Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the devel opment of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.

Background/Aims: Acetyl coenzyme A (acetyl-CoA) is one of the most essential metabolites in cell metabolism but its function and concentration in hepatocellular carcinoma (HCC) remain elusive and controversial. Methods: A comprehensive analysis of acetyl-CoA levels and acetyl-CoA synthetase 2 (ACSS2) expression across a range of samples, including patient specimens from both hepatitis B virus (HBV) positive and HBV negative HCC individuals, HBV-transgenic mouse HCC models, and multiple cell lines. Furthermore, to evaluate the functional significance of ACSS2 in HBV-related HCC, we implemented both genetic and pharmacological inhibition strategies targeting ACSS2. Molecular mechanism and mitophagy assessment were revealed by cleavage under target and tagmentation sequencing, RNA sequencing, bioinformatic analyses, transmission electron microscopy and JC-1 staining. Results: Our study revealed a distinct metabolic signature of HBV-related HCC, marked by elevated acetyl-CoA, which was driven by ACSS2. ACSS2 was upregulated by the carbohydrate response element-binding protein in HBV-related HCC. Furthermore, ACSS2 improved tumor cell proliferation, an effect that was dependent on its enzymatic activity. Mechanistically, ACSS2-induced acetyl-CoA accumulation activated voltage-dependent anion channels 1 transcription through increased H3K27ac occupancy, which subsequently promoted mitophagy and HBV-related HCC tumorigenesis. Notably, targeting ACSS2 by depletion or inhibition with a catalytic inhibitor significantly suppressed tumor growth. Conclusions These findings not only illustrate the interplay between metabolic reprogramming, epigenetic modification, and tumorigenesis in the context of HBV infection, but also highlight ACSS2 as a novel metabolic vulnerability in HBV-related HCC. Therefore, targeting ACSS2 could be a novel strategy against HBV-related HCC.

The chemical constituents were systematically separated from the roots of Berberis polyantha by various chromatographic methods, including silica gel column chromatography, HP20 column chromatography, polyamide column chromatography, reversed-phase C_(18) column chromatography, and preparative high-performance liquid chromatography. The structures of the compounds were identified by physicochemical properties and spectroscopic techniques(1D NMR, 2D NMR, UV, MS, and CD). Four phenylpropanoids were isolated from the methanol extract of the roots of B. polyantha, and they were identified as(2R)-1-(4-hydroxy-3,5-dimethoxyphenyl)-1-propanone-O-β-D-glucopyranoside(1), methyl 4-hydroxy-3,5-dimethoxybenzoate(2),(+)-syringaresinol(3), and syringaresinol-4-O-β-D-glucopyranoside(4). Compound 1 was a new compound, and other compounds were isolated from this plant for the first time. The anti-inflammatory activity of these compounds was evaluated based on the release of nitric oxide(NO) in the culture of lipopolysaccharide(LPS)-induced RAW264.7 macrophages. At a concentration of 10 μmol·L~(-1), all the four compounds inhibited the LPS-induced release of NO in RAW264.7 cells, demonstrating potential anti-inflammatory properties.
Plant Roots/chemistry* ; Animals ; Mice ; Berberis/chemistry* ; RAW 264.7 Cells ; Macrophages/immunology* ; Drugs, Chinese Herbal/isolation & purification* ; Nitric Oxide/metabolism* ; Molecular Structure ; Anti-Inflammatory Agents/isolation & purification*

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project "Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.
Stomach Neoplasms/genetics* ; Humans ; Precision Medicine/methods* ; China ; Immunotherapy/methods*

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

OBJECTIVE To explore the clinical characteristics of patients with hematologic tumors combined with Fusarium infection and analyze the prevention and control measures.METHODS Six patients with hematologic neo-plasms combined with Fusarium infection diagnosed at the First Medical Center of the People's Liberation Army General Hospital from Apr.2019 to Dec.2023 were selected as research objects.Through retrospective analysis of patients' clinical data,the clinical manifestations,diagnosis,treatment and prevention strategies of Fusarium in-fection in hematologic neoplasms were analyzed.RESULTS All six patients with hematologic neoplasms combined with Fusarium infection were neutropenic or deficient patients,with main symptoms including moderate fever,painful skin nodules,rash,skin broken and crusted,and scrotal swelling and pain.Patients with severe neutrophil deficiency were susceptible to blood-borne Fusobacterium infections.Four patients had a markedly elevated G-test and Fusorium was first detected by microbiome metagenomic next-generation sequencing(mNGS)in blood,earli-er than traditional pathogenic culture methods.Five patients had Fusarium detected in urine or stool cultures.All six patients received empirical antibacterial and antifungal treatments,but the fungal infection treatment effects were poor.Treatment was adjusted according to the pathogenetic findings,mainly using a combination regimen based on liposomal amphotericin B or posaconazole tablets,with three patients cured and three death.Two pa-tients were from the same ward with a sixteen-day interval.Although no evidence of infection transmission was found,there was still a risk of cross-infection in patients with hematological malignancies and severe immunodefi-ciency.Measures for the prevention and control of hospital-acquired infections were implemented for patients with Fusarium infection and the ward.CONCLUSIONS The clinical manifestations of patients with hematological tumors combined with Fusarium infection are complex and varied with high mortality rates.MNGS testing is valuable in the early diagnosis of Fusarium infection,and it is necessary to explore new treatment options and hospital-ac-quired infectious disease prevention and control measures to improve the prognosis.

Objective To explore the reproductive health literacy of young female sailors,investigate the influence of long voyage on the health,and propose strategies on health education.Methods A total of 49 young female sailors were enrolled.Their healthy status was evaluated by annual medical records.The questionnaire survey was also conducted to evaluate general status related to reproductive health,knowledge pertaining to reproductive health,and health status after long voyage.Results Some female sailors had symptoms of thyroid diseases,such as hypothyroidism(32.65%),benign thyroid nodules(59.18%),and suspected papillary carcinoma(in one sailor,2.04%).Ultrasonography revealed breast nodules in 5 subjects(10.00%),mammary gland hyperplasia in 46 subjects(93.88%)and reproductive benign diseases in 6 subjects(12.24%).The physical and mental states significantly changed in the late stage of long voyage.The changes in work and living environment were the main factors affecting the emotional and psychological status in the long voyage.The discomfort in menstrual cycle piled up with the above-mentioned changes during a long voyage,and the menstruation significantly affected the duty of 36.73%of female sailors during a long voyage.Female sailors had little knowledge about the prevention and examination of breast cancer and thyroid cancer.Conclusion The living and work environment during a long voyage may affect the health of the thyroid,mammary gland and reproductive system of young female sailors.Active health education via online and offline ways is helpful to improve the health literacy and self-care of female sailors,subsequently enhancing the combat effectiveness.