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.

ObjectiveTo investigate the effect of SU056， an azo-podophyllotoxin derivative， on carbon tetrachloride （CCl₄）-induced liver fibrosis in mice and related mechanisms of action. MethodsA total of 12 mice were randomly divided into control group， model group （CCl₄+normal saline）， and treatment group （CCl₄+SU056）， with 4 mice in each group. Mice were given intraperitoneal injection of CCl₄ to establish a model of liver fibrosis， and during the middle stage of modeling， the mice in the treatment group were given daily intraperitoneal injection of SU056. Liver histopathological injury， collagen deposition， and liver function were assessed based on HE staining， Masson staining， Sirius Red staining， the content of hydroxyproline in liver tissue， and the serum levels of alanine aminotransferase and aspartate aminotransferase， and immunofluorescence assay was used to measure the expression levels of smooth muscle actin α （α-SMA）， collagen type Ⅰ， and Y-box binding protein 1 （YB1）. The human hepatic stellate cell （HSC） line LX-2 and primary mouse HSC were used， and CCK-8 assay was used to measure cell proliferation； Transwell assay was used to observe cell migration； quantitative reverse transcription-polymerase chain reaction and Western Blot were used to measure the expression levels of collagen type Ⅰ， collagen type Ⅲ， YB1， phosphorylated mammalian target of rapamycin （mTOR）， and phosphorylated S6K， so as to validate the function of the YB1/mTOR signaling axis. The one-way or two-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsIn the mouse model of liver fibrosis induced by CCl₄， compared with the model group， the treatment group had significant alleviation of inflammatory cell infiltration， collagen deposition， and pseudolobule formation in liver tissue and significant reductions in the serum levels of alanine aminotransferase and aspartate aminotransferase and the content of hydroxyproline in liver tissue （all P<0.01）. Immunofluorescence assay showed that SU056 significantly inhibited the abnormal high expression of α-SMA， collagen type I， and YB1 in liver tissue （all P<0.01）. In vitro experiments showed that SU056 inhibited the transforming growth factor-β1-induced proliferation of LX-2 cells （P<0.01）， the migration of LX-2 cells （P<0.05）， and the transcriptional up-regulation of collagen type Ⅰ and collagen type Ⅲ （all P<0.05） in a dose-dependent manner， and SU056 could inhibit the spontaneous activation of primary HSC in vitro. Mechanistic studies revealed that transforming growth factor-β1 simultaneously upregulated the expression levels of YB1， phosphorylated mTOR， and phosphorylated S6K in LX-2 cells， and treatment with SU056 （10 and 20 µmol/L） could downregulate the protein expression levels of collagen type I， YB1， phosphorylated mTOR， and phosphorylated S6K. Specific knockdown of YB1 or administration of the mTOR inhibitor rapamycin exerted a similar effect as SU056. SU056 also inhibited the co-upregulation of α-SMA and phosphorylated mTOR in liver tissue of model mice （P<0.01）. ConclusionSU056 can effectively inhibit HSC activation， proliferation， migration， and extracellular matrix production both in vivo and in vitro and thus delay the progression of liver fibrosis， by disrupting the YB1/mTOR positive feedback signaling axis.

With the accelerating aging of the population, the proportion of elderly patients with lung cancer continues to rise, presenting multiple challenges to perioperative management. This paper systematically reviews the clinical characteristics of elderly lung cancer patients. Based on the comprehensive geriatric assessment, it proposes incorporating seven major geriatric syndromes—frailty, delirium, sarcopenia, cognitive impairment, malnutrition, dysphagia, and mood disorders—into the core evaluation system. By integrating multimorbidity management with complication prevention and control, an integrated "geriatric syndrome-multimorbidity-complication" perioperative management model is constructed. Furthermore, this paper outlines stratified intervention strategies for geriatric syndromes, a "five-step" workflow for comorbidity management, and a comprehensive intervention pathway for complications across the preoperative, intraoperative, and postoperative phases. Multidisciplinary team (MDT) collaboration serves as the core mechanism to achieve individualized comprehensive treatment. This paper aims to provide a novel perioperative comprehensive treatment model for elderly lung cancer patients, which is centered on geriatrics, supported by multidisciplinary collaboration, and guided by precision medicine.

Objective To analyze the impact of digital conversion of screen-film chest radiographs on image quality in patients with pneumoconiosis. Methods Ten high-kilovoltage screen-film chest radiographs from pneumoconiosis patients were digitally converted using three devices, including a digital single-lens reflex camera, a smartphone, and a medical film scanner. The image quality and optical density values before and after image adjustment were compared across different image formats. Results Before adjustment, among the 30 JPEG images, 24 had diagnostic defect areas and 26 had unqualified optical density values. In RAW format, optical density values were not qualified in 18 out of 30 images. In DICOM format, optical density values were not qualified in nine of ten images. After adjustment, optical density values were not qualified in 13 JPEG images, 12 RAW images, and one DICOM image. Comparisons of chest radiograph quality grade distributions before and after adjustment showed significant differences for all three image formats (all P<0.05). Conclusion After being digitally converted by taking images in RAW format with digital photographic equipment and undergoing post-processing adjustment, the quality of screen-film chest radiographs for pneumoconiosis can meet the diagnostic requirements.

