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.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective:To investigate the miRNA-mRNA regulatory network in a rat model of Tourette syndrome(TS)using transcriptomic technology and to screen key signaling pathways and potential traditional Chinese medicine(TCM)candidates for intervention.Methods:A TS rat model was established using iminodipropionitrile(IDPN).RNA sequencing was performed to identify differentially expressed miRNAs and mRNAs in the brain tissues of TS rats.Bioinformatics analysis was applied to construct interaction networks,and network pharmacology was further employed to screen potential TCM compounds.Results:After 7 days of IDPN modeling,the model group exhibited motor and stereotypical behavioral changes,with behavioral scores greater than 3 points.Hema toxylin-eosin(HE)staining revealed irregular neuronal nuclear morphology,uneven chromatin distribution,nuclear pyknosis,and increased glial cell density.KEGG enrichment analysis identified key pathways:calcium signaling pathway,neuroactive ligand-receptor interaction,p53 signaling pathway,ECM-receptor interaction,and TGF-β signaling pathway.miR-125a-3p,miR-106-3p,and miR-760-3p were identified as pivotal miRNAs.Potential TCM candidates included Ajuga decumbens,Acanthopanax bark,Codonopsis pilosula,Stephania japonica,Os Draconis,Notopterygium root,Siraitia grosvenorii,Zanthoxylum nitidum root,Morinda officinalis,and Corydalis yanhusuo.Conclusion:The miRNAs miR-106-3p,miR-125a-3p,and miR-760-3p may mediate TS pathogenesis by altering critical signaling networks,including the calcium signaling pathway,neuroactive ligand-receptor interaction,and ECM-receptor interaction pathways,leading to neuroimmune inflammation and dopaminergic system dysregulation.TCM compounds such as Corydalis yanhusuo and Ajuga decumbens may exert therapeutic effects through multi-component synergistic regulation of these miRNAs and downstream pathways.

Aim To study the role and mechanism of ML210 in glioma.Methods The cell viability was detected by CCK8 assay.The percentage of dead cells was detected by SYTOXstaining.The role of ferroptosis-signaling pathway in gliomas was detected bygenomics.Cell proliferation was observed by EdU staining and clone formation assay.Cell migration ability was detec-ted by scratch healing assay.The apoptosis was detec-ted by flow cytometry.Cell mitochondrial function was assesses by JC-1 staining.The mechanism of action of ML210 was detected by molecular docking coupled with immunoblotting assay(Western blot).The levels of ROS,MDA were observed by ELISA.Results Compared with the control group,ML210 treatment dose-dependently decreased glioma cell viability,in-hibited cell proliferation,migration,and increased cell apoptosis and mitochondrial dysfunction,which were reversed by ferroptosis antagonists.Gene microarray screening showed that 688 genes of the ferroptosissig-naling pathway were aberrant and 10 signaling path-ways were altered in gliomas.Molecular docking re-sults showed that ML210 binding to GPX4 significantly inhibited the protein expression level of GPX4 and pro-moted the elevation of ROS and MDA levels.Conclu-sions ML210 produces anti-glioma cells via GPX4-mediated ferroptosis pathway.

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.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

Objective:To explore the predictive efficacy of the HFA-ICOS score for cancer therapy-related cardiac dysfunction (CTRCD) in Chinese patients with breast cancer and lymphoma.Methods:This study was a single-center retrospective cohort study which included patients with breast cancer and lymphoma who were treated with anthracyclines from February 2018 to February 2025 at the First Affiliated Hospital of Dalian Medical University. Patients were evaluated at baseline with cardiac biomarkers and echocardiography, including left ventricular ejection fraction and global longitudinal strain of the left ventricle. After anthracycline therapy, they were followed up at 1, 3, 6, and 12 months. Data involved biomarkers and echocardiography were collected to determine whether CTRCD had occurred. The patients were categorized into low-risk, intermediate-risk, high-risk, and very-high-risk groups using the HFA-ICOS scoring model. The cumulative probability of CTRCD under different HFA-ICOS risk stratification was analyzed using Kaplan-Meier survival curves. The effect of HFA-ICOS risk stratification on CTRCD was assessed using an univariate Cox proportional hazards regression model. The predictive efficacy of the HFA-ICOS model and its utility in clinical decision-making were assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curves at each time point.Results:A total of 286 patients, aged 55 (44, 61) years, were enrolled, of whom 33 (11.5%) cases were male. And 113 (39.5%) patients developed CTRCD during a median follow-up time of 111 (70, 210) days. HFA-ICOS risk stratification showed that 228 (79.7%) were low-risk, 49 (17.1%) were intermediate-risk, and a total of 9 (3.1%) were high-risk and very high-risk. The difference in the occurrence of CTRCD over time between patients with different HFA-ICOS risk stratification was statistically significant ( Plog-rank<0.001). Cox proportional regression hazards analysis showed an increased risk of CTRCD development in intermediate-risk ( HR=1.95, 95% CI 1.22-3.00, P=0.006) and high-risk and very high-risk patients ( HR=4.12, 95% CI 1.66-8.54, P=0.004) compared with low-risk patients. The ROC curves showed that the area under the curve of the HFA-ICOS model predicting CTRCD was 0.532, 0.597, 0.600 and 0.577 at 1, 3, 6 and 12 months, respectively. The calibration curves indicated Brier scores of 0.041 (95% CI 0.013-0.067), 0.144 (95% CI 0.115-0.173), 0.232 (95% CI 0.215-0.249) and 0.236 (95% CI 0.220-0.251) at 1, 3, 6 and 12 months, correspondingly. The clinical decision curve suggested that clinical intervention may have a net benefit when the risk threshold is between 0.15 and 0.18 at 1 month, between 0.10 and 0.50 at 3 months, and between 0.30 and 0.70 at 6 and 12 months. Conclusion:The HFA-ICOS score could predict the occurrence of CTRCD in patients with breast cancer and lymphoma treated with anthracycline drugs, although its predictive efficacy is limited, and the prediction model requires further validation in a larger population.