Development and Validation of an Interpretable Machine Learning Model for Predicting Adverse Clinical Outcomes in Placenta Accreta Spectrum: A Multicenter Study.

Rationale And Objectives: Placenta accreta spectrum (PAS) is a serious perinatal complication. Accurate preoperative identification of patients at high risk for adverse clinical outcomes is essential for developing personalized treatment strategies. This study aimed to develop and validate a high-performance, interpretable machine learning model that integrates MRI morphological indicators and clinical features to predict adverse outcomes in PAS, and to build an online prediction tool to enhance its clinical applicability.

Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti- agent targeting cellular iron homeostasis.

Introduction: Candidiasis, mainly caused by , poses a serious threat to human health. The escalating drug resistance in and the limited antifungal options highlight the critical need for novel therapeutic strategies.

Methods: We evaluated 12 machine learning models on a self-constructed dataset with known anti- activity.

Machine learning-based identification of telomere-related gene signatures for prognosis and immunotherapy response in hepatocellular carcinoma.

Telomere in cancers shows a main impact on maintaining chromosomal stability and unlimited proliferative capacity of tumor cells to promote cancer development and progression. So, we targeted to detect telomere-related genes(TRGs) in hepatocellular carcinoma (HCC) to develop a novel predictive maker and response to immunotherapy. We sourced clinical data and gene expression datasets of HCC patients from databases including TCGA and GEO database.

Transcriptome Analysis on the Quality of in Different Soil Moisture Conditions at Harvesting Stage.

is a traditional Chinese tonic herb. Its main medicinal components are secondary metabolites such as flavonoids and flavonol glycosides, but the biosynthetic mechanism is still unclear. Moisture conditions are a key environmental factor affecting medicinal components during harvesting.

The causality between CD8NKT cells and CD16CD56 on NK cells with hepatocellular carcinoma: a Mendelian randomization study.

Background: Hepatocellular carcinoma (HCC), which is featured with high morbidity and mortality worldwide, is a primary malignant tumor of the liver. Recently, there is a wealth of supporting evidence revealing that NK cell-related immune traits are strongly associated with the development of HCC, but the causality between them has not been proven.

Methods: Two-sample Mendelian randomization (MR) study was performed to probe the causal correlation between NK cell-related immune traits and HCC.