentation, downloadable Practice Aids, and complete CME information, and to apply for credit, please visit us at https://bit.ly/4av12w4. CME credit will be available until June 27, 2025.
Join the leading All Range PCD Pharma Franchise in India and grow your business with a trusted partner. We offer an extensive range of high-quality pharmaceutical products, competitive pricing, and comprehensive marketing support. Benefit from our expertise, wide distribution network, and excellent customer service. Elevate your pharma business with See Ever Healthcare's proven PCD franchise model.
https://www.seeeverhealthcare.com/all-range-pcd-pharma-franchise-in-india/
Hemodialysis: Chapter 8, Complications During Hemodialysis, Part 2 - Dr.GawadNephroTube - Dr.Gawad
- Video recording of this lecture in English language: https://youtu.be/FHV_jNJUt3Y
- Video recording of this lecture in Arabic language: https://youtu.be/D5kYfTMFA8E
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Chair and Presenter, Stephen V. Liu, MD, Benjamin Levy, MD, Jessica J. Lin, MD, and Prof. Solange Peters, MD, PhD, prepared useful Practice Aids pertaining to NSCLC for this CME/MOC/NCPD/AAPA/IPCE activity titled “Decoding Biomarker Testing and Targeted Therapy in NSCLC: The Complete Guide for 2024.” For the full presentation, downloadable Practice Aids, and complete CME/MOC/NCPD/AAPA/IPCE information, and to apply for credit, please visit us at https://bit.ly/4bBb8fi. CME/MOC/NCPD/AAPA/IPCE credit will be available until July 1, 2025.
Decoding Biomarker Testing and Targeted Therapy in NSCLC: The Complete Guide ...
Possible Solution for Managing the Worlds Personal Genetic Data - DNA Guide, Inc.
1. Navigating Genetic Data
Regulation, Privacy and Ease of Use
Presentation @ BIT World DNA Day and Genome Day, Dalian, China 2011
DNA Guide, Inc. All rights reserved 2011
Alice Rathjen, President, Founder
alice@dnaguide.com
2. The Problem..
Inadequate Infrastructure
Genetic Data Explosion
Huge investment in
Sequencing Technologies
and Molecular Diagnostics
Personalized Medicine
R&D
Pharma /
Clinical
Trials
Consumers/
Patients
INSURANCE
Government & NGO Regulations
Health
Services
The amount of genetic data is about to explode. However, there’s currently inadequate infrastructure for leveraging the value
of genetic data in health care: current software is designed for researchers, there’s a shortage of genetically trained health care
professionals and people fear their genetic data could be used against them. These issues need to be addressed for
personalized medicine to succeed.
3. Anxiety and Fear of Genetic Data
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Personal genetic information is highly sensitive data touching on the areas of identity, paternity, self worth, and privacy. The
problem that really needs to be solved is how to cultivate a sense of trust between physicians and patients and how to
structure health information transfer in such a way that patients can participate in the management of their data as their bodies
become increasingly digital.
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How Can Personalized Medicine Grow?
Physician as Guide
With Cost Effective,
Real Time Delivery
of Personalized
Information
Patients Increasing
Participation in
Management of their
Genome and Medical
Information
With proper tools we can provide physicians and patients a sense of mastery and control over genetic and health
datasets. This will help facilitate higher patient engagement and opt-in rates for participation in studies - which in turn
will speed up the process of discovery, approval and market adoption of personalized medicine.
5. Traditional Patient/Research Model
• Patient Gets Sick –
Provides Sample
• Small Patient Sample Sizes
• Written Consent?
• Patient/ Data Separated
• Data Quality Over Time?
• Data Liability Over Time?
In the current typical health information system a person gets sick, signs away their rights to their tissues and/or
information, and receives no benefit in return. This model results in small, expensive, research studies and it acquires
significant liabilities over time with regards to consent disputes and potential loss of anonymity. It also makes
tracking individuals, or improving data collection over long stretches of time, difficult.
