The relevant metabolite biomarkers for risk prediction of early onset of ventricular fibrillation (VF) after ST-segment elevation myocardial infarction (STEMI) remain unstudied. Here, we aimed to identify these imetabolites and the important metabolic pathways involved, and explore whether these metabolites could be used as predictors for the phenotype. Plasma samples were obtained retrospectively from a propensity-score matched cohort including 42 STEMI patients (21 consecutive VF and 21 non-VF). Ultra-performance liquid chromatography and mass spectrometry in combination with a comprehensive analysis of metabolomic data using Metaboanalyst 5.0 version were performed. As a result, the retinal metabolism pathway proved to be the most discriminative for the VF phenotype. Furthermore, 9-cis-Retinoic acid (9cRA) and dehydrophytosphingosine proved to be the most discriminative biomarkers. Biomarker analysis through receiver operating characteristic (ROC) curve showed the 2-metabolite biomarker panel yielding an area under the curve (AUC) of 0.836. The model based on Monte Carlo cross-validation found that 9cRA had the greatest probability of appearing in the predictive panel of biomarkers in the model. Validation of model efficiency based on an ROC curve showed that the combination model constructed by 9cRA and dehydrophytosphingosine had a good predictive value for early-onset VF after STEMI, and the AUC was 0.884 (95% CI 0.714–1). Conclusively, the retinol metabolism pathway was the most powerful pathway for differentiating the post-STEMI VF phenotype. 9cRA was the most important predictive biomarker of VF, and a plasma biomarker panel made up of two metabolites, may help to build a potent predictive model for VF.

Vascular smooth muscle cells (VSMCs) contribute to the deposition of extracellular matrix proteins (ECMs), including Type IV collagen, in the vessel wall. ECMs coordinate communication among different cell types, but mechanisms underlying this communication remain unclear. Our previous studies have demonstrated that X-box binding protein 1 (XBP1) is activated and contributes to VSMC phenotypic transition in response to vascular injury. In this study, we investigated the participation of XBP1 in the communication between VSMCs and vascular progenitor cells (VPCs). Immunofluorescence and immunohistology staining revealed that Xbp1 gene was essential for COL4A1 expression during mouse embryonic development and vessel wall ECM deposition and stem cell antigen 1-positive (Sca1+)-VPC recruitment in response to vascular injury. Western blot analysis elucidated an Xbp1 gene dose-dependent effect on COL4A1 expression and that the spliced XBP1 protein (XBP1s) increased protease-mediated COL4A1 degradation as revealed by Zymography. RT-PCR analysis revealed that XBP1s in VSMCs not only upregulated COL4A1/2 transcription but also induced the occurrence of a novel transcript variant, COL4A1s, in which the front part of exon 4 is joined with the rear part of exon 42. Chromatin-immunoprecipitation, DNA/protein pulldown and in vitro transcription demonstrated that XBP1s binds to exon 4 and exon 42, directing the transcription from exon 4 to exon 42. This leads to transcription complex bypassing the internal sequences, producing a shortened soluble COL4A1s protein that increased Sca1+-VPC migration. Taken together, these results suggest that activated VSMCs may recruit Sca1+-VPCs via XBP1s-mediated COL4A1s secretion, leading to vascular injury repair or neointima formation.

The aim of this paper is to discover differentially expressed genes related to ferroptosis (DEFRGs) in patients with ST-segment elevation myocardial infarction (STEMI) and to construct a reliable prognostic signature that incorporates key DEFRGs and easily accessible clinical factors. We did a systematic review of Gene Expression Omnibus datasets and picked datasets SE49925, GSE60993, and GSE61144 for analysis. We applied GEO2R to find DEFRGs and overlapped them among the picked datasets. We performed functional enrichment analysis to explore their biological functions. We built an optimal model with least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression. We tested the clinical value of the signature with survival analysis, ROC curve, decision curve analysis and a prognostic nomogram. We also confirmed the model externally with plasma samples from our center's patients. A prognostic signature combining three overexpressed DEFRGs (ACSL1, ACSL4, TSC22D3) and two clinical variables (serum creatinine level, Gensini score) was established. The signature effectively classified patients into low- and high-risk groups. Survival analysis, ROC curve analysis, and DCA showed its robust predictive performance and clinical utility of the signature within two years after the onset of the disease. The external validation cohort confirmed the significant difference in major adverse cardiovascular events (MACEs) between the low- and high-risk groups. This study revealed DEFRGs in patients with STEMI and developed a prognostic signature that integrates gene expression levels and clinical factors for stratifying patients and predicting the risk of MACEs.

重复经颅磁刺激（rTMS）是一种安全的非侵入性干预技术，在改善阿尔茨海默病（AD）患者的认知功能上取得了一定的效果，有望成为AD患者治疗的有效方法之一。然而不同rTMS治疗方案之间的疗效差异较大，这可能与刺激频率、模式、部位、维持时间、强度、聚焦能力等有关。研究表明，高频疗效优于低频；间接θ爆发刺激与常规rTMS疗效相当，但每次治疗时间短，患者依从性增加；刺激AD患者受损脑区或关联网络可增加疗效；短期强化治疗与长期维持治疗结合可维持疗效；结合认知损害程度动态调整刺激强度可增加疗效；利用磁共振功能连接方法等技术可解决rTMS的聚焦能力。本综述分析了上述影响因素，为今后rTMS治疗AD的临床方案设计提供思路。

Background: Lower extremity deep vein thrombosis (LEDVT), a common postoperative breast cancer complication, depends on multiple factors, such as systemic inflammation and immune status. We assessed the preoperative systemic immune-inflammatory index (SII) as a LEDVT risk predictor and did stratified nursing intervention based on it. Material/Methods: A retrospective analysis was conducted on 552 patients who underwent breast cancer surgery from January 2019 to May 2020. Univariate and multivariate methods were used to screen independent risk factors for postoperative LEDVT, and effects of risk stratification and nursing intervention on the validation cohort were observed. Results: A total of 46 patients (8.3%) developed postoperative LEDVT. Compared with the control group, the LEDVT group had older age, higher proportion of menopause, higher preoperative Autar score, SII, and plasma D-dimer level, lower albumin level, and later postoperative pathological stage, with statistically significant differences (P<0.05). Multivariate logistic regression showed SII, plasma D-dimer, and menopause were independent predictors of postoperative LEDVT. ROC curve analysis showed SII had the strongest predictive ability, with an AUC of 0.714. Subsequently, 126 patients in the validation set were stratified according to the preoperative SII score, and intensified nursing intervention was implemented for high-risk patients, resulting in a significant reduction in the incidence of LEDVT (3.3% vs 8.3%, P=0.046). Conclusions: Preoperative SII level can be used as an independent risk predictor of postoperative LEDVT in breast cancer patients. Applying it for risk stratification and implementing intensified nursing intervention for high-risk patients can significantly reduce the incidence of postoperative LEDVT.