BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...IJTET Journal
inAbstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step.
An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come.
This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective.
Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology.
Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase.
The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data.
While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.
http://kaashivinfotech.com/
http://inplanttrainingchennai.com/
http://inplanttraining-in-chennai.com/
http://internshipinchennai.in/
http://inplant-training.org/
http://kernelmind.com/
http://inplanttraining-in-chennai.com/
http://inplanttrainingchennai.com/
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...IJTET Journal
inAbstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step.
An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come.
This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective.
Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology.
Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase.
The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data.
While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.
http://kaashivinfotech.com/
http://inplanttrainingchennai.com/
http://inplanttraining-in-chennai.com/
http://internshipinchennai.in/
http://inplant-training.org/
http://kernelmind.com/
http://inplanttraining-in-chennai.com/
http://inplanttrainingchennai.com/
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
Prof. Mark Coles (Oxford University) - Data-driven systems medicinemntbs1
The summary of Prof. Mark Coles' presentation from the Jun 11-12th 2019 event Data-driven systems medicine at Cardiff University Brain Research Imaging Centre.
Intelligent Systems for Cancer Genomics (AIS305) - AWS re:Invent 2018Amazon Web Services
One of the most exciting frontiers in science is building automated systems that use existing biomedical data to understand and ultimately treat human disease. The key difficulty in the case of cancer is that it is a highly heterogeneous disease, making it challenging to uncover which molecular alterations in tumors are important for the disease and to predict how an individual will respond to treatment. This talk presents an overview of integrative computational methods for analyzing cancer genomes that leverage a diverse range of complementary data in order to extract biomedically relevant insights.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
Prof. Mark Coles (Oxford University) - Data-driven systems medicinemntbs1
The summary of Prof. Mark Coles' presentation from the Jun 11-12th 2019 event Data-driven systems medicine at Cardiff University Brain Research Imaging Centre.
Intelligent Systems for Cancer Genomics (AIS305) - AWS re:Invent 2018Amazon Web Services
One of the most exciting frontiers in science is building automated systems that use existing biomedical data to understand and ultimately treat human disease. The key difficulty in the case of cancer is that it is a highly heterogeneous disease, making it challenging to uncover which molecular alterations in tumors are important for the disease and to predict how an individual will respond to treatment. This talk presents an overview of integrative computational methods for analyzing cancer genomes that leverage a diverse range of complementary data in order to extract biomedically relevant insights.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
Bioinformatics Introduction and Use of BLAST ToolJesminBinti
Hi, I am Jesmin, studying MCSE. I think this file will help you if you want to know the basic information about Bioinformatics and the use of BLAST tool. The BLAST tool is the tool that matches the sequences of DNA,RNA and proteins.
Introduction
Overview
Reductionist approach
Holistic approach
What is systems biology?
○ Advantages of Systems Biology
Tools of holistic approach
○ Proteomics, Transcriptomics and Metabolomics
Conclusion
References
Conferencia de la Dra. Ana María Roa, Bióloga Molecular, sobre Epigenética, impartida en la Universidad Popular Carmen de Michelena de Tres Cantos el 1 de marzo de 2013.
Más información en:
http://www.universidadpopularc3c.es/index.php/actividades/conferencias/event/448-conferencia-una-revision-de-los-conocimientos-fundamentales-de-la-biologia-de-la-celula-la-epigenetica
Cardiotoxicity is unfortunately a common side effect of many modern chemotherapeutic agents. The mechanisms that underlie these detrimental effects on heart muscle, however, remain unclear. The Drug Toxicity Signature Generation Center at ISMMS aims to address this unresolved issue by providing a bridge between molecular changes in cells and the prediction of pathophysiological effects. I will discuss ongoing work in which we use next-generation sequencing to quantify changes in gene expression that occur in cardiac myocytes after they are treated with potentially toxic chemotherapeutic agents. I will focus in particular on the computational pipeline we are developing that integrates sophisticated sequence alignment, statistical and network analysis, and dynamical mathematical models to develop novel predictions about the mechanisms underlying drug-induced cardiotoxicity.
