Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis.

Developing a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.

Background: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy.

Objective: This study aims to employ machine learning algorithms to identify key indicators from the 20-item Modified Checklist for Autism in Toddlers, revised (M-CHAT-R) combining with ASD-related sociodemographic and environmental factors, to distinguish ASD from typically developing children.

Methods: Data from our prior validation study of the Chinese M-CHAT-R (August 2016-March 2017, n = 6,049 toddlers) were reviewed.

Integrating D-Dimer Thresholds into the Revised Caprini Risk Stratification to Predict Deep Vein Thrombosis Risk in Preoperative Knee Osteoarthritis Patients.

Introduction: Preoperative patients with knee osteoarthritis have a significantly increased risk of venous thromboembolism (VTE). While the Caprini risk assessment model offers some clinical guidance in predicting deep vein thrombosis (DVT), it has a relatively low predictive accuracy. Enhancing the model by integrating biomarkers, such as D-dimers, can potentially improve its accuracy.

Correlation between estimated glucose disposal rate, insulin resistance, and cardiovascular mortality among individuals with metabolic syndrome: a population-based analysis, evidence from NHANES 1999-2018.

Background: Estimated glucose disposal rate (eGDR), is an index of insulin resistance. It is intimately correlated with inflammation and endothelial dysfunction, both of which are contributory factors in the pathogenesis of cardiovascular disease (CVD) and premature mortality. This study aims to explore the correlation between eGDR and both all-cause and CVD-related mortality in adults with metabolic syndrome (MetS).

Engaging and supporting young adults in smoking cessation: Insights from a mobile-based cessation program in China.

Objective: Evidence-based smoking cessation treatments are underutilized by young adult smokers. This study explored young smokers' experiences with a mobile-based smoking cessation program that included a Quit & Win contest at a university in Zhuhai, China, aiming to identify key engagement and cessation mechanisms.

Methods: Twenty participants (aged 18-25 years) were selected through purposive sampling.