Objective To identify immune cell-related biomarkers in lung adenocarcinoma (LUAD) using weighted gene co-expression network analysis (WGCNA). Methods Based on data from The Cancer Genome Atlas (TCGA) database, a gene co-expression network was constructed for the TCGA-LUAD dataset using the "WGCNA" R package, and genes were clustered into different modules. Concurrently, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was applied to the tumor samples in the TCGA-LUAD dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to evaluate the biological functions of genes within the most significantly correlated module. Candidate hub genes from the key module were intersected with a protein-protein interaction (PPI) network to identify the final hub genes. The prognostic performance of these hub genes and their correlation with immune cell infiltration were validated using Kaplan-Meier curves and the Tumor IMmune Estimation Resource (TIMER) algorithm. Finally, a multivariate Cox regression analysis was conducted on the identified hub genes to construct a prognostic risk model. Results In the co-expression network, the brown module was found to be highly correlated with the ImmuneScore, StromalScore, and ESTIMATE Score. Five immune-related hub genes were identified: CD53, PLEK, SPI1, IL10RA, and C3AR1. Enrichment analysis of the brown module revealed that its genes were primarily enriched in GO terms such as "regulation of innate immune response" and KEGG pathways like the "NF-kappa B signaling pathway". Furthermore, the expression levels of these five hub genes were significantly and positively correlated with the infiltration abundance of various immune cells. The immune relevance of the model was validated by the Immunophenoscore (IPS) and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Moreover, the established RiskScore demonstrated significant potential in predicting the response to immunotherapy. Conclusion These five immune-related key genes may serve as novel and effective potential therapeutic targets for LUAD immunotherapy, facilitating the development of personalized diagnosis and treatment strategies for patients with LUAD.

Objective: To investigate the associations between childhood obesity and performance of six minute walk test (6MWT), providing evidence for exercise tolerance assessment and exercise intervention strategies for children and adolescents. Methods: From March 2021 to December 2023, a cohort study was conducted among students recruited from a primary and secondary school in Chongqing, a total of 709 valid samples were included. The 6MWT was used to assess exercise tolerance, with vital signs measured before and after the test. Anthropometric indicators, including height, weight, and waist circumference, were measured using standardized procedures. Generalized additive models (GAM) and restricted cubic spline (RCS) regression were employed to analyze the nonlinear relationships between obesity related indicators and six minute walk distance (6MWD). Results: The mean 6MWD of participants was (602.59±70.73)m. GAM showed that after adjusting for confounding factors, body mass index (BMI) and weight had non linear relationships with 6MWD [effective degrees of freedom were 1.55 and 7.13 respectively], and overweight/obesity was associated with a decrease in 6MWD ( β =-18.65) (all P <0.01). Further RCS regression analysis showed that both BMI and weight showed an "inverted U shaped" non linear relationship with 6MWD in the overall population and sex stratified subgroups; the 6MWD of females was lower than that of males, and it showed a significant downward trend with the increase of BMI or weight (all P <0.05). Conclusion Body weight and BMI in children and adolescents have an important impact on 6MWD, and obesity in children and adolescents is markedly associated with decline in exercise tolerance.

Objective: To analyze changes in tuberculosis infection among junior high school students before and after tuberculosis exposure, so as to provide a reference for improving school tuberculosis prevention and control measures and policy formulation. Methods: Retrospectively collect data on a tuberculosis outbreak that occurred in a grade of a junior high school in Chongqing in 2025, including tuberculosis screening records of students in this grade upon their enrollment in 2022 (1 156 students) and after two tuberculosis outbreaks in 2023 (206 students) and 2025 (171 students). The Wilcoxon signed rank test for paired design was used to compare the induration diameters of the subjects, and the Chi square test was adopted to analyze the rate of tuberculosis infection among students. Results: In the tuberculosis outbreak in 2023, the rate of tuberculosis infection among close contacts ( 11.84 %) and the rate of tuberculosis infection among freshrman at school enrollment (12.89%) showed no statistically significant difference ( χ 2=0.25, P >0.05). The rate of tuberculosis infection of close contacts in the 2025 tuberculosis outbreak (55.56%) was higher than that in the 2023 outbreak (11.84%) ( χ 2=30.42, P <0.01). Among the 106 students included in the cohort analysis, the median induration diameter was 3.50 (1.50, 7.50) mm in 2023 and 8.75 (4.25, 11.50) mm in 2025, with a statistically significant difference ( Z=-5.76, P <0.01). There was no statistically significant difference between the infection rate in 2022 (16.98%) and that in 2023 (10.38%) ( χ 2=1.96, P =0.16). The infection rate in 2025 (43.40%) was higher than those in 2022 and 2023 ( χ 2=17.55, 29.39, both P <0.017). The seroconversion rate of students in the same class in 2025 ( 58.00 %) was higher than that of students in different classes (16.07%), with a statistically significant difference ( χ 2=20.19, P <0.01). All 72 individuals with latent tuberculosis infections identified during the pandemic in 2023 and 2025 refused to undergo prophylactic treatment. Conclusions The lack of preventive treatment may be the underlying cause of the successive outbreaks during the epidemic. Early detection of infection sources and standardized outbreak management are crucial to controlling the spread of the epidemic.

