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CRISPR-Cas systems, consisting of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas), are the latest generation of gene editing technology and have been widely used in molecular biology research. CRISPR-Cas systems also have unlimited potential in the field of medicine, especially in the treatment of human viral diseases, such as blocking virus invasion, interfering with virus replication, and eliminating viral genome and sequelae of virus infection. In this article, the latest research progress of CRISPR-Cas9 system and other CRISPR systems in treatments of several viral diseases are reviewed. In addition, the advantages and potential problems of CRISPR systems as treatment options are analyzed to provide ideas for subsequent related research.

Background Glucose phosphate isomerase (GPI) deficiency is a rare autosomal recessive disorder that causes hereditary nonspherocytic hemolytic anemia (HNSHA). Homozygous or compound heterozygous mutation of the GPI gene on chromosome 19q13 is the cause of GPI deficiency. Fifty-seven GPI mutations have been reported at the molecular level. Case presentation A 5-month-old boy was presented with repeated episodes of jaundice after birth. He suffered from moderate hemolytic anemia (hemoglobin levels ranging from 62 to 91 g/L) associated with macrocytosis, reticulocytosis, neutropenia, and hyperbilirubinemia. Whole-exome sequencing showed that he has a missense mutation c.301G > A (p.Val101Met) in exon 4 and a frameshift mutation c.812delG (p.Gly271Glufs*131) in exon 10. Mutation p.Gly271Glufs*131 is a novel frameshift null mutation in GPI deficiency. Conclusion In a patient with recurrent jaundice since birth, mutations in the GPI gene associated with HNSHA should be evaluated. The c.812delG (p.Gly271Glufs*131) variant may be a novel mutation of the GPI gene. Compound heterozygous mutations c.301G > A (p.Val101Met) and c.812delG (p.Gly271Glufs*131) are not relevant to neurological impairment.

Background and aim The natural history of chronic hepatitis B virus (HBV) infection is usually divided into four phases: immune tolerant (IT), immune active (IA), immune carrier (IC), and immune reactive (IR). Many patients still cannot be classified into the four phases, called “Grey Zone (GZ)”. This study aimed to analyze the liver histological features of the GZ patients to guide antiviral therapy. Methods We retrospectively analyzed the 1454 patients with chronic HBV infection who underwent liver biopsy. GZ patients with identical serum hepatitis Be antigen (HBeAg) and alanine aminotransferase (ALT) levels as those in the IT, IA, IC, and IR phases were categorized into the IT-GZ, IA-GZ-1, IA-GZ-2, IC-GZ, and IR-GZ groups, respectively. We analyzed and compared the histological distribution of liver in these patients. We evaluated independent influencing factors for significant liver histological changes (SLHC) in patients in the GZ subgroups. Results Among the 1454 patients, 690(47.5%) patients in GZ. Among the 690 patients of the GZ, 322(46.7%) patients for whom histological examinations indicated SLHC. The proportion of SLHC within the GZ subgroups was as follows: IT-GZ (50.5%), IA-GZ-1 (75.0%), IA-GZ-2 (48.4%), IC-GZ (32.1%), and IR-GZ (59.6%). In the IT-GZ group, the proportion of patients aged ≤ 30 years with SLHC was 47.1%, and in the IC-GZ group, this proportion was 42.1%. Conclusions 46.7% of GZ patients had significant liver histological changes. For HBeAg-negative patients with ALT ≤ 40U/L, HBV DNA ≥ 2000IU/mL, and an age of ≤ 30 years old, antiviral therapy was recommended; if they expressed concern, a liver biopsy was suggested.

Hepatitis E virus (HEV) poses a significant public health concern, particularly among immunocompromised populations. This study aimed to investigate HEV seroprevalence, clinical characteristics, and associated risk factors in people living with HIV (PLWH) in Shanghai, China. A retrospective analysis was conducted on serum IgG and IgM antibodies specific to HEV in 670 PLWH and 464 HIV-negative health-check attendees. The overall anti-HEV seropositivity rate among PLWH was 30.15% (202/670, 95% CI 26.68–33.62), with an IgG positivity rate of 30.00% (201/670, 95% CI 26.53–33.47). IgM positivity was observed in 1.19% (8/670, 95% CI 0.59–2.39) of PLWH, and dual IgM/IgG positivity was observed in 1.04% (7/670, 95% CI 0.50–2.16) of PLWH. The seropositivity rate of anti-HEV IgG in the HIV-negative health-check attendees was 17.67% (82/464, 95% confidence interval: 14.20–21.14), with no IgM positivity, which was significantly lower than that in PLWH (χ2 = 22.84, p < 0.001). Univariate and multivariate analyses identified advanced World Health Organization (WHO) HIV stage (III/IV) as an independent risk factor for HEV co-infection (p < 0.05). Notably, no significant associations were observed with age, gender, CD4 count, or liver function parameters. These findings underscore the importance of implementing HEV screening protocols and developing targeted preventive strategies for PLWH.

