Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.

Background: Electronic medical records (EMR)-trained machine learning models have the potential in CVD risk prediction by integrating a range of medical data from patients, facilitate timely diagnosis and classification of CVDs. We tested the hypothesis that unsupervised ML approach utilizing EMR could be used to develop a new model for detecting prevalent CVD in clinical settings.

Methods: We included 155,894 patients (aged ≥ 18 years) discharged between January 2014 and July 2022, from Xuhui Hospital, Shanghai, China, including 64,916 CVD cases and 90,979 non-CVD cases.

The interplay of long noncoding RNA HULC with microRNA-128-3p and their correlations with lipid level, stenosis degree, inflammatory cytokines, and cell adhesion molecules in coronary heart disease patients.

Background: Long noncoding RNA HULC (lnc-HULC) and its target microRNA-128-3p (miR-128-3p) regulate endothelial cell function, blood lipid level, and inflammatory cytokine production, which are involved in the pathogenesis of coronary heart disease (CHD). Based on the above information, this study intended to further investigate the correlation between lnc-HULC and miR-128-3p, as well as their clinical values for CHD management.

Methods: Totally, 141 CHD patients and 70 controls were enrolled.

A probability formula derived from serum indicators, age, and comorbidities as an early predictor of dementia in elderly Chinese people.

Introduction: Blood-based indicators are potentially economical and a safe method for screening a population for dementia, although their predictive values have not been unequivocally confirmed. The present study proposes a dementia prediction formula based on serum indicators and patient characteristics.

Methods: From January 2016 to December 2018, the data of elderly patients older than 60 years admitted to the Department of Neurology and Geriatrics in our hospital were retrospectively reviewed.

[Effect of smoking on the microRNAs expression in pneumoconiosis patients].

Objective: To investigate the effect of smoking on the microRNAs (miRNAs) expression in pneumoconiosis patients.

Methods: Real-time qPCR was used to measure the expression levels of miR-21, miR-200c, miR-16, miR-204, miR-206, miR-155, let-7g, miR-30b, and miR-192 in 36 non-smoking patients with pneumoconiosis and 38 smoking patients with pneumoconiosis, and the differences in expression levels between the two groups were evaluated by two-independent samples t-test.

Results: The expression of miR-192 in serum showed a significant difference between non-smoking and smoking pneumoconiosis patients (P < 0.