Evidence for Absence of Latchbridge Formation in Phasic Saphenous Artery

Tonic arterial smooth muscle can produce strong contractions indefinitely by formation of slowly cycling crossbridges (latchbridges) that maintain force at a high energy economy. To fully understand the uniqueness of mechanisms regulating tonic arterial contraction, comparisons have been made to phasic visceral smooth muscles that do not sustain high forces. This study explored mechanisms of force maintenance in a phasic artery by comparing KCl-induced contractions in the tonic, femoral artery (FA) and its primary branch, the phasic saphenous artery (SA). KCl rapidly (5 N/m2) and [ca2+]i (250 nM) in FA and SA. By 10 min, [ca2+]i declined to 175 nM in both tissues but stress was sustained in FA (1.3 x 105N/m2) and reduced by 40% in SA (0.8 x l05 N/m2). Reduced tonic stress correlated with reduced myosin light chain (MLC) phosphorylation in SA (28% vs. 42% in FA). SA expressed more MLC phosphatase than FA, and permeabilized (β-escin) SA relaxed more rapidly than FA in the presence of MLC kinase blockade, suggesting that MLC phosphatase activity in SA was greater than that in FA. The reduction in MLC phosphorylation in SA was insufficient to account for reduced tonic force (latchbridge model), and SA expressed more fast myosin isoforms than did FA. Cytochalasin-D reduced force-maintenance more in FA than SA. These data support the hypothesis that strong force-maintenance is absent in SA because expressed motor proteins do not support latchbridge formation, and because actin polymerization is not stimulated

Associations between specific volatile organic chemical exposures and cardiovascular disease risks: insights from NHANES

IntroductionAn increasing body of research has demonstrated a correlation between pollutants from the environment and the development of cardiovascular diseases (CVD). However, the impact of volatile organic chemicals (VOC) on CVD remains unknown and needs further investigation.ObjectivesThis study assessed whether exposure to VOC was associated with CVD in the general population.MethodsA cross-sectional analysis was conducted utilizing data from five survey cycles (2005–2006, 2011–2012, 2013–2014, 2015–2016, and 2017–2018) of the National Health and Nutrition Examination Survey (NHANES) program. We analyzed the association between urinary VOC metabolites (VOCs) and participants by multiple logistic regression models, further Bayesian Kernel Machine Regression (BKMR) models and Weighted Quantile Sum (WQS) regression were performed for mixture exposure analysis.ResultsTotal VOCs were found to be positively linked with CVD in multivariable-adjusted models (p for trend = 0.025), independent of established CVD risk variables, such as hypertension, diabetes, drinking and smoking, and total cholesterol levels. Compared with the reference quartile of total VOCs levels, the multivariable-adjusted odds ratios in increasing quartiles were 1.01 [95% confidence interval (CI): 0.78–1.31], 1.26 (95% CI: 1.05–1.21) and 1.75 (95% CI: 1.36–1.64) for total CVD. Similar positive associations were found when considering individual VOCs, including AAMA, CEMA, CYMA, 2HPMA, 3HPMA, IPM3 and MHBMA3 (acrolein, acrylamide, acrylonitrile, propylene oxide, isoprene, and 1,3-butadiene). In BKMR analysis, the overall effect of a mixture is significantly related to VOCs when all chemicals reach or exceed the 75th percentile. Moreover, in the WQS models, the most influential VOCs were found to be CEMA (40.30%), DHBMA (21.00%), and AMCC (19.70%).ConclusionThe results of our study indicated that VOC was all found to have a significant association with CVD when comparing results from different models. These findings hold significant potential for public health implications and offer valuable insights for future research directions

A Novel Method for ECG Signal Classification Via One-Dimensional Convolutional Neural Network

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods

An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research communit