This document discusses integrating cancer imaging and omics data through multi-modal, multi-dimensional analysis. Specifically, it provides examples of correlating FDG-PET imaging data with transcriptome data from tumors to identify gene networks related to glucose metabolism and the tumor microenvironment. It also describes developing predictive models of tumor metabolism by combining gene expression and imaging features using neural networks. Finally, it discusses the potential for future integrated biomarkers based on multi-omics analysis to provide insights for new clinically useful imaging biomarkers tailored to specific molecular profiles.
June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
BDW16 London - Mishal Patel, NHS - Modernising Routine Breast Cancer Using Bi...Big Data Week
Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to able to harness this large influx of data is of paramount. Traditionally, the systematic collection of medical images for research from heterogeneous sites has not been commonplace within the NHS and is fraught with challenges including; data acquisition, storage, secure transfer and correct anonymisation. Here we describe the development and implementation of a national centralised oncology image database and discuss the central issues associated with large-scale image acquisition from heterogeneous sites.
The ability to collect fully annotated sets of images for research opens to door to a multitude of potential research opportunities that utilise the legacy images, such as quantitative image informatics. Medical imaging provides the ability to detect and localise many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in the anatomic, physiologic, biochemical or molecular process. Calculating quantitative imaging features from acquired images and using these to build computational models to investigate detection, prognosis, and classification.
June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
BDW16 London - Mishal Patel, NHS - Modernising Routine Breast Cancer Using Bi...Big Data Week
Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to able to harness this large influx of data is of paramount. Traditionally, the systematic collection of medical images for research from heterogeneous sites has not been commonplace within the NHS and is fraught with challenges including; data acquisition, storage, secure transfer and correct anonymisation. Here we describe the development and implementation of a national centralised oncology image database and discuss the central issues associated with large-scale image acquisition from heterogeneous sites.
The ability to collect fully annotated sets of images for research opens to door to a multitude of potential research opportunities that utilise the legacy images, such as quantitative image informatics. Medical imaging provides the ability to detect and localise many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in the anatomic, physiologic, biochemical or molecular process. Calculating quantitative imaging features from acquired images and using these to build computational models to investigate detection, prognosis, and classification.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
Artificial Intelligence Applications in Radiology, presentation by Dr Harrison Bai, Assistant Professor of Diagnostic Imaging, Warren Alpert Medical School, Brown University. His research interests focus on AI, machine learning, and computer vision as applied to medical image analysis. Dr Bai is an associate editor for the journal Radiology: Artificial Intelligence and is currently a principal investigator for an RSNA Research Scholar grant and an NIH grant. The AI Radiology Lab has various areas of work including COVID-19; Treatment response assessment on imaging (brain, TACE, lung, colorectal); Rapid diagnosis of large-vessel ischemic stroke, patient selection and outcome prediction; Tumor characterization on imaging; Infrastructure development; Federated learning; Image registration (CT-guided tumor ablation); Radiology reports natural language processing. The AI pipeline includes DIANA system, Diagnosis model, severity model and progression model across various automated features and the value proposition. One Technique for dealing with missing sequence and imaging artifact- Sequence dropout. Human-in-the-loop AI. In the short- to mid-term, the utilization of AI needs to be combined with human intervention and supervision. Active learning strategy – annotation. Treatment response evaluation on imaging. Automatic quality estimation to flag the failed cases for humans to review and/or edit. Human in the loop annotation. Automatic quality estimation. Federated learning. Semi-supervised and unsupervised learning. AWS NVIDIA Clara Train SDK using TensorFlow 1.14. Annotations vary across imaging sites. Share weights without sharing data. Domain shift – distribution difference between source data and target data leading to performance degradation.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
