Severe obesity is often associated with inflammation and insulin resistance (IR), which expected to increase the risks of mortality and cancers. However, this relationship remains controversial, and it's unclear whether healthy lifestyles can mitigate these risks. The independent and joint associations of severe obesity (body mass index ≥ 35 m/kg), inflammation (C-reactive protein > 10 mg/L and systemic inflammation markers > 9th decile), and IR surrogates with the risks of all-cause mortality and all-site cancers, were evaluated in 163,008 participants from the UK Biobank cohort.
View Article and Find Full Text PDFBackground: Modifiable risk factors are important for prevention of age-related cognitive decline. Prior research has linked both physical activity (PA) and sleep with better memory outcomes. To better understand their potential synergistic effects, we examined independent and interactive effects of actigraphy-based PA and total sleep time on cognitive functioning in cognitively unimpaired older adults.
View Article and Find Full Text PDFBackground: Fatty liver disease may be associated with increased risks of intrahepatic and extrahepatic cancers. Our objective was to investigate associations between new subcategories of steatotic liver disease (SLD) recently proposed by nomenclature consensus group and cancer risk.
Methods: A total of 283 238 participants from the UK Biobank were included.
Chimeric antigen receptor T cells (CAR T cells) with T stem (T) cell-like phenotypic characteristics promote sustained antitumor effects. We performed an unbiased and automated high-throughput screen of a kinase-focused compound set to identify kinase inhibitors (KIs) that preserve human T cell-like CAR T cells. We identified three KIs, UNC10225387B, UNC10225263A and UNC10112761A, that combined in vitro increased the frequency of CD45RACCR7TCF1 T cell-like CAR T cells from both healthy donors and patients with cancer.
View Article and Find Full Text PDFAddressing heavy metal contamination in water bodies is a critical concern for environmental scientists. Traditional detection methods are often complex and costly. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have shown significant potential in analytical chemistry.
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