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 Sanet Vision Integrator (SVI)-based visuomotor integration training on the severity of neglect symptoms, upper limb motor function and activities of daily living (ADL) in patients with post-stroke unilateral spatial neglect (USN). MethodsFrom July, 2023 to September, 2025, 41 patients with post-stroke USN in Beijing Bo'ai Hospital were enrolled and randomly assigned to control group (n = 20) and experimental group (n = 21). Both groups received conventional rehabilitation and visual scanning training, while the experimental group received supplementary SVI-based visuomotor integration training, including saccadic, rotation and hand-eye coordination training, for eight weeks. They were assessed with Chinese Behavioral Inattention Test-Hong Kong version (CBIT-HK), Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and modified Barthel Index (MBI) before and after treatment. ResultsAfter treatment, all the scores significantly improved in both groups (|t| > 4.004, P < 0.001), and the total scores of CBIT-HK and the core visual search sub-items (star cancellation, line crossing and letter cancellation), FMA-UE and MBI were better in the experimental group than in the control group (t > 2.096, P < 0.05). ConclusionSVI-based visuomotor training can effectively alleviate the USN symptoms, broaden the span of visual attention, facilitate the functional recovery of the affected upper extremity, and improve ADL in patients after stroke.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To analyze the medical resource consumption of healthcare-associated infection(HAI)in patients in different groups of disease diagnosis-related grouping(DRG)based on the DRG payment model,provide reference for optimizing prevention and control of HAI as well as resource management.Methods Medical records and DRG-related indicators of discharged patients from a municipal hospital in Sichuan Province from January 1 to December 31,2024 were analyzed retrospectively.Medical resource consumption of patients in HAI group and non-HAI group was compared.Differences in average length of hospital stay and average expense per hospitalization be-tween two groups of patients were analyzed using stratified analysis.Results In 2024,HAI incidence of discharged patients in DRG management in this hospital was 1.57％.There were statistically significant differences in age,gender,admission and discharge ways between the HAI group and the non-HAI group(all P＜0.05).The main HAI sites were lower respiratory tract,surgical site,urinary tract,and blood.The time consumption index(1.63 vs 0.85),average length of hospital stay(21.00 vs 5.00 days),expense consumption index(1.53 vs 0.92),ave-rage expense per hospitalization(44 700 vs 7 300),and multiple expense in HAI group were all higher than those in non-HAI group(all P＜0.05).The consumption of medical resources for bloodstream infection was relatively higher.Patients with HAI were mostly concentrated in the groups related to acute leukemia with major complications or co-morbidities(MCC),intracranial or craniotomy surgery with MCC,tracheotomy with mechanical ventilation for 96 hours,as well as gastric,esophageal,and duodenal surgery.The average length of hospital stay and average ex-pense per hospitalization of patients in HAI group were both higher than those in the non-HAI group,differences were statistically significant(both P＜0.05).Conclusion HAI significantly increase the consumption of medical resources.Based on DRG analysis,key disease groups for infection prevention and control can be further identified,and the consumption of medical resources can be more accurately and precisely evaluated,thereby optimizing the allocation of medical resources and improving hospital operational efficiency.

Objective To compare the clinical efficacies among three surgical approaches of small inci-sion in the lower segment of the sternum,small incision in the right axilla and thoracoscopic 3 incisions for re-pairing adult giant atrial septal defects(ASD).Methods The medical records of 112 patients with giant ASD undergoing surgical repair in this hospital from January 2018 to January 2024 were retrospectively analyzed.According to the different surgical approaches,the patients were divided into the group A(n=35,small inci-sion in the lower part of the sternum),group B(n=37,small incision in the right axilla)and group C(n=40,thoracoscopic 3 incisions).The general data,operation time,cardiopulmonary bypass time,blood transfusion volume,incision length,postoperative VAS score,postoperative mechanical ventilation time,ICU stay time,postoperative hospitalization duration,hospitalization cost and postoperative complications occurrence rates were compared among the various groups.Results There were no statistically significant differences in the operation time,cardiopulmonary bypass time,blood transfusion volume,postoperative mechanical ventilation time,ICU stay time and postoperative hospitalization duration among 3 groups(P＞0.05).The hospitalization cost in the group C was higher than that in the groups A and group B,the postoperative VAS score in the group B was higher than that in the group A and C,the incision size in the group C was shorter than that in the group A and group B,and the differences were statistically significant(P＜0.05).All patients were suc-cessfully repaired.The incidence rate of postoperative pulmonary infection in the group A and group C was lower than that in the group B,and the difference was statistically significant(P＜0.05).Conclusion All three surgical approaches could safely and effectively repair adult giant ASD.Different surgical approaches should be selected based on the specific conditions of the patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objectives:To investigate the prognostic value of the CHA2DS2-VASc and R2CHA2DS2-VASc scores in patients with atrial fibrillation(AF)and heart failure(HF).Methods:Patients with AF and HF from hospitals diagnosed by the Heart Failure Center in Guangdong Province between January 2017 and December 2021 were selected.Major adverse cardiovascular events(MACE)were used as the follow-up endpoint.Statistical methods such as the area under the receiver operating characteristic(ROC)curve(AUC),net reclassification index(NRI),and integrated discrimination improvement(IDI)were applied to evaluate the predictive value of the CHA2DS2-VASc and R2CHA2DS2-VASc scores in patients with AF and HF.Results:A total of 1 839 patients were enrolled in this study,comprising 703 patients in the MACE group and 1 136 patients in the non-MACE group.Compared with the non-MACE group,the MACE group exhibited significantly advanced age,higher prevalence of New York Heart Association class Ⅳ and coronary artery disease,lower diastolic blood pressure and estimated glomerular filtration rate levels,and elevated serum N-terminal pro-B-type natriuretic peptide concentrations(all P<0.05).Additionally,significantly lower proportions of patients in the MACE group received angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors,beta-blockers,mineralocorticoid receptor antagonists,or anticoagulant therapy(all P<0.05).Multivariable logistic regression analysis revealed that each 1-point increment in both CHA2DS2-VASc and R2CHA2DS2-VASc scores was associated with approximately 10%increased risk of MACE.ROC curve analysis demonstrated that the AUC values for predicting MACE in AF patients with HF were 0.555(95%CI:0.528-0.582,P<0.001)for CHA2DS2-VASc and 0.576(95%CI:0.549-0.608,P<0.001)for R2CHA2DS2-VASc,indicating marginally superior discriminatory capacity of the R2CHA2DS2-VASc score.Delong's test confirmed statistically significant differences between the two scoring systems(P=0.001).The R2CHA2DS2-VASc score demonstrated a NRI of 0.259(95%CI:0.166-0.352,P<0.001)and an IDI of 0.007(95%CI:0.005-0.010,P<0.001)compared with the conventional CHA2DS2-VASc score.Although the R2CHA2DS2-VASc score exhibited slightly better predictive accuracy and outcome discrimination capacity than the original scoring system,both scores demonstrated suboptimal clinical predictive performance.Conclusions:Both the R2CHA2DS2-VASc and CHA2DS2-VASc scores show suboptimal performance for predicting the risk of MACE in patients with AF and HF,and the predicting performance of R2CHA2DS2-VASc score is marginally superior to CHA2DS2-VASc score in this patient cohort.

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.