Pneumothorax Detection Using Deep Convolutional Neural NetworksMichael Sebetich
Pneumothorax (PTX), more commonly known as “collapsed lung”, is a potentially life-threatening condition in which air enters the cavity between a patient’s lung and chest wall, inhibiting their ability to breathe. If not treated in a timely manner, typically through the insertion of a chest tube, PTX can result in hypoxia (oxygen deprivation), neurological damage, and, in the worst case, death. Diagnosis of PTX is currently a manual process performed by a radiologist via inspection of a patient’s chest x-ray image. In this paper we explore the potential of Deep Convolutional Neural Networks (CNNs) to automatically diagnose PTX in chest x-rays with the hope of reducing diagnosis time of this condition. We also present heat mapping based on network activations as a novel technique to visualize its performance against individually classified images. The NIH ChestX-ray8 dataset, which is labeled and contains over 100,000 anonymized chest x-rays from 30,000 patients, was used to train a Deep CNN. The final trained CNN is comprised of 5 convolutional layers, 4 pooling layers and 3 dropout layers. This network has a prediction accuracy of 78.5% and a ROC of 0.86 on the validation dataset. These results are encouraging and indicate that with further development Deep Learning has the potential to be clinically useful for automated Pneumothorax detection
Stereotactic Radiosurgery and Radiotherapy of Brain Metastases Clinical White...Brainlab
Learn more: https://www.brainlab.com/iplan-rt
Brain metastases are a common manifestation of systemic cancer constituting as much as 30% of all intracranial malignant tumors. Each year, 15 to 30% of cancer patients develop brain metastases, yielding an incidence of over 100,000 patients in the US. Development of brain metastases leads directly to the patient’s death in the majority of cases.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Ed...Bernard Marr
It is predicted that artificial intelligence (AI) will transform many aspects of our life including healthcare and genomics. AI and machine learning have helped us to understand the genome of organisms and will potentially change the way we treat disease, determine effective drugs and edit genes.
Proteogenomic analysis of human colon cancer reveals new therapeutic opportun...Gul Muneer
We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
Artificial Intelligence Applications in Radiology, presentation by Dr Harrison Bai, Assistant Professor of Diagnostic Imaging, Warren Alpert Medical School, Brown University. His research interests focus on AI, machine learning, and computer vision as applied to medical image analysis. Dr Bai is an associate editor for the journal Radiology: Artificial Intelligence and is currently a principal investigator for an RSNA Research Scholar grant and an NIH grant. The AI Radiology Lab has various areas of work including COVID-19; Treatment response assessment on imaging (brain, TACE, lung, colorectal); Rapid diagnosis of large-vessel ischemic stroke, patient selection and outcome prediction; Tumor characterization on imaging; Infrastructure development; Federated learning; Image registration (CT-guided tumor ablation); Radiology reports natural language processing. The AI pipeline includes DIANA system, Diagnosis model, severity model and progression model across various automated features and the value proposition. One Technique for dealing with missing sequence and imaging artifact- Sequence dropout. Human-in-the-loop AI. In the short- to mid-term, the utilization of AI needs to be combined with human intervention and supervision. Active learning strategy – annotation. Treatment response evaluation on imaging. Automatic quality estimation to flag the failed cases for humans to review and/or edit. Human in the loop annotation. Automatic quality estimation. Federated learning. Semi-supervised and unsupervised learning. AWS NVIDIA Clara Train SDK using TensorFlow 1.14. Annotations vary across imaging sites. Share weights without sharing data. Domain shift – distribution difference between source data and target data leading to performance degradation.
