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Abstract Background The COVID-19 pandemic’s diverse symptomatology, driven by variants, underscores the critical need for a comprehensive understanding. Employing stochastic models, our study evaluates symptom sequences across SARS-CoV-2 variants on aggregated data, yielding essential insights for targeted interventions. Methods We conducted a meta-analysis based on research literature published before December 9, 2022, from PubMed, LitCovid, Google Scholar, and CNKI databases, to investigate the prevalence of COVID-19 symptoms during the acute phase. Registered in PROSPERO (CRD42023402568), we performed random-effects meta-analyses using the R software to estimate pooled prevalence and 95% CI. Based on our findings, we introduced the Stochastic Progression Model and Sequential Pattern Discovery using Equivalence classes (SPADE) algorithm to analyze patterns of symptom progression across different variants. Results Encompassing a total of 430,100 patients from east and southeast Asia, our results reveal the highest pooled estimate for cough/dry cough across wild-type, Delta, and Omicron variants, with fever (78.18%; 95% CI: 67–89%) being the most prominent symptom for the Alpha variant. Symptoms associated with the Omicron variant primarily manifested in upper respiratory tracts, cardiovascular, and neuropsychiatric systems. Stochastic models indicate early symptoms including dry cough and fever, followed by subsequent development of sleep disorders, fatigue, and more. Conclusion Our study underscores the evolving symptomatology across SARS-CoV-2 variants, emphasizing similarities in fever, cough, and fatigue. The Omicron variant presents a distinct profile characterized by milder symptoms yet heightened neuropsychological challenges. Advanced analytical models validate the observed sequential progression of symptoms, reinforcing the consistency of disease trajectory.

Abstract Purpose of review There are significant differences in the transmission rate and mortality rate of COVID-19 under environmental conditions such as seasons and climates. However, the impact of environmental factors on the role of the COVID-19 pandemic and the transmission mechanism of the SARS-CoV-2 is unclear. Therefore, a comprehensive understanding of the impact of environmental factors on COVID-19 can provide innovative insights for global epidemic prevention and control policies and COVID-19 related research. This review summarizes the evidence of the impact of different natural and social environmental factors on the transmission of COVID-19 through a comprehensive analysis of epidemiology and mechanism research. This will provide innovative inspiration for global epidemic prevention and control policies and provide reference for similar infectious diseases that may emerge in the future. Recent findings Evidence reveals mechanisms by which natural environmental factors influence the transmission of COVID-19, including (i) virus survival and transport, (ii) immune system damage, (iii) inflammation, oxidative stress, and cell death, and (iiii) increasing risk of complications. All of these measures appear to be effective in controlling the spread or mortality of COVID-19: (1) reducing air pollution levels, (2) rational use of ozone disinfection and medical ozone therapy, (3) rational exposure to sunlight, (4) scientific ventilation and maintenance of indoor temperature and humidity, (5) control of population density, and (6) control of population movement. Our review indicates that with the continuous mutation of SARS-CoV-2, high temperature, high humidity, low air pollution levels, and low population density more likely to slow down the spread of the virus.

Abstract Background The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies. Methods A total of 345 infertile couples who underwent intracytoplasmic sperm injection (ICSI) treatment with surgical sperm retrieval due to different etiologies from February 2020 to March 2023 at the reproductive center were retrospectively analyzed. The six machine learning (ML) models were used to predict the clinical pregnancy of ICSI. After evaluating the performance characteristics of the six ML models, the Extreme Gradient Boosting model (XGBoost) was selected as the best model, and SHAP was utilized to interpret the XGBoost model for predicting clinical pregnancies and to reveal the decision-making process of the model. Results Combining the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, brier score, and the area under the precision-recall (P-R) curve (AP), the XGBoost model has the best performance (AUROC: 0.858, 95% confidence interval (CI): 0.778–0.936, accuracy: 79.71%, brier score: 0.151). The global summary plot of SHAP values shows that the female age is the most important feature influencing the model output. The SHAP plot showed that younger age in females, bigger testicular volume (TV), non-tobacco use, higher anti-müllerian hormone (AMH), lower follicle-stimulating hormone (FSH) in females, lower FSH in males, the temporary ejaculatory disorders (TED) group, and not the non-obstructive azoospermia (NOA) group all resulted in an increased probability of clinical pregnancy. Conclusions The XGBoost model predicts clinical pregnancies associated with testicular sperm retrieval of different etiologies with high accuracy, reliability, and robustness. It can provide clinical counseling decisions for patients with surgical sperm retrieval of various etiologies.

