Objective To analyze the case data of farmers' foodborne disease surveillance reports in Guizhou Province from 2022 to 2024, and to provide reference for the precise prevention and control of foodborne diseases among farmers in Guizhou Province. Methods Case data of foodborne disease surveillance reports of farmers were systematically collected from 2022 to 2024 in Guizhou Province. Descriptive epidemiological methods were used to analyze the temporal, geographical, and demographic distribution of foodborne diseases among farmers, along with their primary clinical symptoms and pathogen detection results. Results From 2022 to 2024, a total of 22,882 cases of foodborne diseases were reported among farmers in Guizhou Province. The majority of clinical symptoms (97.81%) were related to the digestive system, with summer being the peak season. While females outnumbered males, the gender difference was statistically insignificant (P >0.05). The 36-55 age group accounted for the highest proportion (38.83%), with Zunyi City (34.89%) and Qiandongnan Prefecture (23.21%) reporting the most cases. Fungal products were the most frequently reported suspected food items (26.96%), and home-made preparation was the primary processing method (58.63%). A total of 1 210 fecal samples were collected through active monitoring with an overall detection rate of 13.22%. Norovirus showed the highest detection rate (9.92%, 120/1 210). Statistically significant differences were observed among different seasons, age groups, regions, types and processing methods of suspected food exposure, and pathogen detection rates (P <0.001). Conclusion Foodborne disease prevention and control among farmers in Guizhou Province should focus on the risks of wild mushroom poisoning in summer and homemade foods, and continuously improve farmers' awareness of the dangers of foodborne diseases and food safety.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To investigate the effect of triptolide on cerebral ischemia- reperfusion injury （CIRI） and explore its molecular mechanism. Methods One hundred and forty-four Wistar rats were randomly divided into sham operation group, model group, low, medium, high dose of triptolide group and butylphthalide group, with 24 rats in each group. The CIRI rat model was established by blocking the middle cerebral artery for 2 hours. 3 days before modeling, the rats in each group were ip administration once a day. 24 hours after reperfusion, the neurological deficit score was detected, the rate of cerebral infarction was measured by TTC staining, the blood brain barrier （BBB） permeability was detected by EB penetration test. The pathological changes neurons in the ischemic penumbra cortex were observed by HE and TUNEL staining. The content of inflammatory factors in ischemic cerebral cortex were detected by Elisa method. The expression of TLR4/NF-κB pathway related proteins were detected by Western blot. Results Compared with the model group, the neurological deficit score, cerebral infarction rate and EB content in the triptolide middle, high dose groups and the butylphthalide group were significantly decreased （P<0.05）. The pathological changes of cortical neurons in the ischemic penumbra were significantly improved, and the apoptosis rate of neurons was significantly decreased （P<0.05）. The content of TNF-α, IL-1β and the expression of TLR4, p-NF-κB, cleaved caspase-3, Bax were significantly decreased, the expression of Bcl-2 was significantly increased, the ratio of p-NF-κB/NF-κB and Bax/Bcl-2 were significantly decreased （P<0.05）. The regulatory effect of the high dose triptolide group on various detection indexes were better than that of the butylphthalide group （P<0.05）. Conclusion Triptolide could protect the permeability of BBB, improve the neurological deficit and neuropathy in CIRI rats, and reduce the rate of cerebral infarction, its mechanism may be related to the inhibition of TLR4/NF-κB pathway and which mediated inflammatory response and neuronal apoptosis.