6. Exponential/Disruptive Model
• “Brown Bag” DNA Sample Submission
(includes user account and password)
• People Own Their Own Genome
• Written Consent Evolves Into
Real Time Consent
• Dynamic Communication With Patient
• Participatory Medicine
• Self-Organizing Genetic Research
In the new model a person submits a DNA sample from a kit that contains a user account and password. Values from
their DNA are used to convert them into a node on the network. Thereafter, they can log on, setting up access for their
doctor, or others, to their genetic or other personal data. Written consent evolves into real time consent. If their password
is compromised, the person submits a new sample to re-establish ownership over the dataset. Self-organizing genomes
drive research. Third parties perform audits to prove authorized use.
7. Personalized Medicine Genome Browser
What you see here is a
example of all the
chromosomes in a person’s
genome that a user would see
when logged on.
DNA Guide then adds layers to
this map so that a person’s
genetic data lies beneath this
image. This image has a
coordinate system associated
to it with full pan and zoom
functionality, like a type of
Google earth for the cell.
On top of this platform we
provide tools for managing the
flow of information from the
lab to any research or health
services setting with the ability
to engage the patient at home.
8. Personalized Medicine Genome Browser
In a typical use case
scenario, a physician could
perform a search based on a
term such as “breast cancer”
and immediately view only
those markers out of a
massive dataset that are
relevant for a particular
patient.
A genome browser such as
this could help provide
genetic counselors and
health service providers a
tool to review genetic
information with their
patients.
By placing the data in this
format, we’ll be able to show
structural variants for full
genomes. Current browsers
show just one chromosome
at a time and aren’t able to do
this.
(Mitochondria)
9. At Zoom in Level Each Base Pair Is A
Programmable Object
At the zoom in level each base pair
is a programmable object, allowing
DNA Guide to automate many of the
processes involved in interpreting
genetic data. This programming
interface can be opened up to allow
third parties to develop a whole
series of molecular diagnostic and
recreational applications to be built
that interact with the individuals
DNA.
10. Government/NGO Regulation and
Digital Human Rights
www.DNAguide.com
Different government and NGO’s will have different regulations with regards to genetic data access. In addition,
issues around privacy and the abuse of genetic data may give rise to various forms of digital human rights. Any
entity working with personal genetic data will no doubt face the scenario where different types of base pairs and
different combinations of base pairs will be regulated differently for different users. Hence, the need for software
that manages interpretation and access down to the base pair level will be critical for transmitting genetic
information from the lab to the physician and patient consistent with regulations.
11. Three Points of Dynamic Regulation
Quality of Science
Medical Utility
Viewing Risk
(Graded) A,B,C,D,F
W = Withdrawn
I = Incomplete
(by Scientific Community)
(by Health Care Providers/ Payors)
E = Everyone,
PG = Physician Guidance
R = Restricted
(Genetic Counselors, Ethics)
Category Rating
(Five Star Rating)
(Movie Rating)
The genetic information sector could be dynamically regulated by a process where an interpretation could be submitted
and receive a rating in three areas: quality of science, medical utility and viewing risk. Each category could be the domain
expertise of the entities indicated above by their providing rating standards which would then be applied to each genetic
marker involved in a test.
12. Genetic Information Marketplace
Discovery Ecosystem
Research Feeds
Personalized Medicine
Patients Feed Research
R&D
Pharma /
Clinical
Trials
Consumers/
Patients
INSURANCEGovernment & NGO Regulations
Health
Services
With a rating system for quality of science, medical utility and viewing risk, genetic interpretation will have a clearer path to
market. For example, a health service provider or insurer could formulate policies such as delivering tests with a science score
of A and medical utility rating of five stars with the proper level of counseling triggered the moment the patient accessed their
genetic information.