Chrysochroinae Laporte de Castelnau, 1835 is the very colourful subfamily of Buprestidae. There are 127 species and subspecies of the subfamily which have been recorded in China.In this paper, we reported three genera, two subgenera and five species of the subfamily Chrysochroinae Laporte de Castelnau, 1835 (Coleoptera, Buprestidae) which are all newly recorded from China. These reported taxa belong to two tribes and four genera: Chrysochroa (Chroodema) corbetti (Kerremans, 1893), Chrysochroa (Pyranthe) fulgens ephippigera White, 1843, Demochroa (Demoxantha) gratiosa indica Csiki, 1900, Xanthocata bonvouloirii (Deyrolle, 1861) (all the above four being Chrysochroini) and Cardiaspis mouhotii E. Saunders, 1866 (Dicercini). The five newly-recorded species are briefly described, illustrated and supplemented with relevant biological information.

Purpose The aim of this study was to develop a prognostic nomogram which could predict the prognosis of north Chinese patients with autoimmune cerebellar ataxia (ACA) after immunotherapy.Methods Patients with an initial diagnosis of ACA who accepted first-line immunotherapy at our hospital from March 2018 to May 2023 were retrospectively reviewed. Modified Rankin Scale (mRS) was used to evaluate neurological outcomes. According to the mRS scores after immunotherapy, patients with ACA were divided into good prognosis group (mRS 0–2) and poor prognosis group (mRS 3–6). The nomogram for poor prognosis of ACA patients were built based on logistic regression analysis. The validation of the prognostic model was evaluated by concordance index (C-index), calibration curves, and decision curve analyses (DCAs).Results A total of 86 patients with ACA who received immunotherapy at our hospital were included in this study. They were randomly divided into a training cohort (n = 60) and a validation cohort (n = 26) at a ratio of 7:3. Multivariate analyses revealed that that prognostic variables significantly related to the poor prognosis of ACA were age, elevated cerebrospinal fluid (CSF) albumin (ALB) and abnormal magnetic resonance imaging (MRI). The nomogram was constructed based on above 3 factors. The C-index of the nomogram was 0.935 (95% CI: 0.884-0.991) in the training set and 0.933 (95% CI: 0.763-0.994) in the validation set. The calibration plots for the nomogram showed that predictions of risk of poor prognosis were almost consistent with actual observations. The DCAs showed great clinical usefulness of the nomograms.Conclusion We successfully developed a nomogram to predict poor prognosis for ACA patients using risk factors of age, elevated CSF-ALB and abnormal MRI.

Objective: To explore the effect of Gouqi Nuzhen Liuhe Decoction (GNLHD) on the PI3K/mTOR Signaling Pathway for Premature Ovarian Insufficiency (POI) based on system pharmacology. Methods: First, the system pharmacology approach was used to predict the mechanism of GNLHD. Then, mice were randomly divided into model group, positive group, GNLHD high-dose group, GNLHD medium-dose group, and GNLHD low-dose group. Hematoxylin-eosin (HE) staining was used to observe the pathological changes of ovarian tissue under light microscope. The expression levels of estradiol (E2), follicle-stimulating hormone (FSH) and luteinizing hormone (LH) were detected by enzyme-linked immunosorbent assay. The expressions of PI3K, AKT1 and mTOR proteins in ovarian tissue were detected by immunohistochemistry. Results: The results of system pharmacology showed that GNLHD may regulate biological processes and signaling pathways such as: reproductive structure development, reproductive system development, Oocyte meiosis and so on. Compared with the model group, the levels of E2 in the GNLHD group were increased, and the levels of FSH and LH were decreased (P

