ObjectiveTo investigate the correlation between neutrophil-to-lymphocyte ratio （NLR）， platelet-to-lymphocyte ratio （PLR）， systemic immune-inflammation index （SII） and the severity of intrauterine adhesions （IUA）. MethodsThe retrospective study included 380 patients who underwent transcervical resection of adhesions （TCRA） from December 2019 to March 2025. Based on the American Fertility Society （AFS） classification， patients were divided into mild （n=61）， moderate （n=225）， and severe （n=94） groups. NLR， PLR， and SII were calculated from preoperative blood tests. Statistical analyses included Kruskal-Wallis test and ordinal Logistic regression. ResultsNLR， PLR， and SII were significantly higher in the severe IUA group compared to the mild group （P<0.05）， with SII showing the strongest predictive ability （OR=1.004， P=0.001）. The number of intrauterine procedures was an independent risk factor （OR=1.27/level， P=0.016）. The predictive model ［Logit（P）=-0.676+0.241×operation times+0.004×SII］ effectively identified severe IUA cases. ConclusionInflammatory markers （particularly SII） are correlated with IUA severity and may serve as non-invasive tools for clinical assessment.

Anemia is one of the common accompanying symptoms of tumors. Whether chemotherapy-related anemia （CRA） or anemia caused by the disease itself， it greatly affects patients' survival rate， quality of life， and even their confidence in treatment. Currently， Western medicine mainly treats CRA through blood product transfusion and the use of erythropoietin， which can rapidly increase hemoglobin levels but are associated with strong dependence and short duration of efficacy. Therefore， exploring the theoretical basis， treatment methods， and advantages of traditional Chinese medicine （TCM） in managing CRA has become a focus of current research. According to recent clinical observations and related reports， TCM demonstrates favorable clinical efficacy in the treatment of CRA. By reviewing literature on classic TCM prescriptions for CRA， this article summarizes clinical cases， relevant pharmacological studies， and possible mechanisms of action. These analyses show that classic TCM prescriptions for CRA are mainly tonifying formulas， primarily those that tonify qi and blood and strengthen the spleen and kidney， and they offer clear therapeutic efficacy， high safety， and the potential to reduce toxicity and enhance effectiveness. In addition to tonifying formulas， modern prescriptions for CRA， such as those that promote blood circulation and remove stasis， promote new blood generation， and exert detoxifying and anticancer effects， have also been confirmed by clinical research to provide good therapeutic outcomes. By summarizing and analyzing the efficacy and mechanisms of classic TCM prescriptions for CRA and the clinical research status of modern formulas， this article aims to provide new strategies for the clinical diagnosis and treatment of CRA.

Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

With the continuous development of society, a large number of new chemicals are continuously emerging, which presents a challenge to current risk assessment and safety management of chemicals. Traditional toxicology research methods have certain limitations in quickly, efficiently, and accurately assessing the toxicity of many chemicals, and cannot meet the actual needs. In response to this challenge, computational toxicology that use mathematical and computer models to achieve the prediction of chemical toxicity has emerged. In the meantime, as researchers increasingly pay attention to understanding the interaction mechanisms between exogenous chemical substances and the body from the system level, and multiomics technologies develop rapidly such as genomics, transcriptomics, proteomics, and metabolomics, huge amounts of data have been generated, providing rich information resources for studying the interactions between chemical substances and biological molecules. System toxicology and network toxicology have also developed accordingly. Of these, network toxicology can integrate these multiomics data to construct biomolecular networks, and then quickly predict the key toxicological targets and pathways of chemicals at the molecular level. This paper outlined the concept and development of network toxicology, summarized the main methods and supporting tools of network toxicology research, expounded the application status of network toxicology in studying potential toxicity of exogenous chemicals such as agricultural chemicals, environmental pollutants, industrial chemicals, and foodborne chemicals, and analyzed the development prospects and limitations of network toxicology research. This paper aimed to provide a reference for the application of network toxicology in other fields.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