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

ObjectiveTo find out whether there is any difference in the monitoring results of mosq-ovitraps placed in different orientations in multi-storey residential areas, so as to provide a scientific basis for routine and emergency monitoring of Aedes albopictus with mosq-ovitraps in residential areas. MethodsFrom July 6th to October 26th 2023, one mosquito ovitrap was set up in each of the 4 orientations of east, south, west and north around the buildings in a multi-storey residential area in Jinhui Town, Fengxian District, Shanghai. Data was collected and recorded 72 hours after placement. The chi-square test was used to compare the mosquito ovitrap indices (MOIs) of two independent samples, and the Kruskal⁃Wallis H test was used to compare the MOIs of multiple independent samples. ResultsAfter 16 weeks of surveillance, 997 mosquito ovitraps were recovered, of which 211 were positive, with the mosquito ovitrap index (MOI) of 21.16% and the Aedes albopictus density index of 1.03 mosquitoes·ovitrap^-1. The MOIs were higher in September (24.22%) and October (23.96%), and the MOIs in the west, south and north within the two months were all above 20.00%. From July to October, the MOIs in the east, west, south and north were 20.70%, 22.20%, 25.50% and 16.20%, respectively, and the difference in MOIs among the 4 orientations was not statistically significant (χ²=6.647, P=0.084). Stratified analysis by month showed that in August, the south side of the multi-storey residential areas had the highest MOI (31.30%), the north side had the lowest MOI (1.30%), and there was a statistically significant difference in MOI in the east, west, south and north (χ²=25.986, P<0.001). In October, the MOI in the west was the highest (33.30%) and the MOI in the east was the lowest (6.30%), the difference in MOIs of the 4 orientations was statistically significant (χ²=12.007, P=0.007). The MOIs in the south side of the building in the outskirts of the residential area from the 1st week in July to the 4th week in October was lower (19.20%) than that in the south side of the inner building (31.70%), and the difference in MOI was statistically significant (χ²=5.118, P=0.024). ConclusionThe study of MOI in different orientations in a multi-storey residential area is a preliminary exploration based on field work, and the results show that there is a difference in MOIs in different orientations during the peak breeding period of mosquitoes. Further indicators such as temperature, humidity and wind speed in different orientations can be collected to explore the influencing factors of MOIs.

Objective To develop and validate a deep learning-based segmentation model for low-dose cervical spine X-ray,aiming to address the insufficient segmentation accuracy in low-dose protocols while balancing radiation protection and diagnostic accuracy.Methods A total of 1 363 patients cervical spine X-ray images data were collected.A dose-attenuation mathematical simulation sys-tem was constructed to generate 14 122 dynamic low-dose cervical spine images incorporating quantum noise,contrast degradation,and blur artifacts.A neural network model was developed for automated segmentation of low-dose cervical spine X-ray using this dataset.Results Within the dose range of 50%to 7.5%,the average reults of automatic segmentation by the neural network model and manual segmentation for each group were as follows:50%dose group,intersection over union(IoU)=0.98 vs 0.93(P=0.707)and Dice coefficient(Dice)=0.99 vs 0.96(P=0.749);10%dose group,IoU=0.97 vs 0.87(P=0.201)and Dice=0.99 vs 0.93(P=0.219);7.5%dose group,IoU=0.97 vs 0.67(P<0.01)and Dice=0.98 vs 0.80(P<0.01).Conclusion The developed deep learning model achieved robust cervical spine segmentation(IoU>0.96,Dice>0.98)below diagnostic dose thresholds[peak signal-to-noise ratio(PSNR)<38 dB,structural similarity index(SSIM)<0.65].Under ultra-low-dose conditions(PSNR=27.710 dB,SSIM=0.274),it demonstrated a 44.78%IoU improvement and 22.5%Dice improvement over manual segmentation.This model enables minimal radia-tion exposure while preserving diagnostic performance,confirming its theoretical feasibility for low-dose X-ray image analysis and clinical research potential.

Objective:To develop and validate a clinical prediction model for assessing the risk of postoperative pulmonary complications (PPCs) in elderly patients undergoing surgery with general anesthesia.Methods:This prospective observational study enrolled patients aged ≥65 years who underwent general anesthesia with mechanical ventilation duration >3 hours across six tertiary hospitals between December 2022 and August 2023. Based on follow-up outcomes (until discharge or postoperative day 7), patients were categorized into a non-PPCs group and a PPCs group. Detailed records included baseline patient characteristics, preoperative comorbidities, surgical information (type, duration), and bedside lung ultrasound scores (LUS) assessed within 24 hours postoperatively using a standardized 12-zone protocol. Predictor selection was performed using LASSO regression. Significant predictors identified were incorporated into a multivariate logistic regression analysis to build the prediction model, visualized as a nomogram. Internal validation was conducted via bootstrap resampling (1 000 repetitions). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves for calibration accuracy, and decision curve analysis (DCA) for clinical utility.Results:A total of 130 eligible elderly surgical patients were included. PPCs occurred in 17 patients (incidence rate: 13.1%). Multivariate analysis identified LUS ( OR=1.248, 95% CI: 1.099-1.417, P=0.001) and elective surgery type ( OR=0.206, 95% CI: 0.043-0.988, P=0.048) as independent predictors of PPCs. The nomogram model demonstrated an AUC of 0.867 (95% CI: 0.775-0.959) upon initial testing. Internal validation confirmed good discrimination (AUC=0.863, 95% CI: 0.778-0.972). Calibration curves indicated excellent agreement between predicted probabilities and observed outcomes. Decision curve analysis demonstrated significant clinical net benefit across a wide range of threshold probabilities (0.03-0.89). Conclusions:The clinical prediction model, developed using early postoperative LUS scores and surgical type, effectively predicts the risk of postoperative pulmonary complications in elderly patients following surgery under general anesthesia. The model exhibits strong discrimination, calibration, and clinical utility, providing clinicians with a reliable tool for individualized risk assessment to support clinical decision-making and potentially reduce PPC incidence.

