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.

Hereditary carotid body tumor (HCBT) is a rare neuroendocrine tumor mainly caused by germline mutations of the SDHx gene. Its multifocality, familial aggregation, and potential malignant risk pose unique challenges to diagnosis and treatment. This review systematically expounds the molecular mechanism of HCBT, pointing out that SDHx mutations drive tumorigenesis through the “pseudo-hypoxia” pathway and are regulated by epigenetic and somatic mutations. In terms of diagnosis, we emphasize the crucial role of ⁶⁸Ga-DOTATATE PET/CT and SDHB immunohistochemistry and advocate for multigene panel sequencing for patients with high-risk characteristics (such as early-onset, multifocal, family history or malignant signs) to clarify the genetic background. On the basis of existing evidence, we recommend conducting baseline biochemical tests for all newly diagnosed patients and actively suggest genetic screening for high-risk individuals such as those who are young, have multifocal tumors, or have a family history. In terms of treatment, surgery is the main treatment method for HCBT. Nevertheless, individualized strategies still need to be formulated based on tumor classification and patient’s overall condition and genetic background. Comprehensive management measures such as targeted therapy, radionuclide therapy, and standardized family management are crucial for improving the prognosis of patients.

Objective To analyze the efficacy, safety, and learning curve of the three-lobe holmium laser enucleation of the prostate(HoLEP)in the treatment of benign prostatic hyperplasia(BPH)in a county-level hospital.Methods A retrospective analysis was conducted on the clinical data of 65 BPH patients who underwent the three-lobe HoLEP performed by a single surgeon at the Department of Urology, Yining County People's Hospital, during Dec.2023 and Jun.2024.The enucleation efficiency was calculated by dividing the weight of the enucleated prostatic tissue by the enucleation time.A case scatter diagram of enucleation efficiency was plotted according to the chronological order of the operations, and the learning curve was analyzed after fitting.Based on the inflection points of the learning curve, the learning process was divided into the initial learning, mastery, and proficiency phases.The basic clinical data, perioperative indicators, postoperative complications, and follow-up indicators were compared among the different learning phases.Results All 65 procedures were successfully completed.Marked by the enucleation efficiency reaching a plateau, cases 1-20 were defined as the initial learning phase, cases 21-40 as the mastery phase, and cases 41 onwards as the proficiency phase.The prostate volume in the proficiency phase was significantly larger than that in the initial learning and mastery phases(both P＜0.05).There were no statistically significant differences in other baseline characteristics among the three groups(all P＞0.05).The operation time [(105.50±19.12)min vs.(85.25±26.92)min vs.(69.00±23.58)min] and enucleation time [(76.90±14.19)min vs.(63.70±22.24)min vs.(48.80±20.48)min] showed a decreasing trend across the three groups(all P＜0.05).The enucleation efficiency in both the mastery and proficiency phases was significantly higher than that in the initial learning phase [(1.16±0.44)g/min vs.(0.85 ±0.25)g/min, P＜0.05;(1.36±0.49)g/min vs.(0.85±0.25)g/min, P＜0.05].The enucleation efficiency in the proficiency phase was slightly higher than that in the mastery phase, but the difference was not statistically significant(P= 0.389).There was no significant difference in the incidence of perioperative complications among the three groups(all P＞0.05).At the 6-month follow-up, the international prostate symptom score(IPSS), post-void residual(PVR), and maximum urinary flow(Qmax)were significantly improved compared to preoperative values in all three groups(all P＜0.05);however, no significant differences were observed among the three groups(all P＞0.05).Conclusion The three-lobe HoLEP for the treatment of BPH is safe and effective in a county-level hospital setting.Surgeons with some experience in endoscopic surgery can preliminarily master this technique after a learning period of approximately 20 procedures.

Objective To explore the identification method,pathogenesis,clinical characteristics and individualized pharmacotherapy of asymptomatic hyperuricemia after renal transplantation.Methods The pharmacist was on duty at the organ transplant outpatient clinic.During this time,they analyzed and sorted out the medications,identified and differentiated a case of asymptomatic hyperuricemia related to spironolactone in a patient who had undergone a renal transplant,and provided comprehensive care throughout the entire process.Results The asymptomatic hyperuricemia in this patient might be associated with spironolactone,and the adverse reactions of the patient were alleviated by pharmacists through optimizing clinical treatment.Up to now,no hyperuricemia occurred.Conclusions Pharmacists are required to collaborate closely with clinicians to establish medication profiles for patients under long-term follow-up and to closely monitor and evaluate drug-related adverse reactions.Additionally,they should assess the renal function and immune status of transplant recipients promptly and formulate individualized treatment plans in order to enhance the long-term survival of both the transplanted kidneys and the recipients.

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.

In November 23, 2023, a patient with 9 digits traumatic crush injury by machine compression was emergently admitted to the Department of Hand and Microsurgery, Sichuan Modern Hospital. Emergency procedures included amputation the distal stumps and replantation of proximal phalanges of left ring and little fingers. Wounds in both hands were temporarily covered with bone cement. On December 4, 2023, reconstruction of 5 digits were performed. Digital defects were: Type Ⅲ defects of left index and middle fingers and right thumb and index fingers and Type IV defect of right middle finger. All 5 reconstructed digits survived. Subsequent refinements yielded favourable outcomes and all donor toes were preserved completely. At the 14-month follow-up, the reconstructed digits exhibited satisfactory appearance and length without difficulties in daily life and at work.

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 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.