Description of Different Phases of Brain Tumor Classificationasclepiuspdfs
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
Pneumothorax Detection Using Deep Convolutional Neural NetworksMichael Sebetich
Pneumothorax (PTX), more commonly known as “collapsed lung”, is a potentially life-threatening condition in which air enters the cavity between a patient’s lung and chest wall, inhibiting their ability to breathe. If not treated in a timely manner, typically through the insertion of a chest tube, PTX can result in hypoxia (oxygen deprivation), neurological damage, and, in the worst case, death. Diagnosis of PTX is currently a manual process performed by a radiologist via inspection of a patient’s chest x-ray image. In this paper we explore the potential of Deep Convolutional Neural Networks (CNNs) to automatically diagnose PTX in chest x-rays with the hope of reducing diagnosis time of this condition. We also present heat mapping based on network activations as a novel technique to visualize its performance against individually classified images. The NIH ChestX-ray8 dataset, which is labeled and contains over 100,000 anonymized chest x-rays from 30,000 patients, was used to train a Deep CNN. The final trained CNN is comprised of 5 convolutional layers, 4 pooling layers and 3 dropout layers. This network has a prediction accuracy of 78.5% and a ROC of 0.86 on the validation dataset. These results are encouraging and indicate that with further development Deep Learning has the potential to be clinically useful for automated Pneumothorax detection
Stereotactic Radiosurgery and Radiotherapy of Brain Metastases Clinical White...Brainlab
Learn more: https://www.brainlab.com/iplan-rt
Brain metastases are a common manifestation of systemic cancer constituting as much as 30% of all intracranial malignant tumors. Each year, 15 to 30% of cancer patients develop brain metastases, yielding an incidence of over 100,000 patients in the US. Development of brain metastases leads directly to the patient’s death in the majority of cases.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
Tumor Detection Based On Symmetry InformationIJERA Editor
Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can benoticeable too much. Structural and functional asymmetry in the human brain and nervous system is reviewed in a historical perspective. Brainasymmetry is one of such examples, which is a difference in size or shape, or both. Asymmetry analysis of brain has great importance because itis not only indicator for brain cancer but also predict future potential risk for the same. In our work, we have concentrated to segment theanatomical regions of brain, isolate the two halves of brain and to investigate each half for the presence of asymmetry of anatomical regions inMRI.
The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Ed...Bernard Marr
It is predicted that artificial intelligence (AI) will transform many aspects of our life including healthcare and genomics. AI and machine learning have helped us to understand the genome of organisms and will potentially change the way we treat disease, determine effective drugs and edit genes.
Proteogenomic analysis of human colon cancer reveals new therapeutic opportun...Gul Muneer
We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.
Now a day’s, pharma research is facing challenges in
deciphering molecular understanding of disease initiation,
progress and establishment as well as performance
assessment of drug molecule on such phases of disease
development. Emerging of next generation sequencing
bases molecular tools were found to be a key method for
creating genome wide genomics landscape of gene
mutations, gene expression and gene regulation events.
Although NGS is a powerful tool for molecular research but
same time it have its own technical challenges. Few major
challenges of NGS based pharmacogenomics is
summarized below
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.
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In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure ran a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering. The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers discuss the technologies that made this leap possible
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.
Similar to Integrative analysis of medical imaging and omics (20)
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
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.
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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
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
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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
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
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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!
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
7. INTRODUCTION
Imaging as a part of human data
ConcatenatingFeatures
IntegrationAlgorithms
• Diagnosis
• Management Plan
AbnormalNormal
Integrated biomarker
RiskatDeath
Human
Cannot Do!
8. INTRODUCTION
Imaging
• Spatial Information
• Good algorithms:
e.g. Convolutional Neural Network
• Limited in very-high dimensional
multiplexing.
Omics data
• High-dimensional data (>10,000 molecules)
• Biological pathways/networks
• Limited in spatial information
• (Sometimes) Invasive
11. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
FDG
Uptake
Aggressiveness Vasculature
and Perfusion
Inflammation Necrosis
and
Apoptosis
What causes variable FDG uptake
according to systemic analysis?
12. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
Macro-level glucose metabolism
= Sum of metabolism of all members of tumor
Na KJ and Choi H, J Nucl Med 2018
13. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
Image-based metabolism parameters
&
Transcriptome networks (WGCNA) Na KJ and Choi H, J Nucl Med 2018
14. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
FDG PET data from
TCIA (n = 23)
RNA-seq data from TCGA
(n = 490 / 5827 genes)
Subjects: HNSCC
Na KJ and Choi H, J Nucl Med 2018
15. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
Gene-gene correlation
WGCNA: Weighted gene correlation network analysis
9 gene network modules from HNSCC
Na KJ and Choi H, J Nucl Med 2018
16. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
r
Na KJ and Choi H, J Nucl Med 2018
Blue module
Regulation of cell activation
Regulation of leukocyte activation
Regulation of lymphocyte activation
Leukocyte cell-cell adhesion
Positive regulation of cell activation
17. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
Na KJ and Choi H, J Nucl Med 2018
r
• FDG uptake
correlated with
immune cell enrichments
• Inverse correlation with
M2 macrophage
18. Integrative Analysis of PET and Omics
Correlative Analysis of FDG PET and Transcriptome
r
• FDG uptake
correlated with
immune cell enrichments
• Inverse correlation with
M2 macrophage
Na KJ and Choi H, J Nucl Med 2018
Chang CH, et al. Cell 2015
19. Integrative Analysis of PET and Omics
Integrative usage of imaging and omics information
Microenvironment
Cell Types
Tumor glucose metabolism
TMECellTypes
Glucose Metabolism
FDG
PET
RNAseq
Choi H and Na KJ, Theranostics 2018
20. Integrative Analysis of PET and Omics
Integrative usage of imaging and omics information
Microenvironment Cell TypesTumor glucose metabolism
FDG PET RNAseq
TCGA LUAD (n = 490)TCIA FDG PET (n = 17)
To exploit all the data
Tumor metabolism
by FDG-PET
Gene networks
related to tumor
metabolism
Neural
Network
Tumor metabolism
prediction model
Model made by independent data
using combined GEO and other TCIA data
21. Integrative Analysis of PET and Omics
Integrative usage of imaging and omics information
Choi H and Na KJ, Theranostics 2018
Expressiondata
Tumor metabolism
(maximum SUV)
Encoder
Decoder
HiddenFeatures
626 64 1
Supervised Training
(n = 20)
Unsupervised Training
(n = 226)
Dimensions
Semisupervised learning
Input: 626 gene expression data
Output: Predicting glucose metabolism (estimated by FDG PET)
22. Integrative Analysis of PET and Omics
Integrative usage of imaging and omics information
Choi H and Na KJ, Theranostics 2018
TMECelltypes
Tumors
Glucose
Metabolism
Microenvironment cell types and metabolism
23. Integrative Analysis of PET and OmicsTumorMetabolismIndex
Clusters
Clusters
ImmuneScore
Clusters by TME cell subpopulations
Tumor metabolism
Clusters
Subtypes
ImmuneScores
Choi H and Na KJ, Theranostics 2018
24. Integrative Analysis of PET and Omics
Microenvironment
Cell Types
Tumor glucose metabolism
Low immune cells
Poor Prognosis
High immune cells
CD8+ Tcells, M1 macrophage
Good Prognosis
Low metabolism
Mast cell, CD4+ Tcm
Good Prognosis
High metabolism
Treg cell
Poor Prognosis
ImmuneCells
Glucose Metabolism
Integrative usage of imaging and omics information
Choi H and Na KJ, Theranostics 2018
26. Perspectives of integration of imaging and omics
Future integrative biomarker
Current researches
Correlation
Future Direction
Integrative model
for disease prediction,
And status evaluation
27. Perspectives of integration of imaging and omics
AD & NC
MCI-converter & non-converter
FDG and amyloid PET to predict future cognitive decline
Choi H and Jin KH MIT Tech Review 2017;
Behav Brain Res 2018
f p(Alzheimer|X)
Predictive biomarker for
Future cognitive decline
Image-based Biomarker
28. Perspectives of integration of imaging and omics
Omics-based Biomarker
Low risk group
High risk group
Deep learning-based lung cancer
risk score
Choi H, et al. Biomed Res Int. 2017
29. Perspectives of integration of imaging and omics
Omics-based Biomarker Image-based Biomarker
Image
Features
Transcriptome
Features
Merging Features
Integrated risk stratification model
30. Perspectives of integration of imaging and omics
Insights for new biomarkers
Omics-based analysis
New ideas for
clinically useful
imaging biomarkers
EGFR mutation
anti-EGFR imaging
Single-target era
Multi-omics era
Specific molecular networks
Complementary role of imaging
31. Perspectives of integration of imaging and omics
Empirical
-based
Evidence
-based
Data
-driven
Data integration
Imaging
Genome
Transcriptome
Proteome
Phenome
Clinical Setting
