A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.

The role of sialidase Neu1 in respiratory diseases.

Neu1 is a sialidase enzyme that plays a crucial role in the regulation of glycosylation in a variety of cellular processes, including cellular signaling and inflammation. In recent years, numerous evidence has suggested that human NEU1 is also involved in the pathogenesis of various respiratory diseases, including lung infection, chronic obstructive pulmonary disease (COPD), asthma, and pulmonary fibrosis. This review paper aims to provide an overview of the current research on human NEU1 and respiratory diseases.

Effects of Mao tea from Nankun Mountain on nonalcoholic fatty liver disease in mice.

Non-alcoholic fatty liver disease (NAFLD) poses a significant health threat due to its potential progression to liver fibrosis, cirrhosis, and even liver cancer. Without proper management, NAFLD can lead to severe complications and significantly impact overall health and longevity. This study explores the potential anti-steatosis effects of Nankun Mountain Mao tea (MT) on hepatic lipid accumulation using both in vitro and in vivo models. In vitro experiments reveal that MT reduces lipid accumulation in hepatocytes and counteracts hepatic steatosis induced by palmitic acid and oleic acid. In vivo investigations on high-fat diet (HFD)-fed and high-fat, fructose, and cholesterol (HFFC)-fed mice demonstrate that MT administration alleviates hepatic steatosis by reducing lipid accumulation, enhancing liver function, and mitigating inflammation. Transcriptomic analyses unveil the molecular mechanisms underlying the impact of MT on lipid metabolism and inflammation. It turns out that MT inhibits de novo lipid synthesis and NF-κB pathway against NAFLD. Furthermore, target prediction analysis identifies potential bioactive components group (BCG) within MT that may contribute to its anti-steatosis properties. Validation studies on primary hepatocytes confirm the effectiveness of these bioactive components in diminishing lipid accumulation and inflammation, suggesting their role in the therapeutic efficacy of MT against NAFLD.

Comparative effectiveness of different exercise interventions for elderly patients with hip fracture: A systematic review and Bayesian network meta-analysis protocol of randomized controlled trials.

BACKGROUND: Exercise intervention (EI) is a promising and economical way for elderly patients with hip fracture, but the evidence regarding effective EIs remains fragmented and controversial, and it is unclear which type of exercise is optimal. The purpose of this Bayesian network meta-analysis (NMA) is to compare and rank the efficacy of various EIs in elderly patients with hip fracture. MATERIALS AND METHODS: A comprehensive literature search was performed using a systematic approach across various databases including Medline (via PubMed), CINAHL, CNKI, Web of Science, Wan Fang, Embase, VIP, Cochrane Central Register of Controlled Trials and CBM databases. The search encompasses all available records from the inception of each database until December 2022. The Inclusion literature comprises randomized controlled trials that incorporate at least one EI for elderly patients with hip fracture. We will assess the risk of bias of the studies in accordance with the Cochrane Handbook for Systematic Reviews of Interventions, and assess each evidence of outcome quality in accordance with the Grading of Recommendations Assessment, Development and Evaluation framework. The NMA will be performed by STATA 15.0 software and OpenBUGS version 3.2.3. The identification of publication bias will be accomplished through the utilization of a funnel plot. We will rank the EIs effects according to the cumulative ranking probability curve (surface under the cumulative ranking area, SUCRA). The primary outcomes will be hip function in elderly patients, and the secondary outcomes will be activities of daily living, walking capacity and balance ability of elderly patients. TRIAL REGISTRATION: PROSPERO registration number: CRD4202022340737.