[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 < 0.05), and it decreased gradually in smoking patients with stage I and II pneumoconiosis. In the serum of all pneumoconiosis patients, the expression level of miR-16 was the highest, while the expression level of miR-204 was the lowest. CONCLUSION: Pneumoconiosis patients have differential expression of miRNAs in serum, and smoking has an effect on the miRNAs expression in pneumoconiosis patients.

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. Lnc-HULC and miR-128-3p in peripheral blood mononuclear cells were detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Serum inflammatory cytokines and cell adhesion molecules were further determined by enzyme-linked immunosorbent assay (ELISA) in CHD patients. RESULTS: Lnc-HULC was upregulated, while miR-128-3p was downregulated in CHD patients than in controls (both P < 0.001). The ROC curve further displayed that lnc-HULC (AUC: 0.906, 95% CI: 0.867-0.945) and miR-128-3p (AUC: 0.814, 95% CI: 0.756-0.873) had the potential of discriminating CHD patients from controls. Regarding the correlation between lnc-HULC and miR-128-3p, lnc-HULC was negatively associated with miR-128-3p in CHD patients (rs = - 0.307, P < 0.001), but this association was not observed in controls (rs = - 0.155, P = 0.199). Furthermore, it was discovered that upregulated lnc-HULC was associated with elevated blood lipid levels (TG, LDL-C), inflammatory cytokines (interleukin (IL)-1ß, IL-17A), cell adhesion molecules (VCAM-1), and Gensini score (all P < 0.05) in CHD patients. Meanwhile, miR-128-3p was negatively associated with blood lipid level (LDL-C), inflammatory cytokines (TNF-α, IL-1ß, IL-6), cell adhesion molecules (VCAM-1, ICAM-1), and Gensini score (all P < 0.05) in CHD patients. CONCLUSION: Lnc-HULC and its target miR-128-3p relate to lipid level, stenosis degree, inflammatory cytokines, and cell adhesion molecules in CHD patients.

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. A multivariate logistic regression model was applied to verify the patients' characteristics and serum indicators associated with the risk of dementia. After receiver-operating characteristic (ROC) curve and area under the ROC curve (AUC) analyses, we propose a dementia prediction formula and cutoff values for the predictive ability of early dementia. RESULTS: Four thousand seven hundred twenty two elderly patients were enrolled, and the incidence of dementia was 12.0% (565). When patients had ≥8 comorbidities, their risk of developing dementia was 20 times higher than those without comorbidities. After multivariate regression analysis, age (OR: 1.086, p < .001) and homocysteine (HCY) concentrations (OR: 1.017, p = .003) were proven to be linked to the risk of developing dementia, while total cholesterol (TC) (OR: 0.674, p = .005) was a protective factor for dementia. We developed a formula of age + low-density lipoprotein cholesterol (LDL-C) + TC + HCY + number of comorbidities as a good predictor of dementia (AUC: 0.79), with a probability (cutoff) value of 0.112 (sensitivity 87.4%, specificity 55.8%, and accuracy 60.5%). CONCLUSIONS: High-serum HCY and low TC were risk factors for developing dementia. A cutoff value > 0.112 derived from our formula was an excellent predictor for people at a high risk of developing dementia, and may be a potentially useful diagnostic tool for identifying patients at risk for dementia in routine clinical practice.