Objective:To investigate the clinical characteristics and independent risk factors of severe disease in patients with anti-nuclear matrix protein (NXP) 2 antibody-positive juvenile dermatomyositis (JDM).Methods:A retrospective cohort study was conducted, including 219 anti-NXP2 antibody-positive JDM patients admitted to 23 children′s hospitals across China from July 2011 to July 2023. Patients were classified into severe and non-severe groups based on classification criteria for severe dermatomyositis. Demographic characteristics, clinical manifestations, and laboratory parameters were compared between the 2 groups using independent sample t-test, Mann-Whitney U test, or χ2 test. Univariate and multivariate Logistic regression analyses were performed to identify risk factors for severe disease. The receiver operating characteristic curve was employed to calculate optimal cut-off values. Results:Among the 219 patients, 108 were male and 111 were female, with an age at onset of 6.3 (3.5, 9.4) years. The severe group comprised 69 patients, and the non-severe group 150 patients. The severe group had significantly higher rates of fever, heliotrope rash, subcutaneous edema, periorbital edema, anti-Ro52 antibody positivity, as well as elevated levels of ferritin-to-albumin ratio (FAR), creatine kinase (CK), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) (all P<0.05). Multivariate analysis identified anti-Ro52 antibody positivity ( OR=13.26, 95% CI 1.37-128.29) and elevated FAR ( OR=1.90, 95% CI 1.09-2.31) as independent risk factors for severe anti-NXP2 antibody-positive JDM (both P<0.05). Receiver operating characteristic curve analysis revealed that a FAR cutoff value of 6.82 predicted severe disease with an area under the curve of 0.87 (95% CI 0.81-0.94, P<0.001), sensitivity of 0.85, and specificity of 0.70. All patients received glucocorticoid therapy, and the severe group received higher proportions of steroid pulse therapy, cyclophosphamide, mycophenolate mofetil, intravenous immunoglobulin, biologics, and adjuvant treatments compared to the non-severe group (all P<0.05). In terms of outcomes, 2 patients (2.9%) in the severe group died (due to neurological involvement and intestinal perforation, respectively), while the remaining patients achieved complete clinical response or remission. All patients in the non-severe group achieved remission. Conclusions:The primary clinical features of anti-NXP2 antibody-positive JDM included fever, heliotrope rash, subcutaneous edema, periorbital edema, anti-Ro52 antibody positivity, and elevated levels of CK, AST, LDH, and FAR. Furthermore, anti-Ro52 antibody positivity and a FAR>6.82 were identified as independent risk factors.

