OBJECTIVE To form an expert consensus on the clinical application of parenteral direct thrombin inhibitors （DTIs） in patients during the perioperative period. METHODS Led by Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital （the Affiliated Hospital of UESTC）， a multidisciplinary working group was established. Through literature review and the Delphi method， clinical questions related to the rational perioperative use of parenteral DTIs were identified. A structured design was adopted using the “Population-Intervention-Comparison-Outcome” framework； systematic searches were conducted in CNKI， Medline， Embase and other databases. Relevant evidence from randomized controlled trials and cohort studies was included and synthesized. Evidence quality was assessed using the Grades of Recommendations Assessment，Development and Evaluation （GRADE） approach， and recommendations were formulated through multiple rounds of Delphi surveys and expert consensus meetings. RESULTS &CONCLUSIONS Seven recommendations （each with an expert consensus rate exceeding 90%） on the use of parenteral DTIs in perioperative patients were developed. These recommendations specify drug selection， dosing ranges， key monitoring points， and safety management strategies for parenteral DTIs in various scenarios， including the perioperative period of ventricular assist device implantation， the perioperative period of cardiac surgery， perioperative patients with lower-extremity atherosclerotic disease， the perioperative period of percutaneous coronary intervention in patients with acute coronary syndrome， the perioperative period of carotid artery stenting in patients with carotid stenosis， the perioperative period of patients with right heart thrombosis， and patients who develop related thrombosis and dysfunction after a central venous catheter insertion. In addition， warning and management pathways for perioperative bleeding and thrombotic events were proposed. This expert consensus， which is formulated based on the best available evidence， provides evidence-based guidance for standardized and individualized use of parenteral DTIs in perioperative period.

Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

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.

OBJECTIIVE:The positive role of exercise intervention in the prevention and treatment of sarcopenia has received widespread attention,but the optimal exercise dose for elderly sarcopenic patients still needs to be further determined.The article explored the dose-effect relationship between various elements of exercise prescription and the improvement of muscle mass,muscle strength and physical function in community-dwelling elderly patients with sarcopenia,aiming to provide scientific support for the development of exercise prescription for community-dwelling elderly patients with sarcopenia.METHODS:Literature published from the inception to October 9,2024 in PubMed,EMBASE,Scopus,Web of Science,CNKI,WanFang,VIP,and CBMdisc databases was systematically searched.Meta-analysis was performed using RevMan 5.3 software.Standardized mean difference(SMD)and its 95％CI were used as effect statistics.RESULTS:(1)A total of 11 randomized controlled trials were included,with 348 in the trial group and 304 in the control group.(2)Meta-analysis results showed that exercise improved appendicular skeletal muscle mass index,grip strength,and walking speed in elderly patients with sarcopenia(SMDs 0.46,0.63,0.67,P＜0.05).(3)When the frequency of exercise was 2-3 days/week,appendicular skeletal muscle mass index(SMD=0.57,95％CI:0.28-0.86,P＜0.001),grip strength(SMD=0.70,95％CI:0.37-1.02,P＜0.001),and walking speed(SMD=0.69,95％CI:0.20-1.18,P=0.006)were effectively improved in elderly patients with sarcopenia.(4)When the duration of exercise was 25-60 minutes per session,appendicular skeletal muscle mass index(SMD=0.28,95％CI:0.07-0.50,P=0.01),grip strength(SMD=0.37,95％CI:0.16-0.59,P＜0.001),and walking speed(SMD=0.39,95％CI:0.06-0.73,P=0.02)were effectively improved in elderly patients with sarcopenia.(5)When the exercise intensity was moderate,appendicular skeletal muscle mass index(SMD=0.69,95％CI:0.35-1.03,P＜0.001),and grip strength(SMD=0.36,95％CI:0.09-0.64,P=0.009),and walking speed(SMD=0.91,95％CI:0.34-1.47,P=0.002)were effectively improved in elderly patients with sarcopenia.(6)When the dose of exercise cycle was 8-12 weeks,appendicular skeletal muscle mass index(SMD=0.42,95％CI:0.20-0.64,P＜0.001),grip strength(SMD=0.45,95％CI:0.26-0.64,P＜0.001),and walking speed(SMD=0.76,95％CI:0.27-1.25,P=0.002)were effectively improved in elderly patients with sarcopenia.CONCLUSION:Active,regular exercise can improve muscle health in older adults with sarcopenia.It is recommended that older patients with sarcopenia exercise at least 2 to 3 days per week,25 to 60 minutes each time,lasting for 8 to 12 weeks of moderate intensity exercise to improve muscle health.

