The promise of digital healthcare technologies

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies

CHMP1A encodes an essential regulator of BMI1-INK4A in cerebellar development

Charged multivesicular body protein 1A (CHMP1A; also known as chromatin-modifying protein 1A) is a member of the ESCRT-III (endosomal sorting complex required for transport-III) complex but is also suggested to localize to the nuclear matrix and regulate chromatin structure. Here, we show that loss-of-function mutations in human CHMP1A cause reduced cerebellar size (pontocerebellar hypoplasia) and reduced cerebral cortical size (microcephaly). CHMP1A-mutant cells show impaired proliferation, with increased expression of INK4A, a negative regulator of stem cell proliferation. Chromatin immunoprecipitation suggests loss of the normal INK4A repression by BMI in these cells. Morpholino-based knockdown of zebrafish chmp1a resulted in brain defects resembling those seen after bmi1a and bmi1b knockdown, which were partially rescued by INK4A ortholog knockdown, further supporting links between CHMP1A and BMI1-mediated regulation of INK4A. Our results suggest that CHMP1A serves as a critical link between cytoplasmic signals and BMI1-mediated chromatin modifications that regulate proliferation of central nervous system progenitor cells

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

Ten Emerging Trends in the Epidemiology of Diabetic Retinopathy

10.1080/09286586.2016.1193618OPHTHALMIC EPIDEMIOLOGY234209-22

Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study.

10.1038/s41746-019-0097-xNPJ digital medicine2124

Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

10.1212/WNL.0000000000012226NEUROLOGY974E369-E37

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

10.1016/s2589-7500(20)30063-7The Lancet Digital Health26e295-e30

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings