The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.

Background: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers.

Diagnostic utility of LEF1 and β-catenin in WNT pathway tumors with CTNNB1 mutation.

Objective: This study aimed to compare the expression of lymphoid enhancer factor 1 (LEF1) and β-catenin in basal cell adenoma (BA), desmoid-type fibromatosis (DF), and pancreatic solid pseudopapillary neoplasm (SPN) to evaluate their diagnostic utility in tumors associated with the WNT/β-catenin signaling pathway harboring the mutation of CTNNB1 gene 3 exon.

Methods: Eighty tumor patients, including 26 BAs, 30 DFs, and 24 SPNs, were analyzed. Immunohistochemical staining was identified positive (nuclear staining of LEF1 and β-catenin in > 50% of tumor cells).

Triglyceride-glucose index predicts adverse cardiovascular events in patients with H-type hypertension combined with coronary heart disease: a retrospective cohort study.

Background: The triglyceride‒glucose index (TyG index) is a reliable surrogate for insulin resistance (IR) in individuals with type 2 diabetes mellitus and is associated with cardiovascular disease. Recent studies have reported that H-type hypertension is likewise a predictor of adverse events in patients with coronary heart disease (CHD). However, the relationship between the TyG index and prognosis in patients with H-type hypertension combined with CHD has not yet been reported.

Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks.

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.

pH-sensitive nano-drug delivery systems dual-target endothelial cells and macrophages for enhanced treatment of atherosclerosis.

Atherosclerosis (AS) is a chronic inflammatory disease characterized by vascular endothelial dysfunction. In the early stage of the disease, endothelial cell injury induces the infiltration of inflammatory macrophages, which secrete large amounts of inflammatory factors, further aggravating endothelial cell dysfunction and exacerbating the disease. Therefore, it is promising for co-targeting endothelial cells and macrophages further regulating the inflammatory microenvironment and endothelial cell function for effective treatment.