Clinical effect of external ventricular drainage under intracranial pressure monitoring in the treatment of aneurysmal subarachnoid hemorrhage patients and investigation of the mechanism of miR-146a-5p/STC1 axis in inhibiting early brain injury in aneurys.

Aneurismal subarachnoid hemorrhage (aSAH) is a common disease in the neural system, with high death rate. Our study aimed to explore the clinical effect of external ventricular drainage under intracranial pressure monitoring in the treatment of patients with aSAH and investigate the role along with mechanism of miR-146a-5p in aSAH. Ninety-six aSAH patients were allocated into control group (CG) and study group (SG). The CG was released by lumbar puncture. The SG underwent external ventricular drainage based on intracranial pressure monitoring. The prognosis, daily living ability, neurological function, S100ß and NSE (neuron-specific enolase) levels and incidence of complications were monitored. Besides, a rat model of SAH was built to assess the neurobehavioral function, blood-brain barrier permeability, brain water content, neuronal apoptosis as well as inflammation. SAH cell model stimulated by oxyhemoglobin, and cell apoptosis as well as inflammation were measured. Luciferase reporter assay was implemented to explore the interaction between miR-146a-5p and STC1. Results showed higher GOS and BI scores but lower NIHSS scores, S100ß and NSE levels and complication rates in SG compared with CG. Additionally, miR-146a-5p presented down-regulation in brain tissues of SAH rat model, and overexpressed miR-146a-5p reduced brain injury along with neuroinflammation in SAH rat model. Oxyhemoglobin-induced nerve cell apoptosis along with inflammation after SAH, and overexpressed miR-146a-5p repressed oxyhemoglobin-induced nerve cell apoptosis along with inflammation. STC1 is the target mRNA of miR-146a-5p, and overexpressed miR-146a-5p represses oxyhemoglobin-induced nerve cell apoptosis along with inflammation via regulating STC1 expression. In conclusion, external ventricular drainage under intracranial pressure monitoring could promote prognosis, promote daily living ability, improve neurological function, reduce S100ß protein and NSE levels, and reduce the incidence of complications in patients with aSAH. Meanwhile, miR-146a-5p inhibited early brain injury and neuroinflammation in aSAH via regulating STC1 expression.

LiDAR Dynamic Target Detection Based on Multidimensional Features.

To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman's rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer-Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection.

Detection and validation of circulating endothelial cells, a blood-based diagnostic marker of acute myocardial infarction.

BACKGROUND: Circulating endothelial cells (CECs) are markers of vascular damage that have clinical relevance in many diseases, including acute myocardial infarction (AMI), and may be predictors of treatment responses. Herein, we investigated the diagnostic and prognostic value of CEC monitoring in AMI patients and a murine model. METHODOLOGY/PRINCIPAL FINDINGS: CECs were defined as Hoechst 33342(+)/CD45(-/)CD31(+)/CD146(+)/CD133(-) in human blood samples and Hoechst 33342(+)/CD45(-/)CD31(+)/KDR(+)/CD117(-) in murine samples. To evaluate the validity and variability of our CEC detection system, peripheral blood samples of vascular endothelial growth factor-treated athymic nude mice and AMI patients were collected and subjected to intra-assay analysis. CEC detection by flow cytometry and real-time PCR were compared. Blood samples were obtained from 61 AMI patients, 45 healthy volunteers and 19 samples of the original AMI patients accepted one month treatment, via flow cytometry and expressed as a percentage of peripheral blood mononuclear cells. RESULTS: Our CEC detection method was validated and had limited variability. CEC concentrations were higher in AMI patients compared to healthy controls. One month post-treatment, CECs levels decreased significantly. CONCLUSIONS/SIGNIFICANCE: CEC levels may be useful as a diagnostic and prognostic biomarker in AMI patients.