An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer.

Background: This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.

Methods: Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.

Risk factors analysis of hypokalemia after radical resection of esophageal cancer and establishment of a nomogram risk prediction model.

Objective: This study aimed to explore the risk factors of hypokalemia after radical resection of esophageal cancer (EC) and establish a nomogram risk prediction model to evaluate hypokalemia risk after esophagectomy. Thus, this study provides a reference for the clinical development of intervention measures.

Methods: Clinical data of EC patients who underwent radical surgery from January 2020 to November 2022 in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected.

The role of metagenomic next-generation sequencing in diagnosing and managing post-kidney transplantation infections.

Kidney transplantation (KT) is a life-saving treatment for patients with end-stage renal disease, but post-transplant infections remain one of the most significant challenges. These infections, caused by a variety of pathogens, can lead to prolonged hospitalization, graft dysfunction, and even mortality, particularly in immunocompromised patients. Traditional diagnostic methods often fail to identify the causative organisms in a timely manner, leading to delays in treatment and poorer patient outcomes.

Isolation, identification and characteristics of from diseased rainbow trout ().

is an opportunistic pathogen that can infect humans, animals and aquatic species, which is widely distributed in different aquatic environments and products. In recent years, with the rapid expansion of intensive aquaculture, the disease caused by has occurred. This study aims to understand the pathogenic characteristics of and provide scientific basis for the prevention and control of the epidemic.

Global foot-and-mouth disease risk assessment based on multiple spatial analysis and ecological niche model.

Foot-and-Mouth Disease is a highly contagious transboundary animal disease. FMD has caused a significant economic impact globally due to direct losses and trade restrictions on animals and animal products. This study utilized multi-distance spatial cluster analysis, kernel density analysis, directional distribution analysis to investigate the spatial distribution patterns of historical FMD epidemics.