Jaehee Shim is a Ph.D candidate in the Biophysics and Systems Pharmacology Program at Icahn School of Medicine at Mount Sinai (ISMMS). As a part of her Ph.D. studies, she is building dynamical prediction models based on analysis of gene expression data generated by the Drug Toxicity Signature Generation Center at ISMMS. She received her B.S in Biochemistry from the University of Michigan-Dearborn. Prior to starting her Ph.D, Jaehee worked at the ISMMS Genomics Core with a team of senior scientists and gained experience in improving and troubleshooting RNA sequencing protocols using Next Generation Sequencing Platforms.
Interactomics, Integromics to Systems Biology: Next Animal Biotechnology Fron...Varij Nayan
“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological
systems”
(David Baltimore, Nobel Laureate)
A Systems Biology Approach to Natural Products ResearchHuda Nazeer
Explains the systems biology approach (holistic approach), its advantages and tools used compared to the reductionist approach in natural products research.
Similar to From empirical biomarkers to models of disease mechanisms in the transition to precision medicine (20)
Accelerating the benefits of genomics worldwideJoaquin Dopazo
Grand Challenges in Genomics
A Joint NHGRI and Wellcome Trust Strategic Meeting
25 and 26 February 2019
https://www.wellcomeevents.org/WELLCOME/media/uploaded/EVWELLCOME/event_661/Draft_agenda_for_WT_December_2018.pdf
Join lecture: Nicky Mulder, Han Brunner and Joaquin Dopazo
Big data genómico
Presente y futuro en el manejo de datos genómicos en la práctica clínica
XXIII Jornadas Nacionales de Informática Sanitaria,
Málaga, 16 junio, 2016
http://www.seis.es/JornadasAndalucia16/
The server of the Spanish Population VariabilityJoaquin Dopazo
DNA Day
Hospital Universitario La Paz, Madrid, Spain April 28th, 2014
The first server of the Spanish Population Variability.
Freely available: http://ciberer.es/bier/exome-server/
See alse related tools:
BiERapp: http://bierapp.babelomics.org (to help in the prioritization of disease genes)
TEAM: http://team.babelomics.org (to manage panels of genes for targeter resequencing based diagnostic)
How to transform genomic big data into valuable clinical informationJoaquin Dopazo
How to transform genomic big data into valuable clinical information
The impact of genomics in translational medicine: present view
13th October 2014, Vall d’Hebron Institute of Research (VHIR), Barcelona, Spain
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
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
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
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
NVBDCP.pptx Nation vector borne disease control program
From empirical biomarkers to models of disease mechanisms in the transition to precision medicine
1. Joaquín Dopazo
Clinical Bioinformatics Area,
Fundación Progreso y Salud,
Functional Genomics Node, (INB-ELIXIR-es),
Bioinformatics in Rare Diseases (BiER-CIBERER),
Sevilla, Spain.
From empirical biomarkers to models of
disease mechanisms in the transition to
precision medicine
http://www.clinbioinfosspa.es
http://www. babelomics.org
@xdopazo
ECTB, Barcelona, Spain, 17 abril, 2018
2. Precision medicine is based on a better knowledge of phenotype-genotype relationships.
This ultimately involves the knowledge of disease and drug action mechanisms
Requires of a better way of defining diseases by introducing genomic technologies in the
diagnostic procedures and treatment decisions
Setting the problem in context:
The transition to precision medicine
Intuitive
Based on trial
and error
Identification of
probabilistic
patterns
Decisions and
actions based
on knowledge
Intuitive Medicine Empirical Medicine Systems Medicine
Today Tomorrow
Degree of personalization
Genomic biomarkers
Molecular biomarkers
Mechanistic biomarkers
3. Single-gene biomarkers are the result of
probabilistic associations
http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm
Most “personalized” therapies are based on this
type of biomarkers
4. Despite most biomarkers used are single
gene variants, most human genetic diseases
(and almost all traits) have a modular nature
• Conventional single-gene biomarkers have a demonstrated clinical utility.
However, their success is purely probabilistic, often modest and frequently lack
any mechanistic anchoring to the fundamental cellular processes responsible
for the disease or therapeutic response.