The development of surgery brings about the transformation of surgeons′ con-cepts, and in turn, each renewal of surgical concepts propels progress of surgical techniques. These two aspects complement each other. The treatment of hepatocellular carcinoma is a comprehensive therapy centered on surgery. With the deepening understanding of liver anatomy, the surgical methods have evolved from initial local resection to anatomical liver resection, and then to resection of the tumor-bearing portal vein territory. In recent years, with the emergence of hepatic membrane anatomy, portal plate theory, and three-dimensional visualization, the theoretical and technical systems of laparoscopic anatomical liver resection has become more and more mature. Based on own experience and literature reports, the authors systematically elaborate on the construction of theoretical and technological systems of laparoscopic anatomic liver resection for hepatocellular carcinoma, for reference by colleagues.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the effect of phosphorus-containing replacement solution for the prevention and treatment of hypophosphatemia during continuous renal replacement therapy (CRRT) in critically ill patients with blood phosphorus level ≤1.45 mmol/L, and to provide clinical reference.Methods:It was a historical prospective cohort study. The critically ill patients receiving CRRT with blood phosphorus ≤ 1.45 mmol/L in the intensive care unit of Henan Provincial People's Hospital from October 2021 to January 2023 and from April 2023 to January 2024 was selected as the study subjects. The patients were divided into test group (from April 2023 to January 2024) and control group (from October 2021 to January 2023) according to whether phosphate (1.0 mmol/L) was added to the replacement solution during CRRT, and the differences of clinical data before and after CRRT between the two groups were compared. The patients were divided into hypophosphatemia group and non-hypophosphatemia group according to whether blood phosphorus < 0.81 mmol/L within 24 h after the end of CRRT, and the differences of clinical data between the two groups were compared. Logistic regression analysis was used to analyze the related factors of hypophosphatemia.Results:A total of 149 critically ill patients with blood phosphorus level ≤1.45 mmol/L undergoing CRRT were enrolled in the study, with age of 64(47, 75) years and 87 males (58.4%). Among 149 patients, 84(56.4%) had hypophosphatemia after CRRT, and no hyperphosphatemia occurred. The incidence of hypophosphatemia in test group and control group was 40.0% (30/75) and 73.0% (54/74), respectively. There was no statistically significant difference in baseline clinical data before CRRT between test group and control group (all P>0.05). C-reactive protein ( Z=-3.356, P=0.001), blood calcium ( Z=-3.835, P<0.001) and proportion of hypophosphatemia ( χ2=16.467, P<0.001) in the test group were lower than those in the control group, and blood phosphorus ( Z=3.886, P<0.001) in the test group was higher than that in the control group within 24 h after CRRT. Compared with non-hypophosphatemia group, the proportion of parenteral nutrition ( χ2=6.802, P=0.009) and blood calcium within 24 h after CRRT ( Z=-2.515, P=0.012) in the hypophosphatemia group were higher, and blood phosphorus within 24 h after CRRT ( Z=-10.451, P<0.001), blood phosphorus after 24 h after CRRT treatment ( Z=-5.331, P<0.001) and the proportion of applied replacement solution containing phosphorus ( χ2=16.467, P<0.001) in the hypophosphatemia group were lower. The results of multivariate logistic regression analysis showed that parenteral nutrition ( OR=2.521, 95% CI 1.228-5.175, P=0.012) and application of phosphorus- containing replacement solution ( OR=0.241, 95% CI 0.119-0.491, P<0.001) were independent relevant factors of hypophosphatemia after CRRT in the whole cohort of patients. Conclusions:The application of phosphorus-containing replacement solution in critically ill patients with blood phosphorus level ≤1.45 mmol/L undergoing CRRT is safe and effective, and the incidence of hypophosphatemia is low. Application of phosphorus-containing replacement solution in critically ill patients with blood phosphorus level ≤1.45 mmol/L undergoing CRRT can reduce the incidence risk of hypophosphatemia after CRRT.

Objective:To examine the near-complete genomic analysis of norovirus (NoV) subtype GⅡ.17 [P17] outbreaks in Beijing Chaoyang District from 2014 to 2024.Methods:Data and specimens related to outbreaks of the NoV aggregation in Beijing′s Chaoyang District from 2014 to 2024 were collected. The NoV was identified using real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR). Specimens with positive nucleic acid were amplified by standard PCR, whole genome sequencing and evolutionary analysis. Amino acid site variations were compared.Results:In Chaoyang District, from 2014 to 2024, a total of 637 aggregated outbreaks caused by the NoV infection were reported, of which 584 were successfully typed. The epidemic caused by the GⅡ.17 [P17] subtype accounted for 8.79% (56/637), which was the dominant epidemic gene subtype in 2014-2015, sporadic in 2016-2019, reappeared in 2022, and significantly increased in 2024 (27.27%, 24/88). Outbreaks caused by the GⅡ.17 [P17] subtype occurred mainly from October to December, with the main sites of occurrence in primary schools and kindergartens. This study yielded 53 near-complete genome sequences of the GⅡ.17 [P17] subtype from 46 incidents in Chaoyang District. The GⅡ.17 [P17] subtype sequences of Chaoyang District from 2014 to 2024 were segmented into three subgroups on the evolutionary tree, with sequences from 2014 to 2019, 2022 to April 2024, and May to December 2024 clustered into the d, e, and b subgroups, respectively. In the VP1 region′s P2 area, particularly at the HBGA binding site, subgroups b and e exhibited mutations in 22 and two sites, while subgroups b and e showed mutations in four and one sites, predominantly in the RdRp region.Conclusion:The outbreak caused by the NoV GⅡ.17 [P17] subtype in Chaoyang District from 2014 to 2024 continues, with a significant increase in 2024, and it becomes the dominant gene subtype from October to December. The sequence formation of the NoV GⅡ.17 [P17] subtype in Chaoyang District from January to April 2022 and from May to December 2024 shows two different evolutions, with specific mutation sites, requiring continuous monitoring of the NoV GⅡ.17 [P17] subtype.