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From Home to Hospital: Digitisation of Healthcare.

Microsoft with RingMD, Oneview Healthcare, Vital Images, Aruba, and Clinic to Cloud: The Ecosystem of Healthcare Solutions Providers in Asia.

Data Helps in Improving Nursing Practice, Making Better Decisions.

Launch of Asian Branch for QuintilesIMS Institute.

SINGAPORE – Singapore eHealth Innovations Summit Announces the First EMRAM Stage 7 Hospital in Singapore and Emphasized Technology as Transformative Agent in Specialty Functions.

TAIWAN – Health2Sync Strategically Partners with Taiwan's Ministry of Health and Welfare in Asia's First Government Supported Online Diabetes Care Program.

UNITED STATES – Scientists Identify Protein Involved in Restoring Effectiveness of Common Treatment for Breast Cancer.

UNITED STATES – Scientists Reveal How Signals from Pathogenic Bacteria Reach Danger Sensors of Cells.

UNITED STATES – Scientists Find New Path in Brain to Ease Depression.

UNITED STATES – Tips for Living a Heart Healthy Lifestyle.

CANADA – Review Suggests Eating Oats Can Lower Cholesterol as Measured by a Variety of Markers.

SOUTH KOREA – CSA Group Opens Highly Advanced Electro - Medical Laboratory in Seoul.

AUSTRALIA – Cynata’s Technology Significant Efficacy in Preclinical Asthma Study.

INDIA – Essilor Launches ‘Love to See Change’ Campaign to Educate People about Need to Preserve Visual Health.

This work demonstrates the use of cluster analysis in detecting fair and unbiased novel discoveries. Given a sample population of elective spinal fusion patients, we identify two overarching subgroups driven by insurance type. The Medicare group, associated with lower socioeconomic status, exhibited an over-representation of negative risk factors. The findings provide a compelling depiction of the interwoven socioeconomic and racial disparities present within the healthcare system, highlighting their consequential effects on health inequalities. The results are intended to guide design of fair and precise machine learning models based on intentional integration of population stratification.

A pathway is a collection of two or more proteins/molecules connected by their interactions within and around a cell. We study the informatics and evolutionary issues of pathways. Similar to the definition of homology in the comparison of nucleotide and protein sequences, we define homologous pathways as pathways that are evolved from the same ancestral pathway. We first present a survey of existing pathway databases and discuss their format of pathway representation. Then, our pathway representation, the SLIPR format, is presented. It is a semilinear graphic representation of nodes (proteins) and modes (interactions). Pathways in SLIPR format enable pathway comparisons for evolutionary relationship and large-scale pathway database searches. We also discuss how one can map out orthologous pathways, achieving a predictive power on functional assignment of novel genes, once the pathway is understood well-enough in a closely-related species.

Two worldwide events opened novel reflections in a highly vast scientific literature concerning a universal concept like that of “data”, namely as the results of observations, either experimental or human-mind generated: the “Big Data” and the universal use of informatics, related to each other and both in an exponential increase.

The capacity and speed of modern computers allow us to obtain such immense amounts of data, both from experimental setups or from the elaboration of algorithms, either human-built or through AI. Their handling too is necessarily operated via informatics means, considered distinct from mathematical means. Apparently, these facts have disconnected data evaluation from traditional fields, not only science but also the way to make decisions based on them.

This paper contains reflections on the properties and meaning of the data in themselves, specifically as the vehicle of information almost universally necessary to make decisions, the latter being a frame that is mixed and often prevalent in the literature about big data.

Given the complexity and multifactorial nature of Alzheimer’s disease, investigating potential drug-gene targets is imperative for developing effective therapies and advancing our understanding of the underlying mechanisms driving the disease. We present an explainable ML model that integrates the role and impact of gene interactions to drive the genomic variant feature selection. The model leverages both the Alzheimer’s knowledge base and the Drug-Gene interaction database (DGIdb) to identify a list of biologically plausible novel gene-drug targets for further investigation. Model validation is performed on an ethnically diverse study sample obtained from the Alzheimer’s Disease Sequencing Project (ADSP), a multi-ancestry multi-cohort genomic study. To mitigate population stratification and spurious associations from ML analysis, we implemented novel data curation methods. The study outcomes include a set of possible gene targets for further functional follow-up and drug repurposing.