Advances in Bioengineering and Biomedical Science Research

2020 ◽  
Keyword(s):  
Science Research ◽  
Biomedical Science

Oxford Handbook of Medical Sciences

2021 ◽  
Keyword(s):  
Clinical Medicine ◽  
System Development ◽  
Science Research ◽  
Biomedical Science ◽  
Cell Physiology ◽  
Biomedical Sciences ◽  
Medical Sciences ◽  
Allied Health Professions ◽  
Endocrine Organs ◽  
Scientific Principles

The Oxford Handbook of Medical Sciences is written by biomedical scientists and clinicians to be the definitive guide to the fundamental scientific principles that underpin medicine and the biomedical sciences. It provides a clear and easily digestible account of basic cell physiology, biochemistry, and molecular and medical genetics, followed by chapters integrating the traditional pillars of biomedicine (anatomy, physiology, biochemistry, pathology, and pharmacology) for each of the major systems and processes of the human body: nerve and muscle, musculoskeletal system, respiratory and cardiovascular systems, urinary system, digestive system, endocrine organs, reproductive system, development from fertilization to birth, neuroanatomy and neurophysiology, infection and immunity, and the growth of tissues and organs. Also included are chapters on medicine and society and techniques used in biomedical science research. In its third edition, the Oxford Handbook of Medical Sciences is now fully illustrated in colour, and cross-referenced to the Oxford Handbook of Clinical Medicine, tenth edition, Oxford Handbook of Clinical Specialities, eleventh edition, and Oxford Handbook of Practical Drug Therapy, second edition. Its concise writing style makes it an invaluable source of information for practitioners and students in medicine, biomedical sciences, and the allied health professions.

scholarly journals A review of robust regression in biomedical science research

2020 ◽  
Vol 16 (5) ◽  
pp. 1267-1269
Author(s):  
Sacha Varin ◽  
Demosthenes B. Panagiotakos
Keyword(s):  
Robust Regression ◽  
Science Research ◽  
Biomedical Science

scholarly journals Centers for Mendelian Genomics: A decade of facilitating gene discovery

2021 ◽  
Author(s):  
Samantha M. Baxter ◽  
Jennifer E. Posey ◽  
Nicole J. Lake ◽  
Nara Lygia Sobreira ◽  
Jessica X. Chong ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Science ◽  
Science Research ◽  
Genome Project ◽  
Genomic Research ◽  
Biomedical Science ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Wide Range ◽  
Access To Data

Mendelian disease genomic research has undergone a massive transformation over the last decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration. Over the last 10 years, the NIH-supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution. We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Mendelian genomic research extends beyond generating lists of gene-phenotype relationships, it includes developing tools, training the larger community to use these tools and approaches, and facilitating collaboration through data sharing. Thus, the CMGs have also focused on creating resources, tools, and training for the larger community to foster the understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into AnVIL (NHGRI's Genomic Data Science Analysis, Visualization, and Informatics Lab-Space), sharing candidate genes through Matchmaker Exchange (MME) and the CMG website, and sharing variants in Geno2MP and VariantMatcher. The research genomics output remains exploratory with evidence that thousands of disease genes, in which variant alleles contribute to disease, remain undiscovered, and many patients with rare disease remain molecularly undiagnosed. Strengthening communication between research and clinical labs, continued development and sharing of knowledge and tools required for solving previously unsolved cases, and improving access to data sets, including high-quality metadata, are all required to continue to advance Mendelian genomics research and continue to leverage the Human Genome Project for basic biomedical science research and clinical utility.

“Mobile Nurse” Platform for Ubiquitous Medicine

2007 ◽  
Vol 46 (02) ◽  
pp. 130-134 ◽  
Author(s):  
K. Yoshiuchi ◽  
M. Sone ◽  
T. Ishikawa ◽  
H. Kikuchi ◽  
H. Kumano ◽  
...  
Keyword(s):  
Signs And Symptoms ◽  
Science Research ◽  
Biomedical Science ◽  
Medical Professionals ◽  
Clinical Nurses ◽  
Dynamic Setting ◽  
Disease Mechanisms ◽  
Intelligent Interaction ◽  
Integral Data ◽  
Disease Sign

Summary Objectives : We introduce “Mobile Nurse" (MN) - an emerging platform for the practice of ubiquitous medicine. Methods : By implementing in a dynamic setting of daily life the patient care traditionally provided by the clinical nurses on duty, MN aims at integral data collection and shortening the response time to the patient. MN is also capable of intelligent interaction with the patient and is able to learn from the patient's behavior and disease sign evaluation for improved personalized treatment. Results : In this paper, we outline the most essential concepts around the hardware, software and methodological designs of MN. We provide an example of the implementation, and elaborate on the possible future impact on medical practice and biomedical science research. Conclusions : The main innovation of MN, setting it apart from current tele-medicine systems, is the ability to integrate the patient's signs and symptoms on site, providing medical professionals with powerfultools to elucidate disease mechanisms, to make proper diagnoses and to prescribe treatment.

scholarly journals Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

2018 ◽  
Author(s):  
Haohan Wang ◽  
Xiang Liu ◽  
Yifeng Tao ◽  
Wenting Ye ◽  
Qiao Jin ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Text Mining ◽  
Genetic Association ◽  
Complex Traits ◽  
Science Research ◽  
Biomedical Science ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Genetic Association Database ◽  
Amount Of Knowledge

The increasing amount of scientific literature in biological and biomedical science research has created a challenge in the continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this chal-lenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the flexibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative arti-ficial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current pri-mary task is to build the genetic association database between genes and complex traits of the human. Our contributions in this paper are three-fold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effec-tiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.

scholarly journals RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project

2020 ◽  
Vol 19 ◽  
pp. 117693512092249
Author(s):  
Lianbo Yu
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Science Research ◽  
Biomedical Science ◽  
Systematic Evaluation ◽  
Rna Seq ◽  
Systemic Analysis ◽  
Sequencing Quality ◽  
Community Effort ◽  
Sequencing Platforms

With the widespread RNA-seq applications of different sequencing platforms in biomedical science research in recent years, a systematic evaluation of RNA-seq data quality is crucial and timely. The Sequencing Quality Control (SEQC) project is a large-scale community effort for assessing the performance of RNA-seq technology across different platforms and multiple laboratories, where reference RNA samples with multiple replicates were sequenced at 12 laboratories using 3 sequencing platforms. Different from the SEQC project, we performed an independent and comprehensive analysis of RNA-seq data of the SEQC project to assess sequencing reproducibility across platforms, sequencing sites, sample replicates, and FlowCells, respectively. With the employment of graphical tools and statistical models, our systemic analysis supports a distinctive conclusion that reproducibility across platforms and sequencing sites are not acceptable, whereas reproducibility across sample replicates and FlowCells are acceptable.

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