MiR-411-5p Promotes Vascular Smooth Muscle Cell Phenotype Switch by Inhibiting Expression of Calmodulin Regulated Spectrin-Associated Protein-1.

When stimulated, vascular smooth muscle cells (VSMCs) change from a differentiated to a dedifferentiated phenotype. Dedifferentiated VSMCs have a key activity in cardiovascular diseases such as in-stent restenosis. MicroRNAs (miRNAs) have crucial functions in conversion of differentiated VSMCs to a dedifferentiated phenotype. We investigated the activity of miR-411-5p in the proliferation, migration, and phenotype switch of rat VSMCs.Based on a microRNA array assay, miR-411-5p expression was found to be significantly increased in cultured VSMCs stimulated by platelet-derived growth factor-BB (PDGF-BB). A CCK-8 assay, transwell assay, and scratch test were performed to measure the effect of miR-411-5p on the proliferation and migration of PDGF-BB-treated VSMCs. MiR-411-5p promoted expression of dedifferentiated phenotype markers such as osteopontin and tropomyosin 4 in PDGF-BB-treated VSMCs. Using mimics and inhibitors, we identified the target of miR-411-5p in PDGF-BB-treated VSMCs and found that calmodulin-regulated spectrin-associated protein-1 (CAMSAP1) was involved in the phenotypic switch mediated by PDGF-BB.By inhibiting expression of CAMSAP1, miR-411-5p enhanced the proliferation, migration, and phenotype switch of VSMCs.Blockade of miR-411-5p interaction with CAMSAP1 is a promising approach to treat in-stent restenosis.

Upregulation of the key biomarker kinesin family member 20A (KIF20A) is associated with pulmonary artery hypertension.

OBJECTIVE: Pulmonary artery hypertension (PAH) is a complex, fatal disease with limited treatments. This study aimed to investigate possible key targets in PAH through bioinformatics. METHODS: GSE144274 were obtained from Gene Expression Omnibus (GEO) database. Then, differentially expressed genes (DEGs) between idiopathic pulmonary hypertension (IPAH) and healthy subjects were identified and analyzed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed, and a protein-protein interaction (PPI) network was constructed using STRING. The hub genes were identified by MCODE method. The expression levels of hub genes were validated in vitro and in vivo models. Finally, the ROC analysis was performed based on the level of hub genes in clinical plasma samples. RESULTS: A total of 363 DEGs were identified. GO analysis on these DEGs were mainly enriched in cell division, inflammatory response, among others. In the KEGG pathways analysis, DEGs mainly involved in cytokine-cytokine receptor interaction, rheumatoid arthritis, and IL-17 signaling pathways were enriched. The DEGs were analyzed with the STRING for PPI network analysis, and 62 hub genes were identified by MCODE. Finally, 6 central genes, KIF18B, SPC25, DLGAP5, KIF20A, CEP55 and ANLN, were screened out due to their novelty role in PAH. The expression of KIF20A was validated to be significantly upregulated both in the lung tissue of hypoxia-induced pulmonary hypertension (HPH) mice and proliferative PASMCs. Additionally, KIF20A levels is evelated in PAH plasma and the area under the curve (AUC) to identify PAH was 0.8591 for KIF20A. CONCLUSION: The level of KIF20A elevates during the progression of PAH, which suggestes it could be a potential diagnostic and therapeutic target for the PAH.

Association Between Inflammatory Biomarkers and Contrast-induced Acute Kidney Injury in ACS Patients Undergoing Percutaneous Coronary Intervention: A Cross-sectional Study.

The present study aimed to evaluate the predictive role of inflammatory biomarkers in the development of contrast-induced acute kidney injury (CI-AKI) in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI). The inflammatory biomarkers assessed were: platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), neutrophil-to-lymphocyte*platelet ratio (NLPR), systemic inflammatory index (SII), and systemic inflammation response index (SIRI). Overall, 950 patients undergoing PCI were enrolled. The frequency of CI-AKI was 15.2% (n = 144). The levels of NLR, MLR, NLPR, SII, and SIRI were higher in the CI-AKI group than in the Non-CI-AKI group (P < .05). The addition of NLR ≥2.96, dNLR ≥2.08, NLPR ≥.012, SII ≥558.04, and SIRI ≥1.13 to the Mehran score model significantly increased the area under the curve (P < .05). Multivariable logistic regression analyses indicated that inflammatory biomarkers were significantly associated with CI-AKI, including NLR ≥2.96 (OR = 1.588, P = .017), dNLR ≥2.08 (OR = 1.686, P = .007), SII ≥558.04 (OR = 1.521, P = .030), and SIRI ≥1.13 (OR = 1.601, P = .017). Therefore, inflammation is associated with the development of CI-AKI, and preoperative hematological inflammatory markers could predict the risk of CI-AKI in ACS patients undergoing PCI.

Visceral adiposity index is positively associated with fasting plasma glucose: a cross-sectional study from National Health and Nutrition Examination Survey 2017-2020.

BACKGROUND: Visceral adiposity index (VAI) has been recognized as a reliable indicator for visceral adiposity. However, it remains largely unexplored on its association with fasting plasma glucose (FPG). The current study aims to explore the association between VAI and FPG using a representative dataset. METHODS: A cross-sectional study was carried out based on the dataset from National Health and Nutrition Examination Survey (NHANES) 2017-2020. Univariate and Multiple linear regression analysis were performed to explore the relationship between VAI and FPG. Generalized additive model (GAM) and smooth curve fitting analysis were performed to explore the nonlinear relationship between VAI and FPG. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of VAI for FPG elevation. RESULTS: A total of 4437 participants with complete data were finally included in the research. Individuals were divided into 4 quartiles according to the calculated VAI value: Q1 (VAI<0.69), Q2 (0.69 ≤ VAI < 1.18), Q3 (1.18 ≤ VAI < 2.02) and Q4 (VAI ≥ 2.02). FPG significantly increased with the increasing VAI quartile. Multiple linear regression analysis showed VAI was independently positively associated with FPG after adjusting confounding factors. As a continuous variable, an increase of one unit in VAI was correlated with 0.52 mmol/L (95% CI: 0.41-0.63, p < 0.0001) higher FPG level. As a categorical variable, 4th VAI quartile group was related to 0.71 mmol/L (95% CI: 0.47-0.95, p < 0.001) higher FPG level compared with 1st VAI group. GAM and smooth curve fitting analysis identified the non-linear relationship between VAI and FPG, and 4.02 was identified as the inflection point using two-piecewise linear regression. The positive association between VAI and FPG existed when VAI was lower (ß = 0.73, p < 0.0001) and higher than 4.02 (ß = 0.23, p = 0.0063). ROC analysis indicated VAI has a good predictive value for FPG elevation (AUC = 0.7169, 95% CI: 0.6948-0.7389), and the best threshold of VAI was 1.4315. CONCLUSION: VAI was an independently risk indicator for FPG, and VAI was nonlinearly positively associated with FPG. VAI had a good predictive value for elevated FPG. VAI might become a useful indicator for risk assessment and treatment of hyperglycemia in clinical practice.