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.

ObjectiveTo investigate the effect of Danggui Shaoyaosan on ferroptosis in the rat model of non-alcoholic fatty liver disease （NAFLD） and explore the underlying mechanism based on the nuclear factor E₂-related factor 2 （Nrf2）/solute carrier family 7 member 11 （SLC7A11）/glutathione peroxidase 4 （GPX4） signaling pathway. MethodsThe sixty SD rats were randomly grouped as follows： control， model， Yishanfu （0.144 g·kg^-1）， and low-， medium-， and high-dose （2.44， 4.88， and 9.76 g·kg^-1， respectively） Danggui Shaoyaosan. A high-fat diet was used to establish the rat model of NAFLD. After 12 weeks of modeling， rats were treated with corresponding agents for 4 weeks. Then， the body weight and liver weight were measured， and the liver index was calculated. At the same time， serum and liver samples were collected. The levels or activities of total cholesterol （TC）， triglycerides （TG）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， and Fe²⁺ in the serum and TC， TG， free fatty acids （FFA）， malondialdehyde （MDA）， superoxide dismutase （SOD）， glutathione peroxidase （GPX）， and Fe²⁺ in the liver were measured. Hematoxylin-eosin staining and oil red O staining were employed to observe the pathological changes in the liver. Immunofluorescence was used to assess the reactive oxygen species （ROS） content in the liver. Mitochondrial morphology was observed by transmission electron microscopy. The protein levels of Nrf2， SLC7A11， GPX4， transferrin receptor 1 （TFR1）， and divalent metal transporter 1 （DMT1） in the liver were determined by Western blot. ResultsCompared with the control group， the model group showed increases in the body weight， liver weight， liver index， levels or activities of TC， TG， ALT， AST， and Fe²⁺ in the serum， levels of TC， TG， FFA， MDA， Fe²⁺， and ROS in the liver， and protein levels of TFR1 and DMT1 in the liver （P<0.01）， and decreases in the activities of SOD， GPX and the protein levels of Nrf2， SLC7A11， and GPX4 in the liver （P<0.05， P<0.01）. Meanwhile， the liver tissue in the model group presented steatosis， iron deposition， mitochondrial shrinkage， and blurred or swollen mitochondrial cristae. Compared with the model group， all doses of Danggui Shaoyaosan reduced the body weight， liver weight， liver index， levels or activities of TC， TG， ALT， AST， and Fe²⁺ in the serum， levels of TC， TG， FFA， MDA， Fe²⁺， and ROS in the liver， and protein levels of TFR1 and DMT1 in the liver （P<0.01）， while increasing the activities of SOD and GPX and the protein levels of Nrf2， SLC7A11， and GPX4 in the liver （P<0.01）. Furthermore， Danggui Shaoyaosan alleviated steatosis， iron deposition， and mitochondrial damage in the liver. ConclusionDanggui Shaoyaosan may inhibit lipid peroxidation and ferroptosis by activating the Nrf2/SLC7A11/GPX4 signaling pathway to treat NAFLD.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

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.

Objective To investigate the expression of microRNA-508-3p(miR-508-3p)in epithelial ovarian cancer(EOC)tissue,its impact on the migration and invasion of ovarian cancer cells,and its regulatory relationship with zinc-finger E-box-binding homeobox 1(ZEB1).Methods The surgical resection of EOC cancer tissues and paired adjacent normal tissues were collected.SKOV3 cells were divided into the NC mimic group(transfected with NC mimic),miR-508-3p mimic group(transfected with miR-508-3p mimic),si-NC group(transfected with si-NC),si-ZEB1 group(transfected with si-ZEB1)and co-transfection group(co-transfected with si-ZEB1 and miR-508-3p mimic).The mRNA expression levels of miR-508-3p and ZEB1 in EOC cancer tissues,adjacent normal tissues and five groups of cells were measured by real-time quantitative polymerase chain reaction.The Transwell assay was used to detect the cell migration and invasion abilities.Results The relative expression levels of miR-508-3p in EOC tissues and adjacent normal tissues were 0.77±0.36 and 1.07±0.40,the relative expression levels of ZEB1 mRNA in EOC tissues and adjacent normal tissues were 2.10±1.21 and 1.29±0.95,and the differences were statistically significant(all P＜0.01).The migration cell number of the NC mimic,miR-508-3p mimic,si-NC,si-ZEB1 and co-transfection groups was 633.00±32.49,319.20±19.89,650.40±25.85,375.00±17.25 and 129.40±17.10;the invasion cell number was 527.20±25.01,288.60±16.68,520.00±25.83,293.40±18.37 and 76.60±8.76;the relative expression levels of miR-508-3p were 1.05±0.37,3.94±1.21,1.01±0.21,1.26±0.34 and 3.40±0.41;the relative expression levels of ZEB1 mRNA were 1.00±0.04,0.58±0.05,1.00±0.08,0.54±0.07 and 0.29±0.03,respectively.The above indicators showed statistically significant differences between the miR-508-3p mimic group and the NC mimic group,between the si-NC group and the co-transfection group(P＜0.01,P＜0.05).Conclusion MiR-508-3p is lowly expressed in EOC cancer tissue,and it may inhibit the migration and invasion of ovarian cancer cells by targeting ZEB1 expression.

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.