Brain derived β-interferon is a potential player in Alzheimer's disease pathogenesis and cognitive impairment.

Background: Recent research has postulated that the activation of cGAS-STING-interferon signalling pathways could be implicated in the pathogenesis of Alzheimer's disease (AD). However, the precise types of interferons and related cytokines, both from the brain and periphery, responsible for cognitive impairment in patients with AD remain unclear.

Methods: A total of 131 participants (78 [59.

Incidence of hepatobiliary malignancies in primary sclerosing cholangitis: systematic review and meta-analysis.

Background And Aims: Primary sclerosing cholangitis (PSC) is a known risk factor for hepatobiliary malignancies. We conducted a systematic review and meta-analysis of published studies to determine the incidence and risk factors for hepatobiliary malignancies in people with PSC.

Methods: Pubmed and Embase databases were searched from inception to April 10, 2024 for cohort studies reporting data on the incidence of cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC), or gallbladder cancer (GBC) in PSC.

LED illumination-assisted Activation of Peroxydisulfate by Heterogeneous CuS under Alkaline Condition for Efficient Organic Pollutants Removal.

The copper-based materials were considered as promising catalysts for the activation of peroxydisulfate (PDS), but the study on the CuS-activated PDS under LED illumination and alkaline condition was little reported. In this work, CuS, a simple and readily available heterogeneous catalyst, was employed to enhance the activation of PDS under alkaline condition through LED illumination. The results indicated that under LED illumination, the degradation rate of tetracycline (TC) during the first 15 min was 3.

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods.

Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review.

Background: Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging.

Aims: As artificial intelligence (AI) may predict therapeutic responses, this systematic review aims to assess the performance and effectiveness of radiomics and AI-based models in predicting TACE outcomes in patients with HCC.