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: 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*

[This corrects the article DOI: 10.11909/j.issn.1671-5411.2017.08.005.].

OBJECTIVES: As early as the Apollo 11 mission, astronauts experienced ocular, skin, and upper airway irritation after lunar dust (LD) was brought into the return cabin, drawing attention to its potential biological toxicity. However, the biological effects of LD exposure through the digestive system remain poorly understood. This study aimed to evaluate the impact of digestive exposure to lunar dust simulant (LDS) on gut microbiota and on the intestine, liver, kidney, lung, and bone in mice. METHODS: Eight-week-old female C57BL/6J mice were used. LDS was used as a substitute for lunar dust, and Shaanxi loess was used as Earth dust (ED). Mice were randomly divided into a phosphate buffered saline (PBS) group, an ED group (500 mg/kg), and a LDS group (500 mg/kg), with assessments at days 7, 14, and 28. Mice were gavaged once every 3 days, with body weight recorded before each gavage. At sacrifice, fecal samples were analyzed by 16S ribosomal RNA (rRNA) sequencing; inflammatory cytokine expression [interleukin (IL)-1β, IL-6, and tumor necrosis factor alpha (TNF-α)] in intestinal, liver, and lung tissues was measured by real-time reverse transcription PCR (real-time RT-PCR); hematoxylin and eosin (HE) staining was performed on lung, liver, and intestinal tissues; Periodic acid-Schiff (PAS) staining was used to assess the integrity of the intestinal mucus barrier, and immunohistochemical staining was performed to evaluate the expression of mucin-2 (MUC2). Serum biochemical tests assessed hepatic and renal function. Femoral bone mass was analyzed by micro-computed tomography (micro-CT); osteoblasts and osteoclasts were assessed by osteocalcin (OCN) and tartrate-resistant acid phosphatase (TRAP) staining. Bone marrow immune cell subsets were analyzed by flow cytometry. RESULTS: At day 10, weight gain was slowed in ED and LDS groups. At days 22 and 28, body weight in both ED and LDS groups was significantly lower than controls (both P<0.05). LDS exposure increased microbial species richness and diversity at day 7. Compared with the PBS and ED groups, mice in the LDS group showed increased relative abundance of Deferribacterota, Desulfobacterota, and Campylobacterota, and decreased Firmicutes, with increased Helicobacter typhlonius and reduced Lactobacillus johnsonii and Lactobacillusmurinus. HE and PAS staining of the colon showed that mucosal structural disruption and goblet cell loss were more severe in the LDS group. In addition, immunohistochemistry revealed a significant downregulation of MUC2 expression in this group (P<0.05). No obvious pathological alterations were observed in liver HE staining among the 3 groups, and none of the groups exhibited notable hepatic or renal dysfunction. HE staining of the lungs in the ED and LDS groups showed increased perivascular inflammatory cell infiltration (both P<0.05). CONCLUSIONS LDS exposure via the digestive route induces gut dysbiosis, intestinal barrier disruption, pulmonary inflammation, bone loss, and bone marrow immune imbalance. These findings indicate that LD exposure poses potential health risks during future lunar missions. Targeted restoration of beneficial gut microbiota may represent a promising strategy to mitigate LD-related health hazards.
Animals ; Dust ; Mice ; Mice, Inbred C57BL ; Dysbiosis/etiology* ; Female ; Gastrointestinal Microbiome/drug effects* ; Moon ; Liver/metabolism* ; Digestive System/microbiology* ; Lung/metabolism* ; Kidney