13. Example of Genetic Information Flow
PATIENT
Seeks Health Services
Submits DNA Sample
Views interpretation of results
from physician
Participation in Clinical Trials
Receives Drugs Info from
Pharma
Health Services Payer Entities
Require DNA tests for reimbursement of Rx and determine which genetic tests qualify for reimbursement
LAB
Process sample and results
Provide raw DNA data to
database storage for
interpretation
PHARMA/BIOTECH/R&D
INDUSTRY
Provide sample collection kits
and information regarding
personalized medicine
Interface with physician and
patients in clinical trials
Provide lab with new products
and services
Provide patient with retail
outlet for personalized
medicine products
PHYSICIAN,
PATHOLOGIST,
GENETIC COUNSELOR
Assess Patient
Interact with insurance to
determine eligibility
Prescribe test
Collect patient DNA sample
Submit DNA sample to lab
View lab information and
interpret results
Provide analysis and
recommendation to patient
Prescribe course of action.
Interface with pharma
regarding personalized
medicine
Interact with pharma with
clinical trial information
DNA Guide
Genome Management
Software Information Flow
14. The symbols below are an example of how we could convert SNPs information into a graph form to help explain
genetic variation. Using these symbols it’s possible to stack 1,000s of genomes on top of each other in a map and see
variation.
Mobile Platform Symbols
For Ease of Use
Highest Risk
Slightly Higher
Risk
Normal
Lower Risk
Low Magnitude High Magnitude
Below we see how complex ranges of information across multiple locations could be converted to symbols to
make genetic information more easily understood by non-scientific audiences. For example, a red symbol
indicates higher risk and green lower risk. The larger the dot – the more significant the association between
high, normal or low risk.
15. High Risk, Low Risk Assessment
Fast and Affordable
Here’s an example of what
the diagnostic results for a
high risk genome could
look like
By using a simple symbol
classification, DNA Guide
is able to provide a quick
assessment for the entire
genome.
More detailed information
could be available by
selecting the objects in the
map to generate a report.
16. Converting the $1,000 Genome into the
Two Minute Genome
Here’s an example of a low risk
genome result.
Complex molecular diagnostic
information can be delivered in
a format that is fast and
affordable on a mobile device.
DNA Guide’s software is able
to convert the $1,000 genome
into the two minute genome –
bringing personalized
medicine to the point of care.
17. DNA Guide Toolkit
DNA Security
Token DNA Compass DNA Body
DNA Guide uses values within the DNA
sample to uniquely identify every dataset.
This token can serve as a dynamic or
static IP address - allowing every
organism to become a node on the
network.
DNA Guide provides dynamic maps of entire
genomes available on all mobile platforms. DNA
Guide’s Compass can perform spatial analysis
across multiple layers of different types of genetic
data. Current browser solutions on the
marketplace are limited to single chromosomes
with one dimensional analysis.
DNA Guide’s DNA Body will provide
expression data, medical records, and
images to be linked to a map of the human
body and to genomic location.
DNA Guide’s solution has three core modules : a security component and map linking genetic data to 2d
and 3d representation of the cell or body. The total solution offers genetic data interoperability for all
users involved in personalized medicine.
18. DNA Guide Security Token
DNA Guide selects about two hundred values
within each DNA sample to uniquely identify
one in a trillion persons. This DNA token
provides the foundation for further security
and a mechanism for providing privacy over
the dataset.
• Uniquely identify each dataset
• Store and retrieve genetic data anonymously
• Perform audits, merge data
• Re-associate information throughout a person’s lifetime
• Have variations for different uses
Raw DNA Values
DNA Security Token
19. Mapping the Human Genome With
Geographic Information Systems (GIS)
DNA Guide Novel Approach:
Physical (or biological) data with annotation information is
mapped to point, line or polygon object(s) with coordinates to
enable the spatial query and analysis of information.
Line (mRNA, siRNA, indels,
translocations)
(x,y,z)
Point (alleles, SNPs, genes,
Methylation, Expression Data each
as a separate layer in the map)
• Data is optimized for spatial comparisons with ability to utilize
raster to vector conversion techniques.
