Sperm migration through the female tract is crucial to fertilization, but the role of the complex and confined structure of the fallopian tube in sperm guidance remains unknown. Here, by confocal imaging microchannels head-on, we distinguish corner- vs. wall- vs. bulk-swimming bull sperm in confined geometries. Corner-swimming dominates with local areal concentrations as high as 200-fold that of the bulk. The relative degree of corner-swimming is strongest in small channels, decreases with increasing channel size, and plateaus for channels above 200 μm. Corner-swimming remains predominant across the physiologically-relevant range of viscosity and pH. Together, boundary-following sperm account for over 95% of the sperm distribution in small rectangular channels, which is similar to the percentage of wall swimmers in circular channels of similar size. We also demonstrate that wall-swimming sperm travel closer to walls in smaller channels (~100 μm), where the opposite wall is within the hydrodynamic interaction length-scale. The corner accumulation effect is more than the superposition of the influence of two walls, and over 5-fold stronger than that of a single wall. These findings suggest that folds and corners are dominant in sperm migration in the narrow (sub-mm) lumen of the fallopian tube and microchannel-based sperm selection devices.

Enhanced pollutant removal by FeOOH/RGO hydrogels relying on π–π and π–Fe interactions.

This study aimed to explore the potential diagnostic biomarkers and mechanisms underlying acute myocardial infarction (AMI). We downloaded four datasets (GSE19339, GSE48060, GSE66360, and GSE97320) from the Gene Expression Omnibus database and combined them as an integrated dataset. A total of 153 differentially expressed genes (DEGs) were analyzed by the linear models for microarray analysis (LIMMA) package. Weighted gene co-expression network analysis was used to screen for the significant gene modules. The intersection of DEGs and genes in the most significant module was termed ''common genes'' (CGs). CGs were mainly enriched in ''inflammatory response,'' ''neutrophil chemotaxis,'' and ''IL-17 signaling pathway'' through functional enrichment analyses. Subsequently, 15 genes were identified as the hub genes in the proteinprotein interaction network. The Fc fragment of IgE receptor Ig (FCER1G) and prostaglandin-endoperoxide synthase 2 (PTGS2) showed significantly increased expression in AMI patients and mice at the 12-h time point in our experiments. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of FCER1G and PTGS2. The area under ROC curve of FCER1G and PTGS2 was 77.6% and 80.7%, respectively. Moreover, the micro (mi)RNA-messenger (m)RNA network was also visualized; the results showed that miRNA-143, miRNA-144, and miRNA-26 could target PTGS2 in AMI progression.

Self-assembled 3D hierarchical iron (hydr)oxides are synthesized with different alcohol additives for water treatment.

The purpose of this study was to summarize the clinical characteristics and risk factors of major adverse cardiovascular events (MACEs) in patients who had had acute myocardial infarction (AMI) within 1 year of percutaneous coronary intervention (PCI). A total of 421 AMI patients who were treated with PCI and experienced MACEs within 1 year of their admission were included in this retrospective study. In addition, patients were matched for age, sex, and presentation with 561 patients after AMI who had not had MACEs. The clinical characteristics and risk factors for MACEs within 1 year in AMI patients were investigated, to develop a nomogram for MACEs based on univariate and multivariate analyses. The C statistic was used to assess the discriminative performance of the nomogram. In addition, calibration curve and decision curve analyses were conducted to validate the calibration performance and utility, respectively, of the nomogram. After univariate and multivariate analyses, a nomogram was constructed based on age (odds ratio (OR): 1.030; 95% confidence interval (CI): 1.014–1.047), diabetes mellitus (OR: 1.667; 95% CI: 1.151–2.415), low-density lipoprotein cholesterol (OR: 1.332; 95% CI: 1.134–1.565), uric acid (OR: 1.003; 95% CI: 1.001–1.005), lipoprotein (a) (OR: 1.003; 95% CI: 1.002–1.003), left ventricular ejection fraction (OR: 0.929; 95% CI: 0.905–0.954), Syntax score (OR: 1.075; 95% CI: 1.053–1.097), and hypersensitive troponin T (OR: 1.002; 95% CI: 1.002–1.003). The C statistic was 0.814. The calibration curve showed good concordance of the nomogram, while decision curve analysis demonstrated satisfactory positive net benefits. We developed a convenient, practical, and effective prediction model for predicting MACEs in AMI patients within 1 year of PCI. To ensure generalizability, this model requires external validation.