Objective To investigate the exposure-response relationships and lag effects between air pollutants (PM_2.5, PM₁₀, O₃, and NO₂) and mortality in Jiading District, Shanghai, and to provide a scientific basis for the formulation of environmental health policies. Methods Using an individual-level time-stratified case-crossover design, conditional logistic regression models in conjunction with a distributed lag nonlinear model (DLNM) were employed to analyze the exposure-response relationship and temporal lag patterns of ambient air pollution on resident mortality in Jiading District (1994–2024). Results A total of 59 048 death cases were collected, including 18,701 deaths from cardiovascular diseases and 11 731 deaths from respiratory diseases. PM_2.5 and NO₂ had a significant impact on all-cause mortality, cardiovascular disease mortality, and respiratory disease mortality, with the most significant effects observed within a lag of 0–3 days. PM₁₀ also had some impact on these three types of mortality, but its effect was generally weaker than that of PM_2.5 and NO₂. The exposure-response curves showed that the risk of death increased rapidly with increasing concentrations of PM_2.5 and PM₁₀, while the effect of NO₂ plateaued at higher levels. No significant differences were found across age or gender subgroups. Conclusion Short-term exposure to PM_2.5, PM₁₀, and NO₂ significantly increases all-cause mortality risk in Jiading District, with effects persisting up to 7 days, highlighting the need for enhanced air pollution control measures, particularly targeting fine particulate matter.

bjective To analyze the growth trends of height and weight among primary and secondary school students, and explore the developmental characteristics and gender differences at different age groups, and to provide a scientific basis for adolescent health policy formulation. Methods Based on 675 175 health examination records of 227 978 students aged 6-17 years in Shiyan City from 2015 to 2024, a logistic growth model was employed to fit the curves of height and weight changes with age. Results From 2015 to 2024, height and weight showed steady increases across all age groups, exhibiting typical sigmoidal growth patterns. The growth rates varied across age groups: the younger age group (6-9 years) showed a moderate growth (annual height increase of 0.5-1.0 cm, weight increase of 0.03-0.06 kg/year), while the older age group (10-17 years) demonstrated a significant growth (annual height increase of 1.5-2.0 cm, weight increase of 0.22-0.38 kg/year). The growth rate curves displayed a unimodal distribution. The growth inflection points of male students occurred later than that of female students (height inflection point: 9.87 years for males vs. 8.98 years for females; weight inflection point: 10.70 years for males vs. 9.99 years for females). Female students experienced a more concentrated but shorter period of growth and development. The peak height growth rate was 7.40 cm/year at age 9 for females and 7.09 cm/year at age 10 for males, while the peak weight growth rate was 5.04 kg/year at age 10 for females and 5.27 kg/year at age 11 for males. Conclusion The physical development of primary and secondary school students in Shiyan City follows a logistic growth pattern, with significant gender differences and characteristics of adolescent growth spurts. Female students exhibit an earlier and more concentrated growth process.

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.

