Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study.

Background: Post-stroke cognitive impairment (PSCI) is a serious complication of stroke that warrants prompt detection and management. Consequently, the development of a diagnostic prediction model holds clinical significance.

Objective: Machine learning algorithms were employed to identify crucial variables and forecast PSCI occurrence within 3-6 months following acute ischemic stroke (AIS).

Bioinformatics analysis of diagnostic biomarkers for Alzheimer's disease in peripheral blood based on sex differences and support vector machine algorithm.

Background: The prevalence of Alzheimer's disease (AD) varies based on gender. Due to the lack of early stage biomarkers, most of them are diagnosed at the terminal stage. This study aimed to explore sex-specific signaling pathways and identify diagnostic biomarkers of AD.

[Expression of c/ebpα gene in MDS and its clinical significance].

This study was aimed to investigate the expression of CCAAT/enhancer binding protein alpha gene (c/ebpα) in patients with myelodysplastic syndromes (MDS) and to explore the significance of c/ebpα in pathogenesis and progression of MDS. Real time quantitative reverse transcriptase polymerase chain reaction (RQ-PCR) method was used to detect the expression level of c/ebpα mRNA in bone marrow mononuclear cells (BMMNC) of 33 patients with MDS and 14 normal controls. The results showed that the expression level of c/ebpα mRNA in low-risk and high-risk MDS was significantly lower than that of normal controls (p < 0.