• Modular nature of genetic diseases: Causative genes for the same or
phenotypically similar diseases may generally reside in the same biological
module, either a protein complex (Lage et al, 2007), a sub-network of protein
interactions (Lim et al, 2006) , or a pathway (Wood et al, 2007)
Goh 2007 PNAS
Same disease
in different
populations is
caused by
different
genes
affecting the
same
functionsFernandez, 2013, Orphanet J Rare Dis.
Disease
genes are
close in the
interactome
5. There are exceptions: MammaPrint, an
example of successful breast cancer decision
support test based on a multigenic biomarker
The strength of this approach is that it is unbiased: there are
no assumptions about which genes are likely to be involved in
the process of interest. For example, in a data-driven study of
the prognosis of patients with breast cancer, little was known
about the function of 15 of the 70 genes that were found to
constitute a prognostic gene-expression signature4. A
drawback of this approach is that the outcome relies solely on
the quality of the data (and the samples).
By contrast, using the knowledge-driven approach, genes that
are thought to be relevant to a particular cancer trait are
selected on the basis of the scientific literature.
Finding
genes
1 2
Assessing
functions
Risk is calculated as a
function of the 70 gene
expression levels
risk= f(gene1, gene2, … gene70)
By historic reasons
genes were first
selected and their
functionalities were
assessed afterwards.
6. Change in the paradigm
MammaPrint and other multigenic
biomarkers: bottom up, from genes
to functions that define one (or
several) biological modules.
Models of cell functionality:
top-down mechanism-based
biomarkers, from biological
modules to genes
7. Two problems: defining
functional modules and
modeling their behavior
Definition
Gene ontology:
descriptive;
unstructured
functional labels
Interactome:
relationships among
components but
unknown function
Pathways:
relationships among
components and
their functional roles
Behavior
Enrichment methods. GO, etc. (simple
statistical tests). No information on how
components relate among them
Connectivity models. Protein-protein, protein-
DNA and protein-small molecule interactions
(tests on network properties). No information
the functional roles of the components
Mathematical models. Kinetic models
including stoichiometry, balancing reactions, etc.
Computational models. Models of signalling
pathways, metabolic pathways, regulatory
pathways, etc. (executable models)
8. Defining the module:
Pathways: maps of cell activity
(in sickness and in health)
disease-maps.org/
www.genome.jp/kegg
reactome.org
www.wikipathways.org/
Oncogénesis
Alzheimer
More disease maps…
Parkinson
9. Defining pathway activity
We first need a map: pathways are defined in different repositories (KEGG,
Reactome, Biocarta, disease maps, etc.) Then we need to define our
objective within the map (elementary cell processes or functions we are
interested on).
What pathway level makes a real biological meaning?
Gene sub-pathway pathway
Enrichment methods
(pathway-level): Different
and often opposite cell
behaviors are triggered by
the same pathway.
E.g.: death and survival
Gene level: The same gene can trigger different (and
often opposite) responses, depending on the stimulus
Survival
Death
Sub-pathway
(elementary circuit)
connects stimulus to
response
10. Decomposition of a pathway into
their elementary circuits triggering
cell functional responses
11. How realistic are models of
pathway activity?
Beyond static biomarkers—The activity
of signalling networks as an alternate
biomarker?
Fey et al., Sci. Signal. 8, ra130 (2015).
Inability of JNK activation (that mediates
apoptosis) is associated to bad prognostic,
irrespective of MYCN amplification status
Problem:
ODE can
efficiently
solve only
small
systems
Construct, activity inferred
12. 𝑆 𝑛 = 𝜐 𝑛 ∙ 1 − 1 − 𝑠 𝑎
𝑠 𝑎 ∈𝐴
⋅ 1 − 𝑠𝑖
𝑠𝑖∈𝐼
From individual gene
expression profiles…
…to profiles of circuit
activity (and
functional activity)
Two types of activities
Signal propagation models of
signaling pathways
Are scalable
13. Gene expression data are transformed
into signal activity intensities
Cases / controls
Normalizedgenes Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
…….
…….
…….