To address sparse meteorological data and the “smoothing effect” over complex terrain, this study proposes a spatiotemporal model based on a Diffusion Graph Convolutional Network (DG model). Focusing on Quanzhou, China, and using 2020–2024 data from 198 stations, the model integrates diffusion graph convolution and residual learning to capture nonlinear meteorological patterns. Ensemble experiments (100 iterations) demonstrate that the DG model significantly outperforms Ordinary Kriging and the KCN baseline in stability and accuracy. Specifically, it improves mountainous temperature prediction by 23.4% (40.0% vs. KCN) through terrain-adaptive weighting, effectively reproducing physical distribution characteristics. Furthermore, the model reduces inherent ERA5 reanalysis bias by integrating historical station data while maintaining background consistency. Validated against spatial-only (OSI) and temporal-only (OTI) variants, the DG model offers a robust approach for high-resolution meteorological reconstruction in complex terrain.

Hepatocellular carcinoma (HCC) is one of the most common and aggressive malignancies worldwide, with chronic hepatitis B virus (HBV) infection being the primary etiology. HBV-induced liver fibrosis and cirrhosis are significant pathological foundations for the development of HCC. Although several predictive models for HCC in patients with chronic hepatitis B (CHB) exist, a unified model for predicting the progression from cirrhosis, based on pathological diagnosis, to HCC has not yet been established. This study aims to explore the probability and predictive factors of intrahepatic malignant tumor development from a pathological perspective, providing a theoretical basis for clinical intervention. This retrospective study enrolled patients with HBeAg-positive CHB who had pathological cirrhosis (Scheuer/Ludwig stage S4) at the Shanghai Public Health Clinical Center before April 2023. Inclusion criteria comprised persistent HBsAg positivity for at least 6 months and pathological cirrhosis (stage S4) with disease remission following antiviral therapy. Exclusion criteria included cirrhosis stages 0-3, concurrent infections with other viruses, and severe comorbidities. A total of 471 patients were included, with 34 developing HCC during follow-up. Patients were randomly assigned to a training set ( = 328) and a validation set ( = 143). Univariate and stepwise multivariate logistic regression analyses were performed to identify independent risk factors for HCC, and a predictive nomogram was constructed. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), concordance index (C index), calibration curves, and decision curve analysis (DCA). The incidence of HCC was 4.89% in the training set and 2.34% in the validation set. Univariate analysis identified age, CHE, WBC, Hb, PLT, ANC, AMC, HA, and CIV as significantly associated with HCC development. Multivariate analysis confirmed age, WBC, C4, and CIV as independent predictive factors. The nomogram based on these factors demonstrated satisfactory predictive performance, with AUC values of 0.869 and 0.762 in the training and validation sets, respectively. Calibration curves showed good agreement between predicted and actual outcomes in both sets. Decision curve analysis indicated that the model's net benefit was significantly higher than that of \"treat-all\" or \"treat-none\" strategies when the high-risk threshold was set between 5% and 40%, highlighting its clinical utility. This study developed a predictive model for HCC based on age, WBC, C4, and CIV in patients with HBV-related cirrhosis. The model effectively predicted the risk of HCC and provided a reference for clinical intervention. Despite limitations in sample size, the model exhibited robust predictive performance and clinical applicability. Future work should validate the model in multicenter studies and integrate multi-omics data to develop a more comprehensive predictive system.

