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: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

Objective:To investigate the value of electrical cardiometry(EC),a noninvasive hemodynamic testing technique,in the di-agnosis of heart failure(HF)in adults.Methods:A prospective study was conducted among the adult patients who were hospitalized in Department of Cardiology,The First Affiliated Hospital of Chongqing Medical University,from September 2022 to May 2024,and the patients who were diagnosed with HF were enrolled as HF group,while those who were excluded from the clinical diagnosis of HF were enrolled as control group.The two groups were compared in terms of general clinical data and related parameters measured by EC,in-cluding stroke volume variation(SVV),systolic time ratio(STR),pre-ejection period(PEP),index of contractility(ICON),ICON varia-tion(VIC),left ventricular ejection time(LVET),and left ventricular ejection fraction(LVEF).A multivariate logistic regression model established by the forward method was used to investigate the factors associated with HF.The receiver operating characteristic(ROC)curve was plotted for the hemodynamic parameters measured by EC in the diagnosis of HF,and the area under the ROC curve(AUC)was calculated.Results:A total of 239 patients were enrolled,with 126 in the HF group and 113 in the control group.Compared with the control group,the HF group had significantly higher SVV,STR,PEP,VIC,and age and significantly lower ICON,LVET,and LVEF(all P<0.001).The multivariate logistic regression model estab-lished by the forward method showed that STR(odds ratio[OR]=1.199,95%CI=1.110-1.294,P<0.001),VIC(OR=1.176,95%CI=1.090-1.269,P<0.001),and age(OR=1.068,95%CI=1.010-1.128,P<0.05)were positively correlated with HF,and ICON(OR=0.968,95%CI=0.941-0.996,P<0.05)and LVEF(OR=0.854,95%CI=0.798-0.913,P<0.001)were negatively correlated with HF.The ROC curve analysis showed that STR,VIC,and ICON had an AUC of 0.887(95%CI=0.848-0.927),0.891(95%CI=0.851-0.932),and 0.718(95%CI=0.654-0.782),respectively,in the diagnosis of HF,with an optimal cut-off value of 37.5%,19.5%,and 49.3,respec-tively,a sensitivity of 91.3%,73.8%,and 50.0%,respectively,and a specificity of 61.4%,93.8%,and 86.7%,respectively.Among these indicators,STR combined with VIC had an AUC of 0.940(95%CI=0.912-0.967)in the diagnosis of HF,with a sensitivity of 83.3%and a specificity of 91.2%.Conclusion:The EC method can effectively assess the cardiac functional status of adult patients,and STR combined with VIC has some clinical value for the diagnosis of heart failure.

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

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.

Objective To explore the sleep quality and mental fatigue level in elderly patients with cerebrovascular small disease(CSVD).Methods A total of 222 patients aged over 65 years old hospitalized due to chronic diseases in Department of Neurology of the Affiliated Jiangning Hospi-tal of Nanjing Medical University from August 2022 to June 2024 were recruited prospectively and continuously.According to the CSVD score,they were divided into a CSVD group(CSVD score≥1,148 cases)and a non-CSVD group(CSVD score=0,74 cases).All the patients were evaluated by sleep quality,fatigue and neuropsychological scale when they were fully cooperated and in good condition.Subsequently,the patients in the CSVD group were further assigned into a good sleep subgroup(117 cases)and a poor sleep subgroup(31 patients).Results The CSVD group had significantly higher total score of Pittsburgh sleep quality index(PSQI),sleep quality score,sleep disturbance score,total score of self-rating fatigue,and mental fatigue score than the non-CSVD group(P＜0.01).The sleep quality score,sleep disturbance score,and mental fatigue score were risk factors for CSVD(P＜0.05).The mental fatigue score was significantly higher in the CSVD patients with poor sleep than those with good sleep(4.13±1.15 vs 2.50±1.92,P＜0.01).Conclusion Elderly CSVD patients were more likely to have decreased sleep quality and mental fatigue,and among them,those with poor sleep quality are prone to having mental fatigue than those with good sleep.