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 study the improvement effect and mechanism of Tripterygium wilfordii multiglucoside （TWM） on nephrotic syndrome in rats. METHODS The nephrotic syndrome model was established by intravenous injection of adriamycin via the tail vein. The modeling rats were randomly divided into the model group （distilled water）， prednisone group （10 mg/kg）， and TWM high- and low-dose groups （10 and 5 mg/kg， respectively）. Additionally， blank group （distilled water） without model induction was established. Each group consisted of 9 rats. Rats in each group were administered the corresponding drugs or distilled water by gavage， once a day， for 6 consecutive weeks. The histopathological morphology of kidney tissues in rats was observed； the levels of 24-hour urinary protein （24 h-UTP） and serum biochemical indicators [albumin （ALB）， blood urea nitrogen （BUN）， serum creatinine （SCr）， cholesterol （CHOL）， and triglyceride （TG）] in rats were determined； the levels of oxidative stress indicators [superoxide dismutase （SOD）， malondialdehyde （MDA）] in kidney tissue of rats were determined； expressions of hypoxia-inducible factor-1α （HIF-1α）/microRNA-155-5p （miR-155-5p）/nuclear factor erythriod 2- related factor 2 （Nrf2） signaling pathway-related mRNA and protein in the renal tissues of rats were detected. RESULTS Compared with the blank group， the rats in the model group exhibited disordered renal tissue structure， with a small amount of glomerular necrosis and edema of the renal tubular epithelial cells. 24 h-UTP， serum levels of SCr， BUN， CHOL and TG， MDA content， mRNA and protein expressions of HIF-1α and Keap1 as well as the expression of miR-155-5p in renal tissues were increased significantly （ P ＜0.05）. Serum level of ALB， SOD level in renal tissue as well as mRNA and protein expressions of Nrf2 were decreased significantly （ P ＜0.05）. Compared with the model group， TWM high-dose and low-dose groups exhibited significant improvements in renal injury， with notable reversals in the levels of the above quantitative indicators （ P ＜0.05）. CONCLUSIONS TWM can alleviate oxidative stress-induced damage and thereby improve nephrotic syndrome in rats by regulating the HIF-1α/miR-155-5p/Nrf2 signaling pathway.

Objective: To explore the impact of adverse childhood experiences (ACEs) on health risk behaviors (HRBs) among college students and the mediating role of psychological symptoms, so as to provide a basis for developing intervention strategies. Methods: From March to April 2023, a convenience cluster sample of 1 801 students from 12 universities in Nanning, Liuzhou, Guilin, Wuzhou of Guangxi completed an online survey. A self designed questionnaire, Adverse Childhood Experiences-International Questionnaire (ACE-IQ) and Symptom Checklist-90 (SCL-90) were used for evaluation tools. Binary Logistic regression, structural equation modeling (SEM) and Bootstrap methods were used to analyze the associations and mediating effects. Results: Overall, 71.2% of college students experienced at least one type of ACE, with emotional neglect (40.3%) and emotional abuse ( 25.2 %) having the highest detection rates. The top three HRBs were unhealthy diet (77.8%), physical inactivity (54.1%), and smoking/alcohol use (18.5%). Logistic regression showed that poor family functioning, abuse, and extra familial violence were each associated with an increased risk of smoking/alcohol use ( OR =1.14, 1.11, 1.18) and deliberate self harm ( OR =1.26, 1.19,1.30) (all P <0.05). Experience of abuse increased the risk of high risk sexual behavior and family dysfunction increaded the risk of physical inactivity, respectively ( OR = 1.07 , 1.04, both P <0.05). Mediation analysis revealed that anxiety ( β =0.20) and depression ( β = 0.09 ) partially mediated the pathway from poor family functioning to deliberate self harm; paranoia ( β =0.02) partially mediated the pathway from abuse to high risk sexual behavior; and obsessive-compulsive symptoms ( β =0.26) and depression ( β =0.10) partially mediated the pathway from extra familial violence to deliberate self harm (all P <0.05). Conclusion Psychological symptoms play a mediating role in the association between ACEs and HRBs, and mental health interventions may reduce the risk of HRBs among college students.

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.

Complex liver resection (CLR) is a collective term for surgical procedures addre-ssing complex invasion of intrahepatic vasculobiliary structures that cannot be radically resected through conventional methods. The ex vivo liver resection and autotransplantation (ELRA) and its modified techniques have significantly enhanced the technical feasibility of CLR implementation. In recent years, advancements in modified ELRA techniques and derivative procedures, including conversion resection, in-situ hypothermic perfusion, and auxiliary liver transplantation, have further diversified CLR methodologies, offering more personalized treatment options for CLR candidates. Given the complexity of such cases and substantial variations in surgical approach selection, improving procedural safety and scalability remains a critical challenge in CLR practice. The authors review the current application of modified techniques based on ELRA in CLR, evaluate the clinical significance based on institutional experiences, and propose future directions and individual selection for advancing the safe implementation of CLR.