Objective:To investigate the infection rate of cytomegalovirus (CMV) and Epstein-Barr virus (EBV) in patients with severe aplastic anemia (SAA) after intensive immunosuppressive therapy in combination with a thrombopoietin receptor agonist (lST+TPO-RA) as well as assess the clinical impact of treatment.Methods:A retrospective, case series study was undertaken involving patients with SAA who were admitted to Soochow Hopes Hematonosis Hospital, The First Affiliated Hospital of Soochow University, and Zhengzhou Third People′s Hospital from June 2022 to February 2025. Thirty patients with complete CMV and EBV monitoring data after IST+TPO-RA treatment were enrolled. The first activation time of CMV and EBV, the maximum viral load, the first negative conversion time, and blood routine tests within 3 days before CMV and EBV positivity, during the positive period, and within 3 days after turning negative were recorded. The patients were followed up for 9 months after the completion of IST. One-way analysis of variance was used to compare the changes of blood routine before and after virus positivity and after turning negative. The χ2 test was used to compare the viral infection rate and the therapeutic effect of IST between the two groups. Results:The 30 SAA patients comprised 15 males and 15 females with an average age of (40.0±16.9) years. Of the 30 patients, 18 (60.0%) were infected with CMV and 6 (20.0%) with EBV. Among them, 17 cases received rabbit anti-human thymocyte immunoglobulin (r-ATG) treatment (r-ATG group), 13 cases received porcine anti-human lymphocyte immunoglobulin (p-ALG) treatment (p-ALG group). The CMV infection rate was significantly higher in the r-ATG group than in the p-ALG group (15/17 vs. 3/13, χ2=13.03, P<0.001); meanwhile, the rate of EBV infection was only slightly higher in the r-ATG group than in the p-ALG group, and the difference did not reach statistical significance (5/17 vs. 1/13, χ2=2.17, P=0.196). In patients infected with CMV, neutrophil, hemoglobin, and platelet counts were significantly decreased during the infection phase, followed by significant increases after CMV clearance ( F=14.48, 11.38, 4.73; all P<0.05). No significant differences in treatment efficacy were found between the r-ATG and p-ALG groups at 3, 6, and 9 months post-IST (all P>0.05). Conclusions:This preliminary study showed that the incidence of CMV and EBV infection in patients with SAA increased after IST, with CMV infections occurring significantly more frequently than EBV infections. The CMV infection rate was significantly higher in patients treated with r-ATG than in those receiving p-ALG. CMV infection was associated with notable alterations in hematological parameters, highlighting the need for close clinical monitoring.

Objective:To determine the correlation between serum soluble CD146 (sCD146) levels and disease activity in patients with systemic vasculitis and the potential of sCD146 as a novel biomarker.Methods:We recruited 304 patients from the systemic vasculitis cohort at Peking Union Medical College Hospital from July 2013 to December 2022. The cohort comprised 200 patients with Takayasu arteritis (TAK) and 104 with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). The patient′s demographic and clinical data, including age, sex, disease duration, disease type, laboratory results, and disease status, were extracted from the database. The serum sCD146 concentration was measured using a sandwich enzyme-linked immunosorbent assay (ELISA). Continuous variables were presented as mean±standard deviation if normally distributed, with between-group comparisons conducted using the t-test. For non-normally distributed data, median ( Q1, Q3) was used, and comparisons between groups were performed using the Mann-Whitney U test. Categorical data were expressed as percentages, and comparisons between groups were conducted using the Chi-square test or Fisher′s exact test,as appropriate. Kendall′s tau-b′s rank correlation coefficient was calculated to evaluate the correlation between sCD146 and variables associated with systemic vasculitis. A two-sided P value <0.05 was considered statistically significant. Results:Serum sCD146 levels were significantly lower in patients with active disease compared to those in remission in both cohorts [TAK: 246 (218, 287) vs. 277 (230, 322) μg/L, Z=-2.58, P=0.010; AAV: (301±90) vs. (344±81) μg/L, t=-2.56, P=0.007]. Serum sCD146 levels were positively correlated with age and disease duration (TAK: τ=0.09, 0.12, P=0.040, P=0.009; AAV: τ=0.28, 0.15, P<0.001, P=0.020). In patients with TAK, sCD146 levels were negatively correlated with IL-6, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and disease activity status ( τ=-0.17, -0.18, -0.16, -0.16; P=0.001, P<0.001, P=0.003, P=0.010). In patients with AAV, sCD146 levels were negatively correlated with platelet count (PLT),disease activity status,and the Birmingham Vasculitis Activity Score ( τ=-0.36, -0.27, -0.27; P<0.001, P=0.007, P=0.001). Conclusion:Serum sCD146 levels were significantly lower in patients with active systemic vasculitis than in remission, displaying a negative correlation with disease activity. These findings suggest that sCD146 has potential as a novel biomarker for assessing disease activity in systemic vasculitis.