OBJECTIVES: Due to prolonged exposure to cosmic radiation and meteorite impacts, lunar surface dust forms nanoscale angular particles with strong electrostatic adsorption properties. These dust particles pose potential inhalation risks, yet their pulmonary toxicological mechanisms remain unclear. Given the need for dust exposure protection in future lunar base construction and resource development, this study established an acute exposure model using lunar soil simulant (LSS) and used Earth soil (ES; Loess from Shaanxi, China) as a comparison to investigate lung injury mechanisms. METHODS: C57BL/6 mice were randomly assigned to 3 groups: Phosphate buffered saline (PBS), LSS, and ES, with 5 to 7 mice per group. Mice in the LSS and ES groups received a single intratracheal instillation to induce acute inhalation exposure. Body weight was monitored for 28 days. Mice were euthanized at days 3, 7, 14, and 28 post-exposure, and peripheral blood, bronchoalveolar lavage fluid (BALF), and lung tissues were collected. Immune cell subsets in BALF were analyzed using flow cytometry. Hematoxylin-eosin (HE) staining assessed lung structure and inflammation; periodic acid-Schiff (PAS) staining evaluated airway mucus secretion; Masson staining examined collagen deposition. Real-time reverse transcription PCR (real-time RT-PCR) was used to measure the mRNA expression of inflammatory cytokines (IL-1β, IL-6, and TNF-α) and epithelial barrier genes (Occludin, Cadherin-1, and Zo-1). Lung tissues at day 7 were subjected to transcriptomic sequencing, followed by immune infiltration and pathway enrichment analyses to determine immunoregulatory mechanisms. RESULTS: Body weight in the ES group progressively declined after day 18 (all P<0.05), while the LSS group showed no significant changes compared with the control group. HE staining showed both LSS and ES induced inflammatory cell infiltration around airways and vasculature, which persisted for 28 days but gradually lessened over time. PAS staining revealed marked mucus hypersecretion in the LSS group at day 3, followed by gradual recovery; no significant mucus changes were observed in the ES group. Masson staining indicated no obvious pulmonary fibrosis in either group within 28 days. Real-time RT-PCR demonstrated significant upregulation of IL-1β and TNF-α in both LSS and ES groups, peaking on day 7, accompanied by downregulation of epithelial barrier genes (Occludin, Cadherin-1, and Zo-1)(all P<0.05). Transcriptomic analysis showed that both LSS and ES activated chemokine-related pathways and enriched leukocyte migration and neutrophil recruitment pathways. Further validation revealed upregulation of CXCL2 and MMP12 in the LSS group, whereas CXCL3 and MMP12 were predominantly elevated in the ES group. CONCLUSIONS Both LSS and ES can induce sustained lung injury and neutrophil infiltration in mice, though the underlying molecular mechanisms differ. Compared with ES, exposure to LSS additionally triggers a transient eosinophilic response, suggesting that lunar dust particles possess stronger immunostimulatory potential and higher biological toxicity.
Animals ; Mice ; Mice, Inbred C57BL ; Soil ; Lung Injury/etiology* ; Dust ; Bronchoalveolar Lavage Fluid ; Moon ; Lung/pathology* ; Inhalation Exposure/adverse effects* ; Male

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective:To examine the manifestations and causes of administrative burden among primary healthcare professionals,and to explore its impact on job burnout through the mediating role of role conflict,providing theoretical and empirical support for governance-level burden-reduction strategies.Methods:An exploratory sequential mixed-methods design was employed,focusing on primary healthcare professionals in Shandong Province.In the first phase,in-depth interviews were conducted with 175 participants;in the second phase,a questionnaire survey of 1,096 participants and follow-up interviews with 107 participants were carried out.Results:The proportions of respondents who reported"heavy"or"very heavy"burdens were 62.7%for inspection,54.8%for documentation,51.8%for reporting,and 24.4%for meetings.Structural equation modeling showed that administrative burden had a direct effect on job burnout(0.150)and an indirect effect through role conflict(0.093).Qualitative findings further indicated that administrative burden largely stemmed from public health traceability requirements and medical insurance policies,and operated through both resource-based and value-based conflicts.Conclusions:Primary healthcare professionals face considerable administrative burdens,which may heighten job burnout through role conflict.Governance reforms should optimize inspection and assessment,streamline data reporting,refine record-keeping,and promote collaborative governance to break the chain of institutional pressure leading to burnout.

Objective:To examine the manifestations and causes of administrative burden among primary healthcare professionals,and to explore its impact on job burnout through the mediating role of role conflict,providing theoretical and empirical support for governance-level burden-reduction strategies.Methods:An exploratory sequential mixed-methods design was employed,focusing on primary healthcare professionals in Shandong Province.In the first phase,in-depth interviews were conducted with 175 participants;in the second phase,a questionnaire survey of 1,096 participants and follow-up interviews with 107 participants were carried out.Results:The proportions of respondents who reported"heavy"or"very heavy"burdens were 62.7%for inspection,54.8%for documentation,51.8%for reporting,and 24.4%for meetings.Structural equation modeling showed that administrative burden had a direct effect on job burnout(0.150)and an indirect effect through role conflict(0.093).Qualitative findings further indicated that administrative burden largely stemmed from public health traceability requirements and medical insurance policies,and operated through both resource-based and value-based conflicts.Conclusions:Primary healthcare professionals face considerable administrative burdens,which may heighten job burnout through role conflict.Governance reforms should optimize inspection and assessment,streamline data reporting,refine record-keeping,and promote collaborative governance to break the chain of institutional pressure leading to burnout.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.