Objective: To examine the effect of icariin plus curcumol on prostate cancer cells PC3 and elucidate the underlying mechanisms. Methods: We employed the Cell Counting Kit 8 assay and colony formation assay to assess cell viability and proliferation. Autophagy expression was analyzed using monodansylcadaverine staining. Immunofluorescence and Western blot analyses were used to evaluate protein expressions related to autophagy, pyroptosis, and the mTOR pathway. Cellular damage was examined using the lactate dehydrogenase assay. Moreover, cathepsin B and NLRP3 were detected by co-immunoprecipitation. Results: Icariin plus curcumol led to a decrease in PC3 cell proliferation and an enhancement of autophagy. The levels of LC3-II/LC3-I and beclin-1 were increased, while the levels of p62 and mTOR were decreased after treatment with icariin plus curcumol. These changes were reversed upon overexpression of mTOR. Furthermore, 3-methyladenine resulted in a decrease in inflammatory cytokines, pyroptosis-related protein levels, and lactate dehydrogenase concentration, compared to the icariin plus curcumol group. Inhibiting cathepsin B reversed the regulatory effects of icariin plus curcumol. Conclusions: Icariin plus curcumol demonstrates great potential as a therapeutic agent for castration-resistant prostate cancer by enhancing autophagy via the mTOR pathway and promoting pyroptosis mediated by cathepsin B. These findings provide valuable insights into the molecular mechanisms underlying the therapeutic potential of icariin and curcumol for prostate cancer treatment.

Abstract In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the versatile video coding (VVC) encoder for VANETs. In this paper, a low-complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray-level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra-prediction mode optimization technique based on CU texture complexity aims to combine intra-prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially reduce encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be reduced by up to 53.29% when compared to the VVC reference model, although the average BD rate is only raised by 1.26%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders used in connected vehicle applications.

Nonalcoholic fatty liver disease (NAFLD) encompasses a disease continuum from simple steatosis to nonalcoholic steatohepatitis (NASH). However, there are currently no approved pharmacotherapies for NAFLD, although several drugs are in advanced stages of clinical development. Because of the complex pathophysiology and heterogeneity of NAFLD, the identification of potential therapeutic targets is clinically important. Here, we demonstrated that tripartite motif 56 (TRIM56) protein abundance was markedly downregulated in the livers of individuals with NAFLD and of mice fed a high-fat diet. Hepatocyte-specific ablation of TRIM56 exacerbated the progression of NAFLD, while hepatic TRIM56 overexpression suppressed it. Integrative analyses of interactome and transcriptome profiling revealed a pivotal role of TRIM56 in lipid metabolism and identified the lipogenesis factor fatty acid synthase (FASN) as a direct binding partner of TRIM56. TRIM56 directly interacted with FASN and triggered its K48-linked ubiquitination-dependent degradation. Finally, using artificial intelligence-based virtual screening, we discovered an orally bioavailable small- molecule inhibitor of FASN (named FASstatin) that potentiates TRIM56-mediated FASN ubiquitination. Therapeutic administration of FASstatin improved NAFLD and NASH pathologies in mice with an optimal safety, tolerability, and pharmacokinetics profile. Our findings provide proof of concept that targeting the TRIM56/FASN axis in hepatocytes may offer potential therapeutic avenues to treat NAFLD., Introduction Nonalcoholic fatty liver disease (NAFLD) is the most prevalent metabolic dysfunction-associated steatotic liver disease due to the global obesity epidemic (1-3). The complex interplay between genetic, epigenetic, metabolic, dietary [...]

Ischemic stroke (IS), which is the third foremost cause of disability and death worldwide, has inflammation and cell death as its main pathological features. IS can lead to neuronal cell death and release factors such as damage-related molecular patterns, stimulating the immune system to release inflammatory mediators, thereby resulting in inflammation and exacerbating brain damage. Currently, there are a limited number of treatment methods for IS, which is a fact necessitating the discovery of new treatment targets. For this review, current research on inflammation and cell death in ischemic stroke was summarized. The complex roles and pathways of the principal immune cells (microglia, astrocyte, neutrophils, T lymphocytes, and monocytes/macrophage) in the immune system after IS in inflammation are discussed. The mechanisms of immune cell interactions and the cytokines involved in these interactions are summarized. Moreover, the cell death mechanisms (pyroptosis, apoptosis, necroptosis, PANoptosis, and ferroptosis) and pathways after IS are explored. Finally, a summary is provided of the mechanism of action of natural pharmacological active ingredients in the treatment of IS. Despite significant recent progress in research on IS, there remain many challenges that need to be overcome.

BackgroundAlthough results from in vitro studies and small randomized controlled trials have shown positive effects of Dazhu hongjingtian injection (DZHJTI) on acute ischemic stroke (AIS), their generalizability to routine clinical practice remains to be established. ObjectiveThe primary aim of this study is to evaluate the effectiveness of DZHJTI treatment for AIS with regard to changes in the stroke-related neurological deficit from baseline to outpatient follow-up, mortality, subsequent vascular events, disability, and traditional Chinese medicine syndrome in real-world clinical settings. By monitoring for adverse events or significant changes in vital signs and laboratory parameters, we also aim to assess the safety of DZHJTI. MethodsThis prospective, multicenter cohort study plans to enroll 2000 patients with AIS within 14 days of symptom onset from 30 hospitals across China. Eligible patients will be followed up for 6 months after initiating medication treatments. The primary outcome will be the change in the National Institute of Health Stroke Scale score from baseline to outpatient follow-up. The secondary outcomes include overall mortality, stroke recurrence, new-onset major vascular events, global disability, and improvement of traditional Chinese medicine syndrome in 6 months. Adverse events or clinically significant changes in vital signs and laboratory parameters, regardless of the severity, will be recorded during the trial to assess the safety of DZHJTI. An augmented inverse propensity weighted estimator will be used to reduce variability and improve accuracy in average treatment effects estimation. ResultsThe clinical trial registration was approved in October 2022, and the recruitment and enrollment of participants started in November 2022. The study’s outcomes are expected to be published in 2025 in reputable, peer-reviewed health-related research journals. ConclusionsThis real-world cohort study is the first to assess the effectiveness and safety of DZHJTI in treating AIS. It may provide additional clinical evidence, including the duration of response, long-term drug effectiveness, and subgroup efficacy data. The study results will be valuable for clinicians and patients seeking optimal treatment for AIS and could lead to better use of DZHJTI and improved patient outcomes. Trial RegistrationITMCTR ITMCTR2022000005; http://tinyurl.com/554ns8m5 International Registered Report Identifier (IRRID)DERR1-10.2196/52447

Transfer-learning has rapidly become one of the most sophisticated and effective techniques in dealing with medical datasets. The most common transfer-learning method uses of a state-of-the-art model and its corresponding parameters as the starting point for new tasks. Recent studies have found that transfer-learning between medical and natural images has minimal advantages, attributed to their different characteristics, even with sufficient data and iterations. This study employs a meta-learning technique, building upon the traditional transfer learning approach, to explore the potential of natural tasks as a starting point for analyzing medical images. In addition, this study investigates the performance of transferring the searched augmentation from natural to medical images. Several studies proposing search algorithms for data augmentation argue that the augmentation techniques can be effectively transferred across different datasets. The results revealed that the transferability between natural and medical images leads to reduced performance owing to the characteristic difference between medical and natural searched augmentation.

With the rapid development of space and information fi eld, hot content distribution intensive scenes will become one of the key directions of satellite network application, and satellite content delivery network (CDN) network is an important means to improve the effi ciency of air and space content distribution.In the architecture of satellite CDN network, due to the uneven time and space of business requirements, the scarcity of satellite resources and the insuffi cient adaptability of existing scheduling algorithms, scheduling algorithms for satellite resources are faced with problems such as high resource dimension, many computing states and large amount of computation, which will reduce the accuracy, response speed and computing performance of scheduling decisions.To solve this problem, a resource scheduling algorithm based on Deep Q-Learning (DQN) algorithm was proposed to improved the effi ciency and accuracy of satellite resource scheduling, and intelligently and quickly perceived the resource situation to make scheduling decisions.Firstly, the user requests were classifi ed, and the shortest path set that the satellite could communicated with was calculated according to the time-varying trajectory of the satellite and the resources of the satellite and the ground.After that, the related information of satellites and users was quantifi ed by Markov model modeling, and the optimal CDN storage node of satellites was calculated by DQN algorithm, which achieved the eff ects of reduced user request delay, reduced satellite-ground resource occupancy rate and improved cache hit rate.