Objective:To investigate the association of monocyte-to-high-density lipoprotein cholesterol ratio (MHR) with the severity of white matter hyperintensities (WMHs) and its spatial distribution.Methods:Patients admitted to the Department of Neurology, Jiangning Hospital Affiliated to Nanjing Medical University due to various chronic diseases or physical examinations between January 2023 and December 2024 were included retrospectively. Past medical history, clinical and imaging data were collected. The Fazekas scale was used to assess the severity of WMHs. According to the scoring results of periventricular WMHs (PVWMHs) and deep WMHs (DWMHs), WMHs were divided into no/mild group (0-1 points) and moderate/severe group (2-3 points). Multivariate logistic regression analysis was used to determine independent correlation factors for the severity of WMHs, PVWMHs, and DWMHs. Results:A total of 357 patients were included, aged 65.42±9.95 years, with 198 males (55.5%). There were 193 patients (54.1%) in the no/mild group and 164 (45.9%) in the moderate/severe group. Univariate analysis showed that the proportion of patients with hypertension, diabetes, history of cerebral infarction and cerebral hemorrhage, carotid plaque, and age, serum creatinine, monocyte count and MHR in the moderate/severe group were significantly higher than those in the no/mild group (all P<0.05). Multivariate logistic regression analysis showed a significant positive correlation between MHR and the severity of WMHs (odds ratio 3.138, 95% confidence interval 1.042-9.451; P=0.042). Further analysis showed a significant positive correlation between MHR and PVWMHs (odds ratio 3.384, 95% confidence interval 1.111-10.305; P=0.032), but no independent correlation with DWMHs. In addition, age and hypertension, diabetes, history of cerebral infarction and cerebral hemorrhage were significantly positively correlated with the severity of WMHs, PVWMHs and DWMHs. Conclusion:MHR is correlated with the severity of WMHs, and higher MHR is significantly associated with PVWMHs, but not with DWMHs.

Objective:To investigate the correlation between body mass index (BMI)/high-density lipoprotein cholesterol (HDL-C) ratio and cerebral small vessel disease (CSVD) in middle-aged and elderly people.Methods:Consecutive middle-aged and elderly patients (aged ≥40 years) who were hospitalized for chronic disease examinations in the Department of Neurology, Jiangning Hospital Affiliated to Nanjing Medical University between February 2022 and May 2024 were included prospectively. According to the overall burden score of CSVD, they were divided into CSVD group (≥1) and non-CSVD group (0). According to age, they divided into middle-aged group (40-59 years old) and elderly group (≥60 years old). The demographic characteristics and clinical data were collected. Binary multivariate logistic regression analysis was used to determine the independent correlation between BMI/HDL-C ratio and CSVD. Forest plot was used to analyze the correlation between BMI/HDL-C ratio and CSVD in different age groups. Results:A total of 710 patients were included, with an age of 66.0±10.0 years and 361 were males (50.8%). There were 261 patients (36.8%) in the CSVD group and 449 (63.2%) in the non-CSVD group. The BMI/HDL-C ratio in the CSVD group was significantly higher than that in the non-CSVD group (23.60±7.00 vs. 20.78±6.40; P<0.001). Multivariate logistic regression analysis showed that BMI/HDL-C ratio was an independent risk factor for CSVD in middle-aged and elderly populations (odds ratio 1.046, 95% confidence interval 1.027-1.064; P<0.001). There were 475 patients in the elderly group, of which 198 (41.7%) had CSVD; there were 235 patients in the middle-aged group, of which 63 (26.8%) had CSVD. Forest plot analysis showed that the association between BMI/HDL-C ratio and CSVD still had statistical significance in different age groups, but the effect intensity was higher in the elderly group than in the middle-aged group. Conclusion:The BMI/HDL-C ratio is independently correlated with CSVD in middle-aged and elderly population, particularly significant in the elderly population.

Objective To explore the relationship between insulin resistance and idiopathic central precocious puberty(ICPP)in girls and the diagnostic value of insulin resistance.Methods Clinical data of 245 girls aged 4 to 7.5 years with low luteinizing hormone(LH)levels(0.2-0.83 IU/L),normal body weight(body mass index standard deviation score between-2 and+2),and early breast development who visited the Department of Pediatric Endocrinology,Henan Provincial Maternal and Child Health Hospital from January 2022 to March 2025 were retrospectively analyzed.According to the Expert Consensus on the Diagnosis and Treatment of Central Precocious Puberty(2022),patients were assigned to an ICPP group(n=123)or a control group(n=122).Correlations between the homeostasis model assessment of insulin resistance(HOMA-IR)and selected indices were assessed.Multivariable logistic regression was used to evaluate the association between HOMA-IR and ICPP,and the diagnostic performance of various indices for ICPP was evaluated.Results HOMA-IR was higher in the ICPP group than in the control group(P<0.001)and was positively correlated with LH peak(rs=0.467,P<0.05)and the LH peak/FSH peak ratio(rs=0.444,P<0.05).The multivariable logistic regression model including age,BMI,and basal LH showed that HOMA-IR was closely associated with ICPP(OR=2.756,95%CI:1.940-3.913).Receiver operating characteristic curve analysis showed that the areas under the curve for basal LH,HOMA-IR,and their combination in diagnosing ICPP were 0.735,0.735,and 0.805,respectively(P<0.05),and the combined model had a greater area under the curve than either basal LH or HOMA-IR alone(both P<0.05).Conclusions HOMA-IR is closely associated with ICPP in girls with low LH and normal body weight,and combining HOMA-IR with basal LH improves early identification and diagnostic efficiency in this population.