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1.
Ann N Y Acad Sci ; 1507(1): 70-83, 2022 01.
Article in English | MEDLINE | ID: covidwho-1673249

ABSTRACT

For many years, it was believed that the aging process was inevitable and that age-related diseases could not be prevented or reversed. The geroscience hypothesis, however, posits that aging is, in fact, malleable and, by targeting the hallmarks of biological aging, it is indeed possible to alleviate age-related diseases and dysfunction and extend longevity. This field of geroscience thus aims to prevent the development of multiple disorders with age, thereby extending healthspan, with the reduction of morbidity toward the end of life. Experts in the field have made remarkable advancements in understanding the mechanisms underlying biological aging and identified ways to target aging pathways using both novel agents and repurposed therapies. While geroscience researchers currently face significant barriers in bringing therapies through clinical development, proof-of-concept studies, as well as early-stage clinical trials, are underway to assess the feasibility of drug evaluation and lay a regulatory foundation for future FDA approvals in the future.


Subject(s)
Aging/genetics , Aging/metabolism , Congresses as Topic/trends , Longevity/physiology , Research Report , Autophagy/physiology , COVID-19/genetics , COVID-19/metabolism , COVID-19/mortality , Cardiovascular Diseases/genetics , Cardiovascular Diseases/metabolism , Cardiovascular Diseases/therapy , Humans , Metabolomics/methods , Metabolomics/trends , Nervous System Diseases/genetics , Nervous System Diseases/metabolism , Nervous System Diseases/therapy , Stem Cell Transplantation/methods , Stem Cell Transplantation/trends
2.
OMICS ; 25(11): 681-692, 2021 11.
Article in English | MEDLINE | ID: covidwho-1541502

ABSTRACT

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.


Subject(s)
Computational Biology/trends , Genomics/trends , Metabolomics/trends , Proteomics/trends , Animals , COVID-19 , Computer Graphics , Epigenomics/trends , Gene Expression Profiling/trends , Humans , Pharmacogenetics/trends , Publications , SARS-CoV-2 , Terminology as Topic
3.
Cytometry B Clin Cytom ; 100(1): 33-41, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1006421

ABSTRACT

Over a remarkably short period of time, a great deal of knowledge about severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection has been acquired, through the focused and cooperative effort of the international scientific community. Much has become known about how the immune response is coordinated to fight infection, and how it becomes dysregulated in severe disease. In this review, we take an in-depth look at the many immune features associated with the host response to SARS-CoV2, as well as those that appear to mark severe disease.


Subject(s)
COVID-19/diagnostic imaging , COVID-19/immunology , Flow Cytometry/methods , Fluorescent Antibody Technique/methods , SARS-CoV-2/immunology , Biomarkers/analysis , COVID-19/pathology , COVID-19/therapy , Chemokines/analysis , Chemokines/metabolism , Cytokines/analysis , Cytokines/metabolism , Fluorescent Antibody Technique/trends , Host-Pathogen Interactions/immunology , Humans , Immunity/physiology , Metabolomics/methods , Metabolomics/trends , Risk Assessment , Severity of Illness Index
4.
Hum Genomics ; 14(1): 35, 2020 10 02.
Article in English | MEDLINE | ID: covidwho-810348

ABSTRACT

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.


Subject(s)
Coronavirus Infections/genetics , Diabetes Mellitus/genetics , Neoplasms/genetics , Pneumonia, Viral/genetics , Precision Medicine/trends , COVID-19 , Cardiomyopathies , Coronavirus Infections/epidemiology , Data Analysis , Diabetes Mellitus/epidemiology , Genomics/trends , Humans , Metabolomics/trends , Neoplasms/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Proteomics/trends
5.
Immunol Cell Biol ; 99(2): 168-176, 2021 02.
Article in English | MEDLINE | ID: covidwho-751690

ABSTRACT

Big data has become a central part of medical research, as well as modern life generally. "Omics" technologies include genomics, proteomics, microbiomics and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The COVID-19 pandemic has shown how quickly information can be generated and analyzed using such approaches, but also showed its limitations. This review will look at how "omics" has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for "precision" or "personalized" medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly, there will be increasing use of large data sets from traditional (e.g. tumor samples, patient genomics) and nontraditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the health-care sector over the coming decade to integrate these resources in an effective, practical and ethical way.


Subject(s)
Genomics/trends , Metabolomics/trends , Precision Medicine/trends , /trends , Artificial Intelligence/trends , COVID-19/epidemiology , Genomics/methods , Humans , Medical Oncology/methods , Medical Oncology/trends , Metabolomics/methods , Pandemics , Precision Medicine/methods , Proteomics/methods , Proteomics/trends , Time Factors , /methods
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