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Background: Subarachnoid hemorrhage (SAH) represents a severe stroke subtype. Our study aims to develop gender‐specific prognostic prediction models derived from distinct prognostic factors observed among different‐gender patients. Methods: Inclusion comprised SAH‐diagnosed patients from January 2014 to March 2016 in our institution. Collected data encompassed patients' demographics, admission severity, treatments, imaging findings, and complications. Three‐month post‐discharge prognoses were obtained via follow‐ups. Analyses assessed gender‐based differences in patient information. Key factors underwent subgroup analysis, followed by univariate and multivariate analyses to identify gender‐specific prognostic factors and establish/validate gender‐specific prognostic models. Results: A total of 929 patients, with a median age of 57 (16) years, were analyzed; 372 (40%) were male, and 557 (60%) were female. Differences in age, smoking history, hypertension, aneurysm presence, and treatment interventions existed between genders (p < 0.01), yet no disparity in prognosis was noted. Subgroup analysis explored hypertension history, aneurysm presence, and treatment impact, revealing gender‐specific variations in these factors' influence on the disease. Screening identified independent prognostic factors: age, SEBES score, admission GCS score, and complications for males; and age, admission GCS score, intraventricular hemorrhage, treatment interventions, symptomatic vasospasm, hydrocephalus, delayed cerebral ischemia, and seizures for females. Evaluation and validation of gender‐specific models yielded an AUC of 0.916 (95% CI: 0.878–0.954) for males and 0.914 (95% CI: 0.885–0.944) for females in the ROC curve. Gender‐specific prognostic models didn't significantly differ from the overall population‐based model (model 3) but exhibited robust discriminative ability and clinical utility. Conclusion: Variations in baseline and treatment‐related factors among genders contribute partly to gender‐based prognosis differences. Independent prognostic factors vary by gender. Gender‐specific prognostic models exhibit favorable prognostic performance. [ABSTRACT FROM AUTHOR]

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a "centralized/decentralized" hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid. [ABSTRACT FROM AUTHOR]

The underwater laser polarization detection technology integrates the polarization characteristics of light into the detection and identification of underwater targets. Addressing the challenge of poor accuracy in identifying targets in strong underwater scattering environments, this article proposes an overall scheme for a laser polarization underwater detection device that suppresses scatter using polarized pulse signals. By overcoming key technological barriers in the design of polarization-preserving optical detection systems and utilizing the method of differential amplitude to measure polarization, a laser polarization underwater detection device was developed and underwater polarization detection experiments were conducted, achieving precise detection of underwater targets. The results indicate that the underwater detection device we designed has a root mean square error of less than 5.7% to detect the polarization of the target, demonstrating the accuracy and precision of the underwater detection device. [ABSTRACT FROM AUTHOR]

Objective: To elucidate the relationship between USP19 and O(6)‐methylguanine‐DNA methyltransferase (MGMT) after temozolomide treatment in glioblastoma (GBM) patients with chemotherapy resistance. Methods: Screening the deubiquitinase pannel and identifying the deubiquitinase directly interacts with and deubiquitination MGMT. Deubiquitination assay to confirm USP19 deubiquitinates MGMT. The colony formation and tumor growth study in xenograft assess USP19 affects the GBM sensitive to TMZ was performed by T98G, LN18, U251, and U87 cell lines. Immunohistochemistry staining and survival analysis were performed to explore how USP19 is correlated to MGMT in GBM clinical management. Results: USP19 removes the ubiquitination of MGMT to facilitate the DNA methylation damage repair. Depletion of USP19 results in the glioblastoma cell sensitivity to temozolomide, which can be rescued by overexpressing MGMT. USP19 is overexpressed in glioblastoma patient samples, which positively correlates with the level of MGMT protein and poor prognosis in these patients. Conclusion: The regulation of MGMT ubiquitination by USP19 plays a critical role in DNA methylation damage repair and GBM patients' temozolomide chemotherapy response. [ABSTRACT FROM AUTHOR]

In order to mitigate the risk of irreversible drowning injuries, this study introduces an enhanced YOLOv5 algorithm aimed at improving the efficacy of indoor swimming pool drowning detection and facilitating the timely rescue of endangered individuals. To simulate drowning and swimming positions accurately, four swimmers were deliberately chosen and observed, with monitoring conducted by drones flying above the swimming pool. The study was approved by the ethics committee of our institution, with the registration number 2022024. The images captured by the drones underwent a meticulous evaluation, and only those deemed suitable were selected to construct the self-made dataset, comprising a total of 8572 images. Furthermore, two enhancements were implemented in the YOLOv5 algorithm. Firstly, the inclusion of the ICA module strengthened category classification and the localization of water behavioral postures, which is improved from the coordinated attention module (CA). Secondly, the PAN module was replaced with the bi-directional feature pyramid network (BiFPN). Subsequently, the improved YOLOv5 algorithm underwent training using the self-made dataset. Evaluation of the algorithm's performance revealed a notably improved detection accuracy rate, recall rate, and an impressive mean Average Precision (mAP) score of 98.1%, 98.0%, and 98.5%, respectively. Our paper introduces the improved YOLOv5 algorithm, surpassing the original YOLOv5 algorithm in terms of recognition accuracy for instances of drowning. [ABSTRACT FROM AUTHOR]