Explore the vast range of titles available.

The emergence of Rocahepevirus ratti genotype 1 (rat hepatitis E virus; rat HEV) in humans presents an unprecedented threat; however, the risk of rat HEV transmission to humans is not well understood. Here, we report the “Distinguishing Antibody Response Elicitation (DARE)” method, which distinguishes exposure to rat HEV. We use four study sets from China for large-scale population analysis: set 1 (hospital visit) and set 3 (ALT abnormality) from Yunnan province, a biodiversity hotspot, and set 2 (received physical examination) and set 4 (ALT abnormality) from Jiangsu province, a non-hotspot control region. rat HEV exposure risk is significantly higher in Yunnan, with 21.97% (190 of 865) in set 1 and 13.97% (70 of 501) in set 3, compared to 0.75% (9 of 1196) in Jiangsu’s set 2. Six spillover infections for rat HEV are identified in set 1, with one case of abnormal ALT. The rat-1d strains carried by rats are closely related to those human infections. Our study reveals the substantial spillover burden posed by rat HEV in biodiversity hotspots and highlights the utility of DARE method for proactive surveillance of public health emergencies. Rat hepatitis E virus (HEV) can infect humans, but the extent of spillover isn’t well studied. Here the authors develop a serological test that distinguishes exposure to rat HEV from other HEV infection and show substantial spillover in a biodiversity hotspot in China. The method can support surveillance of rat HEV.

Our previous studies unexpectedly indicated that the level of serum hepatitis B core antibody (anti-HBc) was positively correlated with the serum alanine aminotransferase (ALT) level. The aim of this study was to determine whether anti-HBc could serve as a potential biomarker for the detection of liver inflammation in chronic hepatitis B (CHB) patients, especially in patients with normal ALT levels. Serum anti-HBc levels were quantified in 655 treatment-naïve CHB patients, including 45 patients who underwent two liver biopsies (baseline phase and the 78 th weeks of antiviral-treatment). Serum anti-HBc levels increased significantly along with the increasing histology activity index (HAI) score. After antiviral-treatment, patients with HAI score reduction had significant decline in serum anti-HBc level. Multivariate analysis showed that anti-HBc was independently associated with moderate-to-severe hepatic inflammation in patients with normal ALT level. Furthermore, serum anti-HBc showed a high diagnostic accuracy for predicting moderate-to-severe inflammation in both hepatitis B e antigen (HBeAg)-positive and HBeAg-negative CHB patients with normal ALT levels (area under the curve, AUC = 0.87 and 0.75; respectively). Thus, anti-HBc may be a strong indicator for assessing the hepatic inflammatory degree and used for antiviral treatment decisions in CHB patients with normal ALT levels.

Researchers identified anti–BNLF2b total antibody as a novel serologic biomarker for nasopharyngeal carcinoma. This antibody, when combined with the standard screening method, improved the positive predictive value to 44.6%.

A fast and low-cost method for HBV genotyping especially for genotypes A, B, C and D was developed and tested. A classifier was used to detect and analyze a one-step immunoassay lateral flow strip functionalized with genotype-specific monoclonal antibodies (mAbs) on multiple capture lines in the form of pattern recognition for point-of-care (POC) diagnostics. The fluorescent signals from the capture lines and the background of the strip were collected via multiple optical channels in parallel. A digital HBV genotyping model, whose inputs are the fluorescent signals and outputs are a group of genotype-specific digital binary codes (0/1), was developed based on the HBV genotyping strategy. Meanwhile, a companion decoding table was established to cover all possible pairing cases between the states of a group of genotype-specific digital binary codes and the HBV genotyping results. A logical analyzing module was constructed to process the detected signals in parallel without program control and its outputs were used to drive a set of LED indicators, which determine the HBV genotype. Comparing to the nucleic acid analysis to HBV viruses, much faster HBV genotyping with significantly lower cost can be obtained with the developed method.

Background The triglyceride-glucose (TyG) index was significantly associated with insulin resistance (IR). Several studies have validated the effect of TyG index on cerebrovascular disease. However, the value of TyG index in patients with severe stroke requiring ICU admission remains unclear. The aim of this study was to investigate the association between the TyG index and clinical prognosis of critically ill patients with ischemic stroke (IS). Methods This study identified patients with severe IS requiring ICU admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database, and divided them into quartiles based on TyG index level. The outcomes included in-hospital mortality and ICU mortality. The association between the TyG index and clinical outcomes in critically ill patients with IS was elucidated using Cox proportional hazards regression analysis and restricted cubic splines. Results A total of 733 patients (55.8% male) were enrolled. The hospital mortality and intensive care unit (ICU) mortality reached 19.0% and 14.9%, respectively. Multivariate Cox proportional hazards analysis showed that the elevated TyG index was significantly related to all-cause death. After confounders adjusting, patients with an elevated TyG index had a significant association with hospital mortality (adjusted hazard ratio, 1.371; 95% confidence interval, 1.053–1.784; P = 0.013) and ICU mortality (adjusted hazard ratio, 1.653; 95% confidence interval, 1.244–2.197; P = 0.001). Restricted cubic splines revealed that a progressively increasing risk of all-cause mortality was related to an elevated TyG index. Conclusion The TyG index has a significant association with hospital and ICU all-cause death in critically ill patients with IS. This finding demonstrates that the TyG index might be useful in identifying patients with IS at high risk of all-cause death.

Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy.

BackgroundConcise and precise prognostic models are urgently needed due to the intricate genetic variations among hepatocellular carcinoma (HCC) cells. Disorder or change in glycolysis metabolism has been considered one of the “hallmarks” of cancer. However, the prognostic value of glycolysis-related genes in HCC remains elusive.MethodsA multigene prognostic model was constructed by least absolute shrinkage and selection operator Cox regression analysis in the The Cancer Genome Atlas (TCGA) cohort with 365 HCC patients and validated in the International Cancer Genome Consortium (ICGC) cohort with 231 HCC patients. The Kaplan–Meier methodology and time-dependent receiver operating characteristic curve were employed to confirm its predictive capability. A predictive nomogram was established based on the stepwise multivariate regression model. The differential expression of prognostic genes between HCC tissues and normal tissues was verified by quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry in an independent sample cohort with 30 HCC patients.ResultsThe glycolysis-related gene signature and the nomogram model exhibited robust validity in predicting prognosis. The risk score was an independent predictor for overall survival (OS). Expression levels of immune checkpoint genes and cell cycle genes were significantly elevated in the high-risk group. The high-risk group presented high levels of immune exclusion. The risk score can distinguish the effect of immunotherapy in the IMvigor210 cohort. The prognostic gene expression showed a significant difference between HCC tissues and adjacent nontumorous tissues in the independent sample cohort.ConclusionThe currently established glycolysis-related gene signature can accurately predict prognosis and reflect immune status, which may be a therapeutic alternative.

The permeable brick pavement system (PBPs) is one of a widely used low impact development (LID) measures to alleviate runoff volume and pollution caused by urbanization. The performance of PBPs on decreasing runoff volume is decided by its permeability, and it was general described by hydraulic conductivity based on Darcy's law. But there is large error when using hydraulic conductivity to describe the infiltration of PBPs, and which infiltration process is not following Darcy's law, so it is important to find more accurate infiltration models to describe the infiltration of PBPs. The Horton, Philip, Green-Ampt, and Kostiakov infiltration models were selected to find an optimal model to investigate infiltration performance of PBPs via a laboratory-scale experiment, and the maximum absolute error (MAE), Bias, and coefficient of determination (R2) were selected to evaluate the models' errors via fitting with experiment data. The results showed that the fitting accuracy of Kostiakov, Philip, and Green-Ampt models was significantly affected by the monitoring area and hydraulic gradients. Meanwhile, Horton model fitted well (MAE = 0.25–0.32 cm/h, Bias = 0.07–0.11 cm/h, and R2 = 0.98–0.99) with the experiment data, and the parameters of the Horton model often can be achieved by monitoring, such as the maximum infiltration rate and the stable infiltration rate. Therefore, the Horton model is an optimal model to describe the infiltration performance of PBPs, which can also be adopted to evaluate hydrological characterization of PBPs.

Background: Renal injury is a critical health issue in pet dogs, often exacerbated by drug-induced nephrotoxicity such as gentamicin (GM). This study investigated the protective effects of ergosterol (Erg), a natural compound from edible mushrooms, against GM-induced damage in Madin–Darby canine kidney (MDCK) cells. Methods: MDCK cells were treated with GM (0.5–3 mmol/L) for 12 h to establish injury. Erg (1 to 32 μg/mL) was pretreated for 12 h before GM exposure (2 mmol/L). Cell viability, nitric oxide (NO), lactate dehydrogenase (LDH), oxidative stress markers (SOD, GSH, CAT, MDA), inflammatory cytokines (IL-1β, IL-6, TNF-α), renal function indicators (Scr, BUN), and autophagy/apoptosis-related proteins (ATG5, Beclin1, P62, BAX, BCL-2) were assessed via CCK-8, ELISA, fluorescence staining, and Western blot. Statistical significance (p < 0.05) was determined by ANOVA and LSD post hoc tests. Results: GM (2 mmol/L) significantly reduced cell viability (p < 0.01) and elevated NO and LDH levels (p < 0.01). Erg pretreatment (4–8 μg/mL) restored cell viability (p < 0.01), suppressed NO (p < 0.01) and LDH release (p < 0.01), and enhanced antioxidant enzyme activities (SOD, GSH, CAT; p < 0.01). Erg attenuated GM-induced reactive oxygen species (ROS) overproduction (p < 0.01) and decreased pro-inflammatory cytokines (IL-1β, IL-6, TNF-α; p < 0.01). Renal markers Scr and BUN were reduced (p < 0.01). Mechanistically, Erg upregulated autophagy proteins ATG5 and Beclin1 (p < 0.01), reduced P62 accumulation (p < 0.01), and lowered the BAX/BCL-2 ratio (p < 0.01). Conclusions: Erg protects MDCK cells from GM-induced nephrotoxicity by restoring autophagy flux, suppressing mitochondrial apoptosis, and mitigating oxidative stress and inflammation. These findings highlight Erg’s potential as a natural therapeutic agent for canine renal injury. Further in vivo studies are needed to validate its clinical efficacy.