Objective: To analyze the epidemiological characteristics and influencing factors of severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang Province from 2019 to 2023, so as to provide the reference for strengthening SFTS prevention and control. Methods: Data on laboratory-confirmed SFTS cases in Zhejiang Province from 2019 to 2023 were collected through the Infectious Disease Reporting Information System of Chinese Disease Prevention and Control Information System. Meteorological data, geographic environment and socioeconomic factors during the same period were collected from the fifth-generation European Centre for Medium-Range Weather Forecasts, Geospatial Data Cloud, and Zhejiang Statistical Yearbook, respectively. Descriptive epidemiological methods were used to analyze the epidemiological characteristics of SFTS from 2019 to 2023, and a Bayesian spatio-temporal model was constructed to analyze the influencing factors of SFTS incidence. Results: A total of 578 SFTS cases were reported in Zhejiang Province from 2019 to 2023, with an annual average incidence of 0.23/105. The peak period was from May to July, accounting for 52.60%. There were 309 males and 269 females, with a male-to-female ratio of 1.15∶1. The cases were mainly aged 50-<80 years, farmers, and in rural areas, accounting for 82.53%, 77.34%, and 75.43%, respectively. Taizhou City and Shaoxing City reported more SFTS cases, while Shaoxing City and Zhoushan City had higher annual average incidences of SFTS. The Bayesian spatio-temporal interaction model showed good goodness of fit. The results showed that mean temperature (RR=1.626, 95%CI: 1.111-2.378) and mean wind speed (RR=1.814, 95%CI: 1.321-2.492) were positively correlated with SFTS risk, while altitude (RR=0.432, 95%CI: 0.230-0.829) and population density (RR=0.443, 95%CI: 0.207-0.964) were negatively correlated with SFTS risk. Conclusions SFTS in Zhejiang Province peaks from May to July. Middle-aged and elderly people and farmers are high-risk populations. Taizhou City, Shaoxing City, and Zhoushan City are high-incidence areas. Mean temperature, mean wind speed, altitude, and population density can all affect the risk of SFTS incidence.

Objective To investigate occupational exposure levels of lead, arsenic and cadmium in the lead smelting plant of Hunan Shui Kou Shan Nonferrous Metals Group Co. Ltd., analyze their effects on health of employees, and compare the applicability of different occupational health risk assessment methods, and to provide a basis for prevention and control of occupational exposure risks in enterprises. Methods According to systematic sampling method, 380 employees with lead, arsenic and cadmium exposure (exposure group) and 100 non-exposure employees (non-exposure group) were selected from 2022 to 2024 for on-site investigation of occupational health [concentration time-weighted average (C_TWA)] and physical examination. The risk was evaluated by qualitative assessment method, the U.S. Environmental Protection Agency (EPA) inhalation risk assessment method, and the Singapore Ministry of Manpower (MOM) semi-quