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 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 investigate the nutritional risk status of children in PICU and analyze its influencing factors.Methods:From July 2021 to February 2023,all children aged 1 to 18 years admitted to PICU of Beijing Children's Hospital were investigated by using the pediatric Yorkhill Malnutrition Scoring tool(PYMS)and the clinical data questionnaire.Results:A total of 492 children in PICU were enrolled.The first nutritional risk screening results showed that there were 32 cases of no/low nutritional risk(6.5%),76 cases of medium risk(15.4%),and 384 cases of high risk(78.1%).The incidence of medium/high nutritional risk was as high as 93.5%.The PYMS score of nutritional risk in PICU was(2.61±1.42).The results of multiple linear regression analysis showed that weight,fever time before admission,white blood cells,body mass index,primary diagnosis,father's education,and diet before illness were the main influencing factors of nutritional risk of children in PICU(P<0.05).Conclusion:Children in PICU are in a state of high nutritional risk.It is suggested that children in PICU should carry out nutritional screening in a standardized manner,identify children with high nutritional risk and its influencing factors early.To actively conduct nutritional assessment and nutritional intervention could improve the clinical outcome of children in PICU.

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.

Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.

AIM:This study investigated the role of solute carrier family 7 member 11(SLC7A11)in sul-fasalazine(SAS)-induced ferroptosis in acute myeloid leukemia(AML)cells,focusing on the inhibitory effect of nuclear factor E2-related factor 2(NRF2)nuclear translocation-mediated activation of SLC7A11 on ferroptosis and its underlying mechanisms.METHODS:SAS-induced proliferation in AML cell lines,Kasumi-1 and THP-1,was assessed using the MTS assay.Cell death inhibitors were employed to determine the mode of cell death.Lipid reactive oxygen species(ROS)levels were measured by flow cytometry;Fe2+,malonodialdehyde(MDA),glutathione(GSH)levels,and glutathione per-oxidase 4(GPX4)activity were assessed using micromethods.Quantitative PCR(qPCR)was performed to evaluate changes in SLC7A11 mRNA during SAS-induced ferroptosis,while Western blot measured SLC7A11 and GPX4 protein levels.Moreover,Western blot assessed NRF2 nuclear translocation post-SAS treatment.The NRF2 inhibitor ML385 was used to validate these effects.SLC7A11 mRNA and protein levels were then measured following combined SAS and ML385 treatment via qPCR and Western blot.Cell viability and ferroptosis-related indices were evaluated under the same treatment conditions.Furthermore,a shRNA vector targeting SLC7A11 was constructed to assess changes in cell viability and ferroptosis markers after SLC7A11 knockdown with SAS.GPX4 protein levels were examined following SLC7A11 knockdown.RESULTS:SAS significantly inhibited the proliferation of Kasumi-1 and THP-1 cells at 200 μmol/L and 300 μmol/L,respectively(P<0.05).Only ferroptosis inhibitors(Fer-1 and DFO)significantly reversed SAS-induced cy-totoxicity(P<0.01).SAS increased lipid ROS,Fe2+,and MDA levels(P<0.01),while reducing GSH and GPX4 activity(P<0.01).The mRNA and protein expressions of SLC7A11 increased during SAS-induced ferroptosis(P<0.01),where-as GPX4 protein decreased significantly(P<0.01).SAS significantly increased the nuclear-to-cytoplasmic NRF2 ratio(P<0.01),which decreased upon co-treatment with ML385(P<0.05).Following SAS and ML385 co-treatment,both SLC7A11 mRNA and protein levels were downregulated(P<0.01).This combination treatment further reduced AML cell viability(P<0.01),an effect reversed by Fer-1 and DFO(P<0.01).Compared with SAS alone,the combination of SAS and ML385 significantly increased lipid ROS,Fe2+,and MDA while reducing GSH levels and GPX4 activity(P<0.01).SLC7A11 knockdown was successfully achieved.Compared with the NC shRNA group,SLC7A11 knockdown cells showed significantly decreased viability after SAS treatment,which was reversed by Fer-1 and DFO(P<0.01).Lipid ROS,Fe2+,and MDA content were significantly increased(P<0.01),and GSH and GPX4 were substantially decreased(P<0.05).Moreover,GPX4 protein expression was considerably reduced after SLC7A11 knockdown(P<0.01).CONCLUSION:SAS induces ferroptosis in AML cells.It promotes the nuclear translocation of NRF2 protein,which activates SLC7A11 ex-pression.Inhibition of NRF2 or downregulation of SLC7A11 sensitizes AML cells to SAS-induced ferroptosis.

The aim of this study is to explore a new path of empowering ideological and political educa-tion in courses with digital intelligence technology,therefore to improve the teaching quality of biology courses in universities,promote the comprehensive implementation of the fundamental task of cultivating morality and talents,and continuously improve the level of"three pronged education".By using the biol-ogy course"Immunology"as a practical carrier,we have constructed a dual-driven framework of"ideo-logical and political guidance+digital intelligence empowerment"utilizing digital tools to further highlight pedagogical feature enhancement,curricular optimization,innovative teaching models,multidimensional assessment,and enhanced teaching refinement.The empowerment of digital intelligence in the ideological and political construction of the course"Immunology"has achieved notable progress.Students' profes-sional abilities and ideological and political literacy have significantly improved,and the proportion of students with excellent grades has been increasing year by year(81.93％of students with good grades or above in 2024),demonstrating outstanding performance in various social practice activities;The course exhibits excellent demonstration and radiation effects on campus,and the teaching results have been sup-ported by the Ministry of Education and provincial-level education reform projects.This study serves as a practical example for the digital transformation of ideological and political education in higher education courses,providing reference and innovative insights for implementing ideological and political elements in other courses of biology majors.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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