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Abstract Patients with traumatic brain injury (TBI) frequently exhibit concomitant immunosuppression. In this study, we evaluated the predictive values of soluble programmed death-1 (sPD-1) and soluble programmed death ligand-1 (sPD-L1) in patients with severe TBI. Peripheral blood sPD-1 and sPD-L1 levels were measured within 48 h of patient admission. A total of 20 healthy volunteers and 82 patients were enrolled in this study. The levels of sPD-1 and sPD-L1 were upregulated in patients with severe TBI (P

Abstract Background With the increasing application of neoadjuvant therapy in rectal adenocarcinoma, there remain many controversies in clinical practical applications. Preoperative radiotherapy (PR) can limit the surgical plane and potentially affect the quality of surgical treatment. This study aimed to investigate the potential impact of PR on the surgical quality of rectal adenocarcinoma. Methods This retrospective study analyzed the clinicopathological data from 6,585 AJCC stage I-III rectal adenocarcinoma in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Kaplan-Meier survival analysis and multivariate Cox proportional were used to assess the impact of PR on survival. Propensity score matching (PSM) was employed to balance the baseline covariates between the PR and non-PR groups and to compare postoperative pathological differences. Results After PSM, PR did not improve overall survival (OS) in stages I (p = 0.33), II (p = 0.37), and III (p = 0.14) patients. Multivariate Cox analysis indicated that PR was not an independent prognostic factor for patients. Restricted cubic spline (RCS) analysis demonstrated a nonlinear negative correlation between OS hazard ratios and both circumferential resection margin (CRM) and lymph node evaluation (LNE). Compared to the non-PR group, patients in the PR group had lower tumor deposits (TD) (p

Abstract Background Porcine epidemic diarrhea virus (PEDV) mainly causes acute and severe porcine epidemic diarrhea (PED), and is highly fatal in neonatal piglets. No reliable therapeutics against the infection exist, which poses a major global health issue for piglets. Luteolin is a flavonoid with anti-viral activity toward several viruses. Results We evaluated anti-viral effects of luteolin in PEDV-infected Vero and IPEC-J2 cells, and identified IC50 values of 23.87 µM and 68.5 µM, respectively. And found PEDV internalization, replication and release were significantly reduced upon luteolin treatment. As luteolin could bind to human ACE2 and SARS-CoV-2 main protease (Mpro) to contribute viral entry, we first identified that luteolin shares the same core binding site on pACE2 with PEDV-S by molecular docking and exhibited positive pACE2 binding with an affinity constant of 71.6 µM at dose-dependent increases by surface plasmon resonance (SPR) assay. However, pACE2 was incapable of binding to PEDV-S1. Therefore, luteolin inhibited PEDV internalization independent of PEDV-S binding to pACE2. Moreover, luteolin was firmly embedded in the groove of active pocket of Mpro in a three-dimensional docking model, and fluorescence resonance energy transfer (FRET) assays confirmed that luteolin inhibited PEDV Mpro activity. In addition, we also observed PEDV-induced pro-inflammatory cytokine inhibition and Nrf2-induced HO-1 expression. Finally, a drug resistant mutant was isolated after 10 cell culture passages concomitant with increasing luteolin concentrations, with reduced PEDV susceptibility to luteolin identified at passage 10. Conclusions Our results push forward that anti-PEDV mechanisms and resistant-PEDV properties for luteolin, which may be used to combat PED.

This paper considers the estimation of component reliability based on system lifetime data with known system signature using the minimum density divergence estimation method. Different estimation procedures based on the minimum density divergence estimation method are proposed. Standard error estimation and interval estimation procedures are also studied. Then, a Monte Carlo simulation study is used to evaluate the performance of those proposed procedures and compare those procedures with the maximum likelihood estimation method under different contaminated models. A numerical example is presented to illustrate the effectiveness of the proposed minimum density divergence estimation method. We have shown that the proposed estimation procedures are robust to contamination and model misspecification. Finally, concluding remarks with some possible future research directions are provided.

IntroductionCoronaviruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), canine coronavirus (CCoV), and feline infectious peritonitis virus (FIPV), have the potential for interspecies transmission. These viruses can be present in complex environments where humans, dogs, and cats coexist, posing a significant threat to both human and animal safety.Methods and resultsIn this study, we developed a novel multiplex TaqMan-probe-based real-time PCR assay for the simultaneous detection and differentiation of SARS-CoV-2, CCoV, and FIPV. Specific primers and TaqMan fluorescent probes were designed based on the N region of SARS-CoV-2 and FIPV, as well as the S region of CCoV, which demonstrated a remarkable sensitivity and specificity toward the targeted viruses, as few as 21.83, 17.25 and 9.25 copies/μL for SARS-CoV-2, CCoV and FIPV, respectively. The standard curve constructed by the optimized method in our present study showed a high amplification efficiency within or near the optimal range of 91% to 116% and R(2) values were at least 0.95 for the abovementioned coronaviruses. A total of 91 samples, including six plasmid mixed mock samples, four virus fluid mixing simulated samples, and 81 clinical samples, were analyzed using this method. Results demonstrated strong agreement with conventional approaches.DiscussionBy enabling the simultaneous detection of three viruses, this method enhances testing efficiency while decreasing costs. Importantly, it provides a valuable tool for the prevalence and geographical distribution of suspected and co-infected animals, ultimately contributing to the advancement of both animal and public health.

Enzootic bovine leukosis, a neoplastic disease caused by the bovine leukemia virus (BLV), was the primary cancer affecting cattle in China before 1985. Although its prevalence decreased significantly between 1986 and 2000, enzootic bovine leukosis has been re-emerging since 2000. This re-emergence has been largely overlooked, possibly due to the latent nature of BLV infection or the perceived lack of sufficient evidence. This study investigated the molecular epidemiology of BLV infections in dairy cattle in Henan province, Central China. Blood samples from 668 dairy cattle across nine farms were tested using nested polymerase chain reaction assays targeting the partial envelope (env) gene (gp51 fragment). Twenty-three samples tested positive (animal-level prevalence of 3.4%; 95% confidence interval: 2.2, 5.1). The full-length env gene sequences from these positive samples were obtained and phylogenetically analyzed, along with previously reported sequences from the GenBank database. The sequences from positive samples were clustered into four genotypes (1, 4, 6, and 7). The geographical annotation of the maximum clade credibility trees suggested that the two genotype 1 strains in Henan might have originated from Japan, while the genotype 7 strain is likely to have originated from Moldova. Subsequent Bayesian stochastic search variable selection analysis further indicated a strong geographical association between the Henan strains and Japan, as well as Moldova. The estimated substitution rate for the env gene ranged from 4.39 × 10−4 to 2.38 × 10−3 substitutions per site per year. Additionally, codons 291, 326, 385, and 480 were identified as positively selected sites, potentially associated with membrane fusion, epitope peptide vaccine design, and transmembrane signal transduction. These findings contribute to the broader understanding of BLV epidemiology in Chinese dairy cattle and highlight the need for measures to mitigate further BLV transmission within and between cattle herds in China.

Mycobacterium bovis (M. bovis), the microorganism responsible for bovine tuberculosis (bTB), is transferred to people by the ingestion of unpasteurized milk and unprocessed fermented milk products obtained from animals with the infection. The identification of M. bovis in milk samples is of the utmost importance to successfully prevent zoonotic diseases and maintain food safety. This study presents a comprehensive description of a highly efficient molecular test utilizing recombinase-aided amplification (RPA)–clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein (Cas) 13a–lateral flow detection (LFD) for M. bovis detection. In contrast to ELISA, RPA–CRISPR–Cas13a–LFD exhibited greater accuracy and sensitivity in the detection of M. bovis in milk, presenting a detection limit of 2 × 100 copies/μL within a 2 h time frame. The two tests exhibited a moderate level of agreement, as shown by a kappa value of 0.452 (95%CI: 0.287–0.617, p < 0.001). RPA–CRISPR–Cas13a–LFD holds significant potential as a robust platform for pathogen detection in complex samples, thereby enabling the more dependable regulation of food safety examination, epidemiology research, and medical diagnosis.

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.