Cases / controls
Circuits
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
…….
…….
…….
A simple transformation of raw data (normalization) and an algorithm for
signal propagation results in accurate estimations of circuit activities.
The same concept that MammaPrint,
but based on biological knowledge, is used here to estimate cell
functional activity
Circuits within
pathwaysCases / controls
Rawdata
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
Ps1 344 344 4556 667 88
Ps2 543 67 88 90 12 36
Ps3 36 833 78 38 99 00
Ps4 59 73 336 677 00 31
…….
…….
…….
risk= f(gene1, gene2, … gene70)
¡ !
14. Models of signaling activity provide
high-throughput estimations of intensity
activation of cell functions from gene
expression measurements
Some (not all) conventional
cell function can be
studied, one at a time, in
individual assays
Hypothesis: the intensity at which functions are triggered
by the signaling system of the cell is more related to
phenotypes than the intensity of gene expression
With mechanistic models, the intensity
at which the whole repertoire of cell
functions is triggered can be measured
in only one individual experiment
15. Signaling activity trigger cell functions
directly related to cancer progression
DNA replication function is a construct: the activity is inferred not measured
DNA replication= f(gene1, gene2, … genen) Hidalgo et al., 2017 Oncotarget
16. Actually, signal activity triggers
all the cancer hallmarks
Hanahan, Weinberg, 2011
Hallmarks of cancer: the next
generation. Cell 144, 646
Negative regulation of release of cytochrome c
from mitochondria (inhibition of apoptosis)
17. The inferred function activity (mechanistic
biomarker) is more correlated to survival
than the activity of any gene (conventional
biomarker) in the circuit
p-val=5.9x10-8
18. Different cancer use different
gene expression programs to
activate the same functions
19. Signal intensity over certain functions
increases in the initiation of cancer while
on others increases with cancer stage
Cell
division
Cell
cycle
Cancer initiation Cancer progression
20. Beyond cancer: understanding the molecular
mechanisms behind tissue death
Blood coagulationMetabolic switch to hypoxiaImmune response deactivation
21. Prediction of IC50 values from the
activity of signaling circuits
Amadoz et al., Sci. Rep. 2015
22. Actionable models
The real advantage of models is that, the same way they can be used to
convert omics data into measurements of cell functionality that provide
information on disease mechanisms and drug MoA, they can be used to
test hypothesis such as “what if I suppress (or over-express) this (these)
gen(es)?” This lead to the concept of actionable models.
By simulating changes of gene expression/activity it is easy to:
• Directly study of the consequences of induced gene over-expressions
or KOs
• Carry out reverse studies of genes that need to be perturbed to change
cell functionalities, such as:
• Reverting the “normal” functional status of a cell
• Selectively kill diseased cells without affecting normal cells
• Enhancing or reducing cell functionalities (e.g., apoptosis or
proliferation, respectively, to fight cancer)
• Etc.
23. Model validation (1)
The activity of some signaling circuits is correlated with cell survival
Increasedsurvival
Increased pathway activity
Repression of apoptosis
Activation of proliferation
Functions:
Activation of apoptosis
Repression of proliferation
Survival data from Achilles cell line KOs (Broad Institute) can be
compared to the change in circuit activities predicted by the model
Essential circuits: once found, other ways of deactivating these circuits
can be find, opening the door to knowledge-based target discovery
Onco-circuit Tumor suppressor circuit
24. Model validation (2)
Deactivation of tumor
suppressor modules
Deactivation in onco-modules
Prostate cell line Skin cell line
1) Prediction of other gene targets, whose inhibition (modeled KO)
deactivate tumor suppressor or onco-circuits
2) Validation of the real KO effects with Achilles II (Tsherniak A, et al.