OBJECTIVE To understand the drug resistance genes,virulence genes and molecular epidemiological characteristics of extensively drug-resistant hypervirulent Klebsiella pneumoniae(XDR-hvKP)strains causing hospital-associated infection.METHODS The clinical isolates of XDR-hvKP were collected from Tongling People's Hospital from Jul.2020 to Dec.2022.The strains were identified by matrix-assisted laser desorption ionization-time-of-flight(MALDI-TOF)mass spectrometry,the common drug resistance genes and virulence genes were an-alyzed by Sanger sequencing,the capsular serotypes were determined by wzi gene sequencing;the drug resistance genes,virulence genes and ST subtypes were observed by means of whole-genome sequencing(WGS)technique.RESULTS Totally 18 strains of XDR-hvKP were collected,55.56％(10/18)of which were isolated from blood specimens,and 61.11％(11/18)were isolated from critical care medicine department.Sanger sequen-cing analysis showed that all of the strains carried blaKPC-2 drug resistance gene;rmpA2(100.00％)and rmpA,i-roN,iutA(94.44％,17/18)were the major virulence genes carried by the strains.WGS analysis indicated that all of the 18 XDR-hvKP isolates carried multiple drug resistance genes such as blaKPC-2 carbapenemase and the viru-lence genes like capsule(rmpA/rmpA2),aerobacterin(iucABCD-iutA),Salmonella(iroN)and yersinin(ybt).All of the ST subtypes were ST 11,and all of the capsular serotypes were KL 64.CONCLUSIONS The ST11-KL64 type XDR-hvKP strains carry blaKPC-2;rmpA,rmpA2,iroN and iutA are the major virulence genes.It is necessary to strengthen the monitoring of key population of the key departments and make joint efforts of multiple departments to contain the transmission of the strains.

Objective:To study the risk factors of thyroid nodules in patients with type 2 diabetes.Methods:Data of patients with type 2 diabetes with normal thyroid function admitted to the Department of Endocrinology of Heze Municipal Hospital from January to June 2024 were collected. Binary logistic regression was used to analyze the influencing factors of thyroid nodules in patients with type 2 diabetes, and the receiver operating characteristic curve (ROC curve) was used to evaluate the diagnostic efficacy of each influencing factor.Results:Among 162 patients with type 2 diabetes, 96 had thyroid nodules, accounting for 59.3%. The incidence of thyroid nodules in women was significantly higher than that in men (χ 2 = 4.56, P = 0.034). Multivariate logistic regression analysis showed that age (≥50 years old), overweight and obesity [body mass index (BMI)≥24.0 kg/m 2], high glycated hemoglobin (≥10%), and high total cholesterol ( > 6.5 mmol/L) were independent risk factors for thyroid nodules in patients with type 2 diabetes ( OR = 1.83, 1.67, 1.08, 3.65, P < 0.05), and men was an independent protective factor ( OR = 0.63, P = 0.039). The ROC curve results showed that total cholesterol and total cholesterol combined with glycated hemoglobin could distinguish patients with thyroid nodules from those without thyroid nodules, with AUC = 0.64 and 0.68, respectively, and the differences were statistically significant ( P < 0.05). Conclusions:The incidence of thyroid nodules in patients with type 2 diabetes is relatively high. Age, overweight and obesity, high glycated hemoglobin, and high total cholesterol are independent risk factors for thyroid nodules in patients with type 2 diabetes, and total cholesterol has the ability to distinguish patients with thyroid nodules.

To understand the background of Escherichia coli(E.coli)carried by artificially bred sika deer and the biological characteristics of the isolated strains,such as drug resistance and pathogenicity,in April 2024,we collected 184 fresh deer fecal samples from four deer farms in Luxiang Township,Shuangyang District,Changchun City,Jilin Province,for isolation and cultivation of E.coli.The isolates were tested for drug resistance and biochemical identification with a BD PhoenixTM-100 Automated Microbiology System.The virulence genes were detected with PCR,and the strains were molecularly typed with ERIC-PCR.A total of 165 E.coli strains were isolated from 184 samples of deer feces,with an isolation rate of 89.67%.Twenty strains had a drug resistance phenotype,and the drug resistance rate was 12.12%;these strains included 15 strains of multi-drug resistant bacteria and 11 strains of ESBL-producing bacteria.Virulence gene detection indicated that the sika deer isolates carried multiple diarrhea-associated virulence genes,such as EAST-1(12.12%),eae(1.21%),stx1(7.88%),stx2(7.27%),and STa(1.82%).ERIC-PCR demonstrated that the isolates showed high polymorphism.The ESBL-producing E.coli carried by sika deer are likely to spread drug resistance in the community and livestock population.Some isolates carried multiple diarrhea-associated virulence genes,thus posing a human transmission risk.Therefore,monitoring of drug resistance and virulence genes must be strengthened,and antibiotics must be used reasonably during the breeding process to avoid excessive use and misuse.