Objective. This study is aimed at exploring the underlying molecular mechanisms of ST-segment elevation myocardial infarction (STEMI) and provides potential clinical prognostic biomarkers for STEMI. Methods. The GSE60993 dataset was downloaded from the GEO database, and the differentially expressed genes (DEGs) between STEMI and control groups were screened. Enrichment analysis of the DEGs was subsequently performed using the DAVID database. A protein–protein interaction network was constructed, and hub genes were identified. The hub genes in patients were then validated by quantitative reverse transcription-PCR. Furthermore, hub gene-miRNA interactions were evaluated using the miRTarBase database. Finally, patient data on classical cardiovascular risk factors were collected, and plasma microRNA-146a (miR-146a) levels were detected. An individualized nomogram was constructed based on multivariate Cox regression analysis. Results. A total of 239 DEGs were identified between the STEMI and control groups. Expression of S100A12 and miR-146a was significantly upregulated in STEMI samples compared with controls. STEMI patients with high levels of miR-146a had a higher risk of major adverse cardiovascular events (MACEs) than those with low levels of miR-146a (log-rank P = 0.034 ). Multivariate Cox regression analysis identified five statistically significant variables, including age, hypertension, diabetes mellitus, white blood cells, and miR-146a. A nomogram was constructed to estimate the likelihood of a MACE at one, two, and three years after STEMI. Conclusion. The incidence of MACEs in STEMI patients expressing high levels of miR-146a was significantly greater than in those expressing low levels. MicroRNA-146a can serve as a biomarker for adverse prognosis of STEMI and might function in its pathogenesis by targeting S100A12, which may exert its role via an inflammatory response. In addition, our study presents a valid and practical model to assess the probability of MACEs within three years of STEMI.

No abstract

Objective Percutaneous coronary intervention (PCI) is one of the most effective treatments for acute coronary syndrome (ACS). However, the need for postoperative revascularization remains a major problem in PCI. This study was to develop and validate a nomogram for prediction of revascularization after PCI in patients with ACS. Methods A retrospective observational study was conducted using data from 1083 patients who underwent PCI (≥6 months) at a single center from June 2013 to December 2019. They were divided into training (70%; n = 758) and validation (30%; n = 325) sets. Multivariate logistic regression analysis was used to establish a predictive model represented by a nomogram. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot and decision curve analysis (DCA), respectively. Results The nomogram was comprised of ten variables: follow-up time (odds ratio (OR): 1.01; 95% confidence interval (CI): 1.00–1.03), history of diabetes mellitus (OR: 1.83; 95% CI: 1.25–2.69), serum creatinine level on admission (OR: 0.99; 95% CI: 0.98–1.00), serum uric acid level on admission (OR: 1.005; 95% CI: 1.002–1.007), lipoprotein-a level on admission (OR: 1.0021; 95% CI: 1.0013–1.0029), low density lipoprotein cholesterol level on re-admission (OR: 1.33; 95% CI: 0.10–0.47), the presence of chronic total occlusion (OR: 3.30; 95% CI: 1.93–5.80), the presence of multivessel disease (OR: 4.48; 95% CI: 2.85–7.28), the presence of calcified lesions (OR: 1.63; 95% CI: 1.11–2.39), and the presence of bifurcation lesions (OR: 1.82; 95% CI: 1.20–2.77). The area under the receiver operating characteristic curve values for the training and validation sets were 0.765 (95% CI: 0.732–0.799) and 0.791 (95% CI: 0.742–0.830), respectively. The calibration plots showed good agreement between prediction and observation in both the training and validation sets. DCA also demonstrated that the nomogram was clinically useful. Conclusion We developed an easy-to-use nomogram model to predict the risk of revascularization after PCI in patients with ACS. The nomogram may provide useful assessment of risk for subsequent treatment of ACS patients undergoing PCI.