ObjectiveTo investigate the molecular mechanisms by which Wenyang Jiedu granules （WYJD） alleviate influenza A virus （IAV）-induced pneumonia based on single-cell transcriptome sequencing. MethodsThirty female BALB/c mice were randomly divided into a blank control group （Control）， IAV group， and WYJD low-， medium-， and high-dose groups （WYJD-L， WYJD-M， WYJD-H； 2.925， 5.85， 11.7 g·kg^-1， n=6）. Except for the Control group， all other groups were intranasally inoculated with IAV subtype H1N1 （A/PR/8/34） to establish an infection model. Two hours after modeling， drug administration was initiated and continued for 5 consecutive days， with daily monitoring of body weight and general condition. On day 6， mice were sacrificed and samples were collected. Lung index was calculated， and histopathological examination of lung tissue was performed. Lung tissues from the Control， IAV， and WYJD-H groups were subjected to single-cell transcriptome sequencing （n=3）， focusing on type I alveolar epithelial cells （AT1） to analyze changes in gene expression and signaling pathways. Western blot was used to detect the expression changes of relevant proteins to validate the single-cell sequencing results. ResultsCompared with the Control group， the IAV group exhibited significantly decreased body weight （P<0.05） and significantly increased lung index （P<0.05）. Compared with the IAV group， all WYJD-treated groups exhibited significantly increased body weight （P<0.01） and significantly decreased lung index （P<0.01）. Single-cell sequencing analysis revealed that WYJD inhibited overactivation of interferon and inflammatory signaling pathways in AT1 cells after IAV infection， including interferon-γ response， interferon-α response， tumor necrosis factor-α/nuclear factor-κB （TNF-α/NF-κB）， and interleukin-6/Janus kinase/signal transducer and activator of transcription 3 （IL-6/JAK/STAT3） pathways. Compared with the Control group， the number of AT1 cells in the IAV group showed a decreasing trend. Compared with the IAV group， the WYJD-H group showed an increasing trend， although neither difference was statistically significant. Further analysis of AT1 cell subpopulation gene expression showed that， compared with the Control group， the IAV group exhibited increased expression of pro-apoptotic genes FAS cell surface death receptor （FAS） and cyclin-dependent kinase inhibitor 1A （CDKN1A）， a significant increase in tumor protein p53 （Tp53） expression （P<0.05）， and significant decreases in expression of the AT1 marker gene advanced glycosylation end-product-specific receptor （AGER） and membrane structural gene caveolin1 （CAV1） （P<0.05， P<0.01）. Compared with the IAV group， the WYJD-H group showed significantly decreased expression of FAS， CDKN1A， and Tp53 （P<0.05， P<0.01）， and significantly increased expression of AGER and CAV1 （P<0.05， P<0.01）. Regarding interferon response-related genes， compared with the Control group， the IAV group showed increased expression of interferon-stimulated gene 15 （ISG15）， interferon-induced protein with tetratricopeptide repeats 3 （IFIT3）， signal transducer and activator of transcription 2 （STAT2）， bone marrow stromal cell antigen 2 （BST2）， and C-X-C motif chemokine ligand 10 （CXCL10）， with a significant increase in 2′，5′-oligoadenylate synthetase-like protein 1 （OASL1） （P<0.05）. Compared with the IAV group， the WYJD-H group showed significantly decreased expression of all the above genes， with highly significant differences for ISG15， IFIT3， STAT2， BST2， and OASL1 （P<0.01）， and a significant difference for CXCL10 （P<0.05）. Among inflammation-related genes， compared with the Control group， the IAV group showed significantly increased expression of intercellular adhesion molecule 1 （ICAM1）， tumor necrosis factor alpha-induced protein 3 （TNFAIP3）， keratin 8 （KRT8）， tumor necrosis factor receptor superfamily member 1A （TNFRSF1A）， and TNFRSF1B （P<0.01）， and increased expression of NFKBIA， a negative regulator of NF-κB （P<0.05）. Compared with the IAV group， the WYJD-H group showed significantly decreased expression of KRT8 and TNFRSF1B （P<0.05）， while ICAM1， NFKBIA， TNFAIP3， and TNFRSF1A showed decreasing trends without statistical significance. Western blot validation showed that， compared with the Control group， protein expression levels of ISG15， FAS， p53， and phosphorylated p65 （p-p65） in lung tissue of the IAV group were significantly increased （P<0.05， P<0.01）. Compared with the IAV group， the WYJD-H group showed significantly decreased expression of these proteins （P<0.05， P<0.01）. ConclusionWYJD may alleviate viral pneumonia by targeting gene expression in AT1 cells， inhibiting overactivated interferon and inflammatory signaling pathways after IAV infection， and downregulating pro-apoptotic signaling， thereby reducing alveolar epithelial injury.