The integration of science and education is not only an important strategy for promoting social progress and technological development,but also a modern form of higher education aiming at cultivating innovative talents.Conducting scientific research training for undergraduate medical students is one of the important ways to cultivate their innovative abilities and comprehensive qualities.Our team proposed a"teaching,science,and ideology trinity"teaching model to comprehensively cultivate students' scientific research comprehensive abilities under the value orientation of ideological and political education by or-ganically integrating molecular biology experimental teaching with the scientific research training of under-graduate medical students.In this teaching activity,taking the experiment of gene polymorphism as an example,our team selected students with research potential from the whole grade and divided them into 4 project groups that were instructed by 4 teachers.The students were trained in the whole process of scien-tific research,including topic selection,project writing,experimental designing,application for research ethics,and project summary.Our team has always adhered to student-contentedness of educational con-cepts to stimulate students' intrinsic motivation throughout the teaching process.Students are the design-ers and implementers of the project,and teachers are only guides and promoters of learning.After this training,students not only became familiar with the writing and implementation of scientific research pro-jects,but also improved their literature reading,experimental designing,experimental skills,and prob-lem-solving abilities.More importantly,this teaching activity also cultivated students' awareness of re-search ethics and academic moral standards.

Objective:To evaluate the value of a combined model based on primary lesion 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer (NSCLC) . Methods:A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set ( n=142) and the validation set ( n=61) at a ratio of 7∶3. A predictive model was developed in the training set, and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score (Radscore). Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic (ROC) curves and clinical application value was assessed by calibration curves and decision curve analysis (DCA) . Results:Among 203 NSCLC patients, 116 had lymph node metastasis, with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening, and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that, sex ( OR=0.48, 95% CI: 0.24-0.95, P=0.036), tumor marker levels ( OR=3.81, 95% CI: 1.84-7.91, P<0.001), long diameter of tumor ( OR=2.56, 95% CI: 1.27-5.16, P=0.009), short diameter of tumor ( OR=3.73, 95% CI: 1.75-7.92, P=0.001), vacuolar sign ( OR=0.32, 95% CI: 0.12-0.86, P=0.024), ring-like metabolism ( OR=3.67, 95% CI: 1.33-10.13, P=0.012), maximum standardized uptake value (SUV max) ( OR=6.57, 95% CI: 3.03-14.25, P<0.001), metabolic tumor volume (MTV) ( OR=2.91, 95% CI: 1.43-5.92, P=0.003), total lesion glycolysis (TLG) ( OR=4.23, 95% CI: 2.08-8.59, P<0.001), Radscore-PET ( OR=21.93, 95% CI: 9.04-53.20, P<0.001) and Radscore-CT ( OR=13.72, 95% CI: 6.12-30.76, P<0.001) were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that, tumor marker levels ( OR=2.55, 95% CI: 1.11-5.90, P=0.028), vacuolar sign ( OR=0.26, 95% CI: 0.08-0.83, P=0.023), SUV max ( OR=5.94, 95% CI: 1.99-17.75, P=0.001), Radscore-PET ( OR=25.51, 95% CI: 5.92-110.22, P<0.001), and Radscore-CT ( OR=8.68, 95% CI: 2.73-27.61, P<0.001) were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors, models were constructed: the traditional model (tumor marker levels, vacuolar sign, SUV max), the PET model (SUV max, Radscore-PET), the CT model (vacuolar sign, Radscore-CT), and the combined model (tumor marker levels, vacuolar sign, SUV max, Radscore-PET, Radscore-CT). ROC curve analysis showed that, the area under curve (AUC) of the traditional, PET, CT, and combined models in the training set were 0.75 (95% CI: 0.67-0.82), 0.90 (95% CI: 0.84-0.95), 0.85 (95% CI: 0.78-0.90), and 0.94 (95% CI: 0.88-0.97), respectively. The predictive value of the combined model was higher than that of the traditional model ( Z=5.01, P<0.001), the PET model ( Z=1.99, P=0.047), and the CT model ( Z=3.25, P=0.001). In the validation set, the AUCs for the traditional model, PET model, CT model, and combined model were 0.65 (95% CI: 0.52-0.77), 0.86 (95% CI: 0.74-0.93), 0.85 (95% CI: 0.73-0.93), and 0.90 (95% CI: 0.80-0.96), respectively. The predictive value of the combined model was superior to that of the traditional model ( Z=3.23, P=0.001). The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%, while in the validation set, the sensitivity and specificity were 82.61% and 94.74%, respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions:Tumor marker levels, vacuolar sign, SUV max, Radscore-PET, and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.