To prevent swine fever transmission, swine farms in China adopt enclosed management, making strict farm personnel biosecurity essential for minimizing the risk of pathogen introduction. However, current shower-in procedures and personnel movement records on many farms still rely on manual logging, which is prone to omissions and cannot support enterprise-level supervision. To address these limitations, this study develops a digital personnel management system designed specifically for the changing-room environment that forms the core biosecurity barrier. The proposed three-tier architecture integrates distributed identification terminals, local central controllers, and a cloud-based data platform. The system ensures reliable identity verification, synchronizes templates across terminals, and maintains continuous data availability, even in unstable network conditions. Fingerprint-based identity validation and a lightweight CAN-based communication mechanism were implemented to ensure robust operation in electrically noisy livestock facilities. System performance was evaluated through recognition tests, multi-frame template transmission experiments, and high-load CAN/MQTT communication tests. The system achieved a 91.4% overall verification success rate, lossless transmission of multi-frame fingerprint templates, and stable end-to-end communication, with mean CAN-bus processing delays of 99.96 ms and cloud-processing delays below 70.7 ms. These results demonstrate that the proposed system provides a reliable digital alternative to manual personnel movement records and shower duration, offering a scalable foundation for biosecurity supervision. While the present implementation focuses on identity verification, data synchronization, and calculating shower duration based on the interval between check-ins, the system architecture can be extended to support movement path enforcement and integration with wider biosecurity infrastructures.

ObjectiveQuantitative hepatitis B surface antigen (HBsAg) and hepatitis B virus (HBV) DNA in the natural history of chronic HBV infection have not been rationally evaluated. This study aimed to re-characterize quantitative HBsAg and HBV DNA in the natural history phases.MethodsA total of 595 and 651 hepatitis B e antigen (HBeAg)-positive patients and 485 and 705 HBeAg-negative patients were assigned to the early and late cohorts, respectively. Based on the ‘S-shape’ receiver operating characteristic (ROC) curves, the HBeAg-positive sub-cohorts with possibly high HBV replication (PHVR) and possibly low HBV replication (PLVR) and the HBeAg-negative sub-cohorts with possibly high HBsAg expression (PHSE) and possibly low HBsAg expression (PLSE) were designated.ResultsThe areas under the ROC curve (AUCs) of HBsAg and HBV DNA in predicting HBeAg-positive significant hepatitis activity (SHA) in the early cohort, sub-cohort with PHVR, and sub-cohort with PLVR were 0.655 and 0.541, 0.720 and 0.606, and 0.553 and 0.725, respectively; those in the late cohort, sub-cohort with PHVR, and sub-cohort with PLVR were 0.646 and 0.501, 0.798 and 0.622, and 0.603 and 0.674, respectively. The AUCs of HBsAg and HBV DNA in predicting HBeAg-negative SHA in the early cohort, sub-cohort with PHSE, and sub-cohort with PLSE were 0.508 and 0.745, 0.573 and 0.780, and 0.577 and 0.729, respectively; those in the late cohort, sub-cohort with PHSE, and sub-cohort with PLSE were 0.503 and 0.761, 0.560 and 0.814, and 0.544 and 0.722, respectively. The sensitivity and specificity of HBsAg ≤4.602 log10 IU/ml in predicting HBeAg-positive SHA in the early cohort were 82.6% and 45.8%, respectively; those in the late cohort were 87.0% and 44.1%, respectively. The sensitivity and specificity of HBV DNA >3.301 log10 IU/ml in predicting HBeAg-negative SHA in the early cohort were 73.4% and 60.8%, respectively; those in the late cohort were 73.6% and 64.1%, respectively.ConclusionQuantitative HBsAg and HBV DNA are valuable, but their capabilities are divergent in delimiting the natural history phases.

A novel intelligent wet feed system was designed to accurately match the dynamic nutritional requirements of lactating sows. An experimental study was conducted to compare the performance of this novel feeding system with the traditional manual feeding method. Twenty-two first-parity sows selected through screening were randomly divided into intelligent feeding and manual feeding groups. Feed intake and backfat thickness changes during lactation were monitored, and the growth performance of 30 piglets was assessed. The effects of feeding methods on feed intake, backfat thickness, and piglet growth were evaluated. Results showed that the intelligent group increased the feed intake under high feeding conditions, with feed conversion efficiency improved by 21.8%. A backfat conservation effect was observed, with backfat loss reduced by 82.5% and the daily loss rate being only 16.6% of that in the manual group. Piglet growth performance was improved, with the peak growth rate increased by 14.2% and the growth inflection point brought forward by 10.6%, both reaching medium to large effect sizes. The results indicate that the intelligent wet feeding system improved feed conversion efficiency in sows under high feeding conditions, reduced backfat loss, and enhanced piglet growth rates. These findings provide references for the application of intelligent feeding technology and offer technical pathways for intelligent and efficient pig farming.

The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function.