2017, Cell 170: 564-576): 60-65% detected accuracy
26. Actionable pathway models
Transcription
We can inhibit EGFR (target of Afanatib) by
reducing its activity value (0.56 in a TCGA
KIRC patient). Absolute KO value = 0
Estrogen signaling pathway http://pathact.babelomics.org/
27. Actionable pathway models
(a holistic view)
The inhibition of the transcription sought has been achieved, but six more pathways
have also been affected in different ways
http://pathact.babelomics.org/
28. Actionable pathway models
Cell cycle inhibited in
Proteoglycans in cancer pathway
Transcription, angiogenesis and other
are inhibited in ErbB signaling pathway
Cell cycle is inhibited in Oxytocin
signaling pathway
http://pathact.babelomics.org/
29. Simulating drug inhibition
“Ideal” KO of
EGFR affects 7
pathways
Real inhibition with
Afanatib affects 11
pathways
Inhibition with
broader spectrum
Trastuzumab
affects 13 pathways
30. An example with melanoma cell line IGR39
SRC gene predicted to be essential by targeting
proliferation-related onco-circuits
GnRH signaling pathway
VEGF signaling pathway
Estrogen signaling pathway
31. Prediction of essential genes that were
never experimentally tested before
0
-4
20
40
60
80
100
-2 0 2
-log10 [IC50]
IGR39
Dasatinib
0 100 250 500 1000 nM
Dasatinib, a specific inhibitor
of SRC, demonstrates the
essentiality of SRC predicted
because the inhibition of the
gene predicts the inhibition of
an onco-circuit
32. Metabolic pathways can also
be modeled
Where ni is the activity of the current node
n, A is the total number of edges arriving to
the node that account for the flux of
metabolites produced in other nodes with
activity values na.
There are 94 modules that
recapitulate the main aspects of
metabolism of carbohydrates,
lipids, amino acids and nucleotides
Metabolic activity:
Differential metabolic activity: two conditions are compared by means of a
Wilcoxon test (FDR adjusted across modules)
34. Metabolic modules also capture cell
functionality associated to cancer prognostic
High activity of Guanine ribonucleotide biosynthesis and Pyrimidine
ribonucleotide biosynthesis modules is associated to low survival.
These modules are target of Mercaptopurine and Gemcitabine.
The mechanism of action of these drugs involves inhibition of DNA synthesis
and that leads to cell death
35. Prediction of gene essentiality from
metabolic pathway essentiality
UPB1 encodes an enzyme (β-ureidopropionase) that catalyzes the last step in the
pyrimidine degradation pathway, required for epithelial-mesenchymal transition
Pyrimidine degradation pathway was predicted to be an onco-module in gastric
cancer cell lines. Predicted genes that switch the pathway off are DPYD, DPYS
(confirmed in Achilles) and UPB1
36. Mechanistic models of cell functional activity
bring the dream of precision personalized
(even individualized) treatments closer
From: Dopazo, 2014, Genomics and transcriptomics in drug discovery. Drug Discovery Today
37. The use of new algorithms that enable the transformation of genomic
measurements into cell functionality measurements that account for
disease mechanisms and for drug mechanisms of action will ultimately
allow the real transition from today’s empirical medicine to precision
medicine and provide an increasingly personalized medicine
The real transition to precision medicine
Intuitive
Based on trial
and error
Identification of
probabilistic
patterns
Decisions and
actions based
on knowledge
Intuitive Medicine Empirical Medicine Precision Medicine
Today Tomorrow
Degree of personalization
38. We are probably less than 10 years away
from having models of cell behavior that
account for phenotypes
Ma et al., Nature Meth. 2018
Predicting cell growth
using a deep neural
network inspired in
GO ontology and
trained with several
millions of yeast
genotypes.
Mechanistic models
of biological systems
will become more
frequent and
accurate in the
coming years
39. Clinical Bioinformatics Area
Fundación Progreso y Salud, Sevilla, Spain, and…
...the INB-ELIXIR-ES, National Institute of Bioinformatics
and the BiER (CIBERER Network of Centers for Research in Rare Diseases)
@xdopazo
@ClinicalBioinfo
Follow us on
twitter
https://www.slideshare.net/xdopazo/
HiPathia Group
Marta Hidalgo (CIPF)
Helena Molina (US)
Isabel Nepomuceno (US)
Carlos Loucera
Kinza Ryan
Cankut Çubuk
Matias Marti
Joaquin Dopazo
See HiPathia
poster #49
+ Miguel A. Pujana, IDIBELL