Identify characteristics of Vietnamese oral squamous cell carcinoma patients by machine learning on transcriptome and clinical-histopathological analysis.

Background/purpose: Oral squamous cell carcinoma (OSCC) is notorious for its low survival rates, due to the advanced stage at which it is commonly diagnosed. To enhance early detection and improve prognostic assessments, our study harnesses the power of machine learning (ML) to dissect and interpret complex patterns within mRNA-sequencing (RNA-seq) data and clinical-histopathological features.

Materials And Methods: 206 retrospective Vietnamese OSCC formalin-fixed paraffin-embedded (FFPE) tumor samples, of which 101 were subjected to RNA-seq for classification based on gene expression.

Poorer objective but not subjective driving performance in drivers vulnerable to sleep loss effects during extended wake.

Sleepiness-related errors are a leading cause of driving accidents, requiring drivers to effectively monitor sleepiness levels. However, there are inter-individual differences in driving performance after sleep loss, with some showing poor driving performance while others show minimal impairment. This research explored if there are differences in self-reported sleepiness and driving performance in healthy drivers who exhibited vulnerability or resistance to objective driving impairment following extended wakefulness.

Association between underlying health conditions and long COVID among non-hospitalized and hospitalized individuals as modified by health literacy: A multi-center study.

Objectives: We investigated the effect modification of health literacy (HL) in ameliorating the negative impact of underlying health conditions (UHC) on long COVID among non-hospitalized and hospitalized survivors.

Study Design: An online cross-sectional study was conducted in Vietnam from December 2021 to October 2022.

Methods: A sample of 4507 participants recruited from 18 hospitals and health centers were those aged 18 or older, had contracted COVID-19 for at least 28 days, and were not in the acute phase of reinfection.

Country-specific key lifestyle factors and health outcomes for resource allocation in the general population: a network analysis across 29 countries.

Background: We aimed to identify the central lifestyle, the most impactful among lifestyle factor clusters; the central health outcome, the most impactful among health outcome clusters; and the bridge lifestyle, the most strongly connected to health outcome clusters, across 29 countries to optimise resource allocation for local holistic health improvements.

Methods: From July 2020 to August 2021, we surveyed 16 461 adults across 29 countries who self-reported changes in 18 lifestyle factors and 13 health outcomes due to the pandemic. Three networks were generated by network analysis for each country: lifestyle, health outcome, and bridge networks.

Social network analysis as a new tool to measure academic impact of physicians.

Introduction: H-index is a widely used metric quantifying a researcher's productivity and impact based on an author's publications and citations. Though convenient to calculate, h-index fails to incorporate collaborations and interrelationships between physicians into its assessment of academic impact, leading to limited insight into grouped networks. We present social network analysis as a tool to measure relationships between physicians and quantify their academic impact.