• Re-project genetic data on the fly for comparison of different
alignments.
• Find the “Needle in the Haystack” (layers optimized by spatial
query).
• Leverage existing mapping tools such as buffer, cluster and
network topology analysis for discovery.
http://en.wikipedia.org/wiki/Geographic_information_system
• View Information in “Thematic Map” format
http://en.wikipedia.org/wiki/Thematic_map
(direction/distance)
Polygon (any Genetic Region)
(in) (out)
20. Mapping From DNA, mRNA, to
Proteins, to Pathways and Beyond
Using Mapping Software to Map the Genome
GIS (Geographic Information Systems)
DNA Guide genome navigation applications use
Geographic Information Systems (GIS)
technology. The graphic objects have “topology”
which allows symbols from different layers in the
map (i.e. genes, SNPs, insertions, deletions, copy
number variations, gene expression data) to know
where they are in relation to each other. Objects
can be queried within the same layer or in relation
to different layers.
Each node in the map can have a 2 or 3D position
and direction associated with it. In the case of
genome data we treat chromosomes as
continents, SNPs as if they're towns on a map,
and genes can be treated like a State (a polygon),
highways (a line) or cities (a point) depending on
how we want to study the information. The
standard GIS data output is a thematic map, an
icon-driven format well suited for mobile
platforms.
By using mapping coordinates, users will be able
to move between layers of genetic information -
all the way from DNA to MRNA to proteins, to
pathways to the function of physiology to body
systems.
From a technology standpoint we’ve redeployed
existing mapping software and swapped out the
sphere of the earth for the cell.
21. DNA Body Slide
The following images were
taken from Google Body
yet represent DNA Guide’s
plans to implement
mapping software to
include a representation of
the human form linked to
genetic data as part of our
solution.
We anticipate users will be
able to click on the body to
generate queries for
information, with our
eventually showing how
their genes are expressed
in their body.
DNA Guide is working
towards a future where a
person’s medical
information is linked to a
representation of their
human form with their
electronic medical record
user account information
being derived from the
values within their DNA.
22. T
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Acknowledgements
DNA Guide, Inc.
http://www.DNAguide.com
Alice Rathjen
President and Founder
alice@dnaguide.com
Deborah Kessler, CEO
William Kimmerly, Ph.D.
Chief Scientific Officer
Xavier Thomas
Product Development Dir.
Saw Yu Wai
Platform Architect
Mark Boguski, MD. Ph.D,
Advisor
Editor's Notes
DNA Guide is a California based start-up – focusing on the security and visualization of genetic data on mobile platforms - the idea being other entities would create genetic data and provide the interpretation for it and we help distribute it. Today I would like to propose a possible solution for managing the worlds personal genetic data – one that may be able to help us all navigate the areas of genetic regulation, privacy and ease of use.
The amount of genetic data is about to explode. However, there’s currently inadequate infrastructure for leveraging the value of genetic data in health care: current software is designed for researchers, there’s a shortage of genetically trained health care professionals and people fear their genetic data could be used against them. These issues need to be addressed for personalized medicine to succeed.
Personal genetic information is highly sensitive data touching on the areas of identity, paternity, self worth, and privacy. The problem that really needs to solved is how to cultivate a sense of trust between physicians and patients and how to structure information transfer in such a way that patients can participate in the management of their health data as their bodies become increasingly digital.
With proper tools we can provide physicians and patients a sense of mastery and control over genetic and health datasets. This will help facilitate higher patient engagement and opt-in rates for participation in studies - which in turn will speed up the process of discovery, approval and market adoption.
In the current typical health information system a person gets sick, signs away their rights to their tissues and/or information and receives no benefit in return. This model results in small, expensive, research studies and acquires significant liabilities over time with regards to consent disputes and potential loss of anonymity. It’s also difficult tracking individuals or improving data collection over long stretches of time.
In the new model a person submits a DNA sample kit that contains a user account and password which in turn provides the foundation for their electronic medical record. They long on, set up access for their doctor, or other entity to their genome and over time /or other personal information. Written consent evolves into real time consent. Ideally people would have the option to only do business with those entities that agree to third party audits to prove authorized use. If their password is compromised – the person submits a new sample to re-establish ownership over the dataset. T
What you see here is a example of all the chromosomes in a persons genome that a user would see when logged on. DNA Guide then adds layers to this map so that a person sequence and/or SNP information lies beneath this image that has a coordinate system associated to it with the full pan and zoom of a type of Google earth for the cell. On top of this platform we then provide tools for managing the flow of information from the lab, to any health services environment, ability to engage physician patient or consumer at home.
At the zoom in level each base pair is a programmable object – allowing DNA Guide to automate many of the processes involved in the interpretation of genetic data. DNA Guide can open up the application programming interface for a whole series of molecular diagnostics and recreational applications to be built that interact with the individuals DNA as manage interpretation and access down to the base pair level. (trigger counseling at the moment information is accessed). We can open up the application programming interface for a whole series of molecular diagnostics and recreational applications to be built that interact with the individuals DNA as well as apply organizational
Different government and NGO’s will have different regulations with regards to genetic data access. In addition - issues around privacy and the abuse of genetic data may give rise to various forms of digital human rights. Any entity working with personal genetic data will no doubt face the scenario where different types of base pairs and different combinations of base pairs will be regulated differently for different users. Hence, the need for software that manages interpretation and access down to the base pair level will be critical for transmitting genetic information from the lab to the physician and patient consistent with regulations.
One way of regulating the genetic information sector is for there to be a process where a commercial interpretation could be submitted and receive an identifier along with a rating in three areas: quality of science, medical utility and viewing risk. Each category could be the domain expertise of the entities indicated above with their providing a rating which would then be applied to each genetic marker involved in the test.
By applying a rating system for quality of science, medical utility and viewing risk – personal genetic data will be able to enter the market place with the various players able to monetize and while refining the deployment of personalized medicine. For example, a health service provider would formulate policies such as delivering tests with a science score of A and medical utility rating of 5 stars or higher and have the application built such that the proper level of patient counseling was triggered the moment the patient went to access their genetic information.
This diagram here outlines the needs regarding the flow of genetic information between patient, payers, the lab, and any research and health services setting.
The symbols above are an example of how we could convert SNPs information into a graph form to help explain genetic variation. Using these symbols it’s possible to stack 1,000s of genomes on top of each other and detect variation. Here we see how complex ranges of information across multiple locations could be placed into a format that would make genetic informaiton available to non-scientific audiences.
Here we did a query on breast cancer and are showing some of his higher risk markers. We’re looking for funding to scale this application to include an entire genome. By placing the data in this format we’ll be able to show insertions, deletions, copy number variants as well as incorporate gene expression data into a single map. Current browsers show just one chromosome at a time and aren’t able to do this.
Here we did a query on breast cancer and are showing some of his higher risk markers. We’re looking for funding to scale this application to include an entire genome. By placing the data in this format we’ll be able to show insertions, deletions, copy number variants as well as incorporate gene expression data into a single map. Current browsers show just one chromosome at a time and aren’t able to do this.
This slide explains some of the benefits of using geographic information systems technology on a genetic dataset. Genetic markers are converted into a point, line or polygon that can be spatially analyzed in relation to each other.
What DNA Guide has done is swap out the earth for the cell. By using mapping coordinates - users will be able to move between layers of genetic information - all the way from DNA to MRNA to proteins, to pathways to the function of physiology to body systems. The current bioinformatics tools aren’t able to do this.
Special thanks to Saw Yu Wai for her work on getting the full extents of the genome working on the various mobile platforms for us and the rest of DNA Guide’s team.