Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

An electrochemical sensor based on a chitosan-carbon nanotube(CS-CNT)composite-modified glassy carbon electrode(GCE)was developed in this work for highly sensitive detection of sunset yellow(SY)in food.The CS-CNT composite dispersion was prepared via an ultrasonic dispersion method.Combined with quantum chemical calculations,the adsorption mechanism of SY molecules onto the electrode surface,facilitated by π-π conjugation and electrostatic interactions,was elucidated.The optimized experimental conditions were determined as follows:12 μL of CS-CNT dispersion modification volume,accumulation time of 300 s,and a phosphate buffer solution at pH 7.0 as supporting electrolyte solution.Experimental results demonstrated that CS significantly enhanced the dispersion of CNT,increasing the effective surface area of the modified electrode by 2.22 times(reaching 0.1587 cm2)compared to the bare GCE.The sensor exhibited a linear detection range of 3.0×10-7 mol/L to 1.0×10-5 mol/L,with a detection limit(S/N=3)of 5.0×10-10 mol/L.Satisfactory spiked recoveries ranging from 96.9%to 101.1%were achieved,along with good preparation reproducibility(relative standard deviation,RSD=4.96%).Interference tests indicated high selectivity of the sensor against citric acid,glucose,and common metal ions.The reliability of the sensor was validated through the detection of SY in actual beverage samples.This electrode design simplified operational procedures,avoiding cross-contamination between measurements,and provided an efficient solution for the on-site monitoring of food additives.

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

Abstract: Objective　To investigate the distribution and drug resistance of pathogenic bacteria separated from ascites of patients in Children’s Hospital Affiliated to Zhengzhou University from 2015 to 2021, and to provide a basis for rational clinical antimicrobial agents. Methods　Bacterial culture, bacterial identification and drug sensitivity analysis were performed on 1 058 non-duplicate ascites culture specimens from January 2015 to December 2021. The clinica1 and microbiologica1 data were ana1yzed by WHONET 5.6 and SAS 9.4 Results　Of the 1 058 specimens, 586 (55.39%) were positive for pathogenic bacteria, with a total of 781 strains isolated. There was no significant trend of increase or decrease in the positivity rate over different years. Male children (63.99%) were more prevalent than female children. Appendicitis (59.22%) was the most common disease and Escherichia coli was the most common causative bacteria. Among neonates (≤28 d), the bacteria with the highest detection rate were Klebsiella pneumoniae (23.50%) and Enterococcus faecium (23.50%), while among children (>28 d), the highest detection rate was Escherichia coli (35.98%). Gram-negative bacteria accounted for 64.79% of the 781 strains, mainly Escherichia coli (38.28%), Klebsiella pneumoniae (8.58%), and Pseudomonas aeruginosa (5.89%); Gram-positive bacteria accounted for 29.45%, mainly Enterococcus faecium (8.58%), Streptococcus constellatus (2.69%), and Enterococcus avium (2.43%); fungi accounted for 1.66% and anaerobic bacteria accounted for 4.10%. The resistance rates of Escherichia coli to cefoperazone/sulbactam, piperacillin/tazobactam, imipenem and meropenem were 6.02%, 4.35%, 4.35%, and 3.68%, respectively. The resistance rates of Klebsiella pneumoniae to these drugs were 59.70%, 59.70%, 50.75% and 53.73% respectively. Linezolid-resistant strains of Enterococcus faecium were found. Conclusion　Appendicitis is the most common abdominal infection in children, and the distribution of ascites pathogens varies with ages and diseases. The pathogenic bacteria are mainly Gram-negative bacteria, and the drug resistance of Klebsiella pneumoniae was more serious. It is particularly important to use antibiotics correctly and rationally to reduce the emergence of drug resistant bacteria.

In this study, an overview of systematic reviews/Meta-analysis(SR/MA) of Chinese herbal injections for sepsis was performed to provide references for clinical practice and promote the quality improvement of clinical evidence. Eight Chinese and English databases such as CNKI, Medline, and EMbase were electronically searched for SR/MA of Chinese herbal injections for sepsis from database inception to June 2022. AMSTAR 2, PRISMA 2020, and GRADE system, combined with Recommendations for Clinical Evidence Grading on Traditional Chinese Medicine Based on Evidence Body, were applied to evaluate the methodological quality, reporting quality, and evidence quality of the included articles. Twenty-seven articles of SR/MA were included, containing four Chinese herbal injections(Xuebijing Injection, Shenfu Injection, Shenmai Injection, and Shengmai Injection). AMSTAR 2 checklist showed that the methodological quality of the SR/MA ranged from moderate to very low. Item 2(prior study design) was the critical item with poor scores, and the non-critical items with poor scores were items 3(explain the selection of the study designs), items 10(report on the sources of funding), and items 16(conflicts of interest stated). In terms of PRISMA 2020, items in eight topics with complete reporting of missing>50%, including search strategy, certainty assessment, results of syntheses, certainty of evidence, registration and protocol, support, competing interests, availability of data, code and other materials. The included SR/MA involved 30 outcome indicators. Evidence quality of mortality, APACHE Ⅱ, and safety, the top three outcome indicators, was evaluated, and all of them were graded as the medium level. The lack of random allocation sequence, allocation concealment mechanism, blinding, and trial sample size was the main reason for the reduction of the evidence level. The available evidence shows that Chinese herbal injections can serve as an effective and safe adjunctive treatment for sepsis, which can reduce mortality, inhibit inflammation, improve coagulation function, and regulate immune function, tissue perfusion, and oxygenation in patients with sepsis. However, the quality of SR/MA was suboptimal, and more high-quality SR/MA is needed to provide evidence to support the efficacy and safety of Chinese herbal injections in the treatment of sepsis.
Humans ; Injections ; Medicine, Chinese Traditional ; Research Design ; Sepsis/drug therapy*