The current research aimed to investigate the effect of miR-7 targeting matrix metalloproteinase 14 (MMP-14) on homocysteine (Hcy)-induced rat cerebral artery vascular smooth muscle cells (VSMCs) proliferation, migration and inflammatory factor expression and its possible mechanism. The expression of miR-7 and MMP-14 in Hcy-induced VSMCs were detected by real-time fluorescent quantitative PCR (RT-qPCR) and Western blot. Methyl Thiazolyl Tetrazolium (MTT) method, Transwell assays and enzyme-linked immunosorbent assay (ELISA) were performed to detect the effect of miR-7 and MMP-14 expression on the proliferation and migration, as well as interleukin 6 (IL-6) and tumor necrosis factor ɑ (TNF-ɑ) expression of Hcy-induced VSMCs. The interaction between miR-7 and MMP-14 was detected by dual-luciferase reporter gene assay. Western blot was applied to analyse the effects of miR-7 and MMP-14 expression on the Toll-like receptor (TLR4)/nuclear transcription factor-KB (NF-κB) signaling pathway. The results showed that after induced by Hcy, the expression of miR-7 in VSMCs was significantly reduced, the expression of MMP-14 was significantly increased, and the cell viability, the number of migrating cells, IL-6 and TNF-ɑ expression were significantly increased (P<0.05). After overexpression of miR-7, the viability, migration cell numbers, IL-6 and TNF-ɑ expression of Hcy-induced VSMCs were significantly reduced (P<0.05). miR-7 directly binds to MMP-14 and negatively regulates the expression of MMP-14. After overexpression of miR-7, the levels of TLR4 and p-NF-κB p65 in VSMCs were significantly reduced (P<0.05); overexpression of MMP-14 could reduce the effect of miR-7 overexpression on TLR4 and p-NF-κB p65 expression in VSMCs (P<0.05). Overexpression of MMP-14 and/or activation of the TLR4/NF-κB signaling pathway could reverse the effect of miR-7 overexpression on the proliferation, migration and IL-6 and TNF-ɑ expression of Hcy-induced VSMCs (P<0.05). It is concluded that miR-7 can inhibit Hcy-induced rat cerebral artery VSMCs proliferation, migration, and inflammatory factor expression by targeting the regulation of MMP-14 expression and inhibiting the activation of the TLR4/NF-κB signaling pathway.

Adolescent idiopathic scoliosis (AIS) can cause abnormal body posture, which has a negative impact on the overall posture. Therefore, timely prevention and early treatment are extremely important. The purpose of this study is to build an early warning model of AIS risk, so as to provide guidance for accurately identifying early high-risk AIS children and adolescents. We conducted a retrospective study of 1732 children and adolescents with or without AIS who underwent physical examination in Longgang District Central Hospital of Shenzhen (LDCHS queue) from January 2019 to October 2022 and 1581 children and adolescents with or without AIS in Shenzhen People Hospital (January 2018 to December 2022) as external validation queues (SPH queue). The random forest model (RFM), support vector machine model, artificial neural network model (ANNM), decision tree model (DTM), and generalized linear model (GLM) were used to build AIS model for children and adolescents. The predictive efficacy of 5 machine learning models was evaluated by receiver operating characteristic curve and decision curve analysis. For screening candidate predictors of AIS, the ratio of sitting height to standing height (ROSHTSH), angle of lumbar rotation, scapular tilt (ST), shoulder-height difference (SHD), lumbar concave (LC), pelvic tilt (PT) and angle of thoracolumbar rotation (AOTR) can be used as a potential predictor of AIS. The effectiveness of the prediction model constructed by the 5 machine learning algorithms was between (area under the curve [AUC]: 0.767, 95% confidence interval [CI]: 0.710–0.824) and (AUC: 0.899, 95% CI: 0.842–0.956) in the training set and internal verification set, respectively. Among them, the ANNM was equipped with the best prediction effectiveness (training set: AUC: 0.899, 95% CI: 0.842–0.956) and (internal verification set: AUC: 0.897, 95% CI: 0.842–0.952). The prediction model of AIS based on machine learning algorithm can achieve satisfactory prediction efficiency, among which ANNM is the best, which can be used to guide clinicians in diagnosis and treatment and improve the prognosis of AIS children and adolescents.

Exploring candidate markers to predict the clinical outcomes of pulmonary infection in stroke patients have a high unmet need. This study aimed to develop machine learning (ML)-based predictive models for pulmonary infection. Between January 2008 and April 2021, a retrospective analysis of 1397 stroke patients who had CT angiography from skull to diaphragm (including CT of the chest) within 24 hours of symptom onset. A total of 21 variables were included, and the prediction model of pulmonary infection was established by multiple ML-based algorithms. Risk factors for pulmonary infection were determined by the feature selection method. Area under the curve (AUC) and decision curve analysis were used to determine the model with the best resolution and to assess the net clinical benefits associated with the use of predictive models, respectively. A total of 889 cases were included in this study as a training group, while 508 cases were as a validation group. The feature selection indicated the top 6 predictors were procalcitonin, C-reactive protein, soluble interleukin-2 receptor, consciousness disorder, dysphagia, and invasive procedure. The AUCs of the 5 models ranged from 0.78 to 0.87 in the training cohort. When the ML-based models were applied to the validation set, the results also remained reconcilable, and the AUC was between 0.891 and 0.804. The decision curve analysis also showed performed better than positive line and negative line, indicating the favorable predictive performance and clinical values of the models. By incorporating clinical characteristics and systemic inflammation markers, it is feasible to develop ML-based models for the presence and consequences of signs of pulmonary infection in stroke patients, and the use of the model may be greatly beneficial to clinicians in risk stratification and management decisions.