Publications by authors named "W G Xia"
Background: Chromosomal instability (CIN), a hallmark of cancer, is commonly linked to poor prognosis in high-grade prostate cancer (PCa). Paradoxically, excessively high levels of CIN may impair cancer cell viability. Consequently, understanding how tumours adapt to CIN is critical for identifying novel therapeutic targets.
View Article and Find Full Text PDFAims: Previous studies have shown that eGDR and TyG, as indicators of insulin resistance (IR), were key risk factors for cardiovascular disease (CVD). Our study further explored the relationship between eGDR change and new-onset CVD, and compared the predictive value of eGDR change, eGDR and TyG.
Materials And Methods: A total of 2895 participants without CVD at baseline from the China Health and Retirement Longitudinal Study (CHARLS) were included, using K-means clustering and cumulative eGDR to measure eGDR change between 2012 and 2015.
Background: Metabolic health is closely related to testosterone levels, and the cardiometabolic index (CMI) is a novel metabolic evaluation metric that encompasses obesity and lipid metabolism. However, there is currently a lack of research on the relationship between CMI and testosterone, which is the objective of this study.
Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2011 to 2016.
Article Synopsis
- The study examines how effective machine learning, particularly the Random Forest Classifier, is in predicting thyroid-specific autoantibodies in patients with Primary Sjogren's syndrome (pSS) based on clinical data.
- A total of 96 pSS patients were analyzed through thyroid function tests to categorize them by the presence of autoantibodies, leading to the exploration of various risk factors using four different machine learning algorithms.
- The Random Forest Classifier yielded the best results (AUC = 0.755), highlighting age, IgG levels, complement C4, and dry mouth as significant predictors for autoimmune thyroiditis in these patients.
The shortwave infrared (SWIR) region is an ideal spectral window for next-generation bioimaging to harness improved penetration and reduced phototoxicity. SWIR spectral activity may also be accessed via supramolecular dye aggregation. Unfortunately, development of dye aggregation remains challenging.
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