The construction of a new cultural system for high-quality development in public hospitals serves as a crucial pillar for achieving their high-quality advancement.During this developmental processe stablishing a cultural framework that aligns with the new development model holds particular significance.Through content analysis methodology,it identifies 18 core elements of the new cultural system for high-quality development in public hospitals.Furthermore it synthesizes seven implementation pathways across three dimensions-organizational patientand employee perspectives:digital leadership organizational reform capability talent innovation capability resource integration capability normative constraint force value co-creation capability and employee support capability.These findings provide both theoretical and practical references for cultivating new cultural constructs that facilitate high-quality development in public hospitals.

Objective:To analyze the correlation between metabolic syndrome indexes and thyroid nodules in individuals receiving health examinations.Methods:It′s a retrospective cohort study. A total of 2 678 individuals who received health examinations in the Health Management Center of the First Hospital of Shanxi Medical University for four consecutive years and met the admission criteria were selected as the research objects. According to the metabolic syndrome index data of health examination, according to the different duration of metabolic syndrome during the observation period, the group-based trajectory model, Bayesian information criterion and average posterior grouping probability were used to determine the best trajectory groups, and the objects were divided into three different metabolic syndrome index trajectory groups: normal, abnormal and recovery group. During the physical examination in 2022 and 2023, the detection of thyroid nodules in each group was followed up, and the difference of detection rate of thyroid nodules in different metabolic syndrome trajectory groups was compared by Log-rank test, and the correlation between different metabolic syndrome index change trajectories and thyroid nodules was analyzed by logistic regression model.Results:The cumulative detection rate of thyroid nodules in normal group, abnormal group and recovery group was 18.8% (77/410), 27.5% (327/1 190) and 24.7% (266/1 078), respectively ( χ2=19.482, P<0.001). In model 4, after adjusting for age, gender, smoking, drinking, staying up late, insomnia, physical activity, family history and other confounding factors, the risk of thyroid nodules in abnormal group and recovery group was still 2.011 times (95% CI: 1.457-2.776) and 2.006 times (95% CI: 1.389-2.897) of that in normal group. Conclusion:There is a positive correlation between metabolic syndrome index and thyroid nodules in individuals receiving health examinations, and metabolic syndrome index can be used as a predictive index of thyroid nodules.

Objective:This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method.Methods:Based on entries from the 2018 China Health and Retirement Longitudinal Study database, a sample of 5 954 elderly individuals was selected. Feature selection using Support Vector Machine Recursive Feature Elimination, Extreme Gradient Boosting (XGBoost) - Recursive Feature Elimination (RFE), and the Lasso algorithm, which was combined with five classifiers-logistic regression, decision trees, random forests, support vector machines, and XGBoost-to explore the classification effectiveness for depressive symptoms in the elderly. Finally, the SHAP method was used to interpret the analysis of the model with the highest receiver operating characteristic curve areas under the curve (AUC).Results:The accuracy of 15 prediction models ranged from 0.702 to 0.743, with AUC between 0.730 and 0.795. Sensitivity was reported at 0.546 to 0.588, while specificity ranges from 0.783 to 0.865. The model XGBoost-RFE-XGBoost presented the highest AUC. Based on SHAP values, the top four factors influencing depressive symptoms in older adults were life satisfaction, duration of nighttime sleep, disability status, and self-rated health.Conclusion:This study developed a highly efficient and interpretable risk prediction model for depressive symptoms in older adults, which could help identify high-risk older adults and give personalized interventions.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim To explore the mechanism of baicalin combined with heat stimulation in treating acute lym-phoblastic leukemia(ALL)based on network pharma-cology and in vitro experiments.Methods The CCK-8 assay was used to screen the suitable conditions for heat stimulation to interfere ALL cell lines Jurkat,CCRF-CEM,Hut-78 and a normal lymphocyte HMy2.CIR,and the effects of baicalin combined with heat stimulation on the proliferation of three ALL cell lines and a normal lymphocyte were tested.The key targets of baicalin combined with fever stimulation for the treatment of ALL were obtained based on network phar-macological analysis,and the potential mechanisms were predicted by gene ontology(GO)annotation and kyoto encyclopedia of genes and genomes(KEGG)en-richment.The expression levels of TNF-α,AKT1,TYMS and CASP3 mRNA in ALL cell lines Jurkat and CCRF-CEM were examined by RT-qPCR with baicalin alone and baicalin combined with heat stimulation.Results The optimal conditions for heat stimulation to intervene ALL cells were 41 ℃ for 24 h,and heat stimulation combined with baicalin synergistically inhibited the growth of ALL cell lines and effectively reduced the cy-totoxicity of baicalin.Based on the network pharmaco-logical analysis,55 intersecting targets of baicalin with ALL diseases and 77 intersecting targets of baicalin with fever were obtained.The results of GO annotation and KEGG enrichment suggested that baicalin com-bined with fever stimulation to intervene ALL might be associated with influencing intracellular reactive oxygen species metabolism,DNA transcription and apoptotic processes involved in cysteine enzymes.Apoptosis,TNF and IL-17 signaling pathways were the key pathways for baicalin combined with heat stimulation in treating ALL.Under heat stimulation at 41 ℃ using SDHA gene as housekeeping gene,in vitro experiments showed that baicalin significantly up-regulated the expression of TNF-α and CASP3,and down-regulated the expression of TYMS in ALL cells.Conclusions Based on net-work pharmacologic analyses and in vitro experiments,baicalin combined with heat stimulation can regulate TNF-α and CASP3 gene levels in ALL cells and de-stroy cellular structure to promote cell apoptosis,thus synergistically treating ALL.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.