ObjectiveTo analyze the epidemiological characteristics of 21 respiratory pathogens in influenza-like illness (ILI) cases in Jing’an District, Shanghai in 2024, and to provide a scientific basis for the prevention and control of respiratory infectious diseases. MethodsData of1 907 ILI cases at four sentinel hospitals in Jing’an District were collected from January to December 2024. Nasopharyngeal swab samples were collected and tested for 21 respiratory pathogens using polymerase chain reaction (PCR) methods. Chi-square test and Cochran-Armitage trend test were used for data analyses. ResultsAmong the 1 907 ILI cases, 1 340 were tested positive (70.27%), including 1 160 (60.83%) virus-positive cases, 424 (22.23%) bacteria-positive cases , and 86 (4.51%) positive cases of other pathogens (fungi, mycoplasma, and chlamydia). The top five viruses by detection rate were: influenza virus (14.84%), SARS-CoV-2 (14.47%), rhinovirus (12.69%), adenovirus (7.08%), and parainfluenza virus (6.71%). The top two bacteria by detection rate were Streptococcus pneumoniae (14.47%) and Haemophilus influenzae (10.33%). Among other pathogens (fungi, mycoplasma, and chlamydia), Mycoplasma pneumoniae showed the highest detection rate (4.30%). In terms of age distribution, statistically significant differences were observed in the detection rates of SARS-CoV-2, Legionella, and Klebsiella pneumoniae (P<0.05), with the highest rates found in individuals aged 65 years and above. Statistically significant differences were also found in the detection rates of rhinovirus, adenovirus, enterovirus, common coronavirus, respiratory syncytial virus, bocavirus, parainfluenza virus, human metapenu-movirus, Streptococcus pneumoniae, Haemophilus influenzae, and Mycoplasma pneumoniae among different age groups (P<0.05), all showing the highest detection rates in the 0‒<15 years age group. In terms of seasonal distribution, SARS-CoV-2, adenovirus, parainfluenza virus, enterovirus, Streptococcus pneumoniae, Haemophilus influenzae, and Mycoplasma pneumoniae showed epidemic peaks in summer; rhinovirus, common coronavirus, bocavirus, and Klebsiella pneumoniae had higher detection rates in autumn. Influenza virus exhibited a peak incidence during winter, while human metapenu-movirus peaked in winter and spring. Significant differences in co-infection detection rates were observed among age groups, with the rate in children aged 0‒<15 years (34.81%) being the highest. The co-infection detection rate was higher in males than in females (P=0.019). Both the single-pathogen detection rate and the co-infection detection rate (P<0.001) varied significantly across seasons: the single-pathogen detection rate was highest in winter (62.06%), while the co-infection detection rate peaked in summer (31.20%) and was lowest in winter (14.52%). ConclusionBased on detection rates, the main pathogens in the ILI population of Jing’an District, Shanghai, 2024 were influenza virus, SARS-CoV-2, rhinovirus, adenovirus, parainfluenza virus, common coronavirus, enterovirus, Human metapenu-movirus, Streptococcus pneumoniae, Haemophilus influenzae, and Mycoplasma pneumoniae. Pathogen detection rates varied by age and season. Coinfection rates were much higher in children than in adults, higher in males than in females, and peaked in summer while being lowest in winter.

Background As one of the primary operational methods in the power grid industry, the insulated glove working method imposes significant physical demands due to the constraints of insulating equipment and specific required postures, resulting in substantial occupational health hazards among workers in this sector, which have garnered widespread social attention. Objective To investigate the prevalence and influencing factors of work-related musculoskeletal disorders (WMSDs) in the neck, shoulder, and lumbar regions among workers wearing insulated gloves, and to provide targeted measures to reduce occupational hazards. Methods Using stratified cluster sampling, 1079 frontline workers were randomly selected from power supply enterprises across 3 provinces in China. The revised Chinese version of the Musculoskeletal Disorders Questionnaire was used to investigate the 1-year prevalence of WMSDs in the neck, shoulder, and lumbar regions among workers wearing insulated gloves, and to collect factors associated with multi-site WMSDs (defined as involvement of ≥2 sites among the neck, shoulder, and lumbar). Results The 1-year prevalence of WMSDs in the neck, shoulder, and lumbar regions was 39.9%, 30.0%, and 25.3%, respectively, with a multi-site WMSDs prevalence of 32.0%. Multiple logistic regression analysis showed that, compared to workers with technical secondary school education or senior high school education and below, workers with junior college education or bachelor’s degrees and above had a higher risk of multi-site WMSDs (OR=2.12, 95%CI: 1.47, 3.06). Compared to workers with <10 years of work experience, those with 10-<20 years of work experience had a higher risk of multi-site WMSDs (OR=1.96, 95%CI: 1.45, 2.67). Working in uncomfortable postures "sometimes" (OR=1.64, 95%CI: 1.14, 2.36), "frequently" (OR=2.75, 95%CI: 1.76, 4.29), and "very frequently" (OR=3.55, 95%CI: 2.04, 6.19) were significantly associated with an increased risk of multi-site WMSDs compared to never working in such postures. Frequent repetitive movements of the low back (OR=2.05, 95%CI: 1.48, 2.84) increased the risk of multi-site WMSDs, while sufficient rest time decreased the risk (OR=0.45, 95%CI: 0.34, 0.61). Conclusion The prevalences of single-site and multi-site WMSDs in the neck, shoulder, lumbar regions are relatively high among workers wearing insulated gloves. Factors associated with multi-site WMSDs include 10-<20 years of work experience, junior college education or bachelor’s degrees and above, uncomfortable working postures, frequent repetitive low-back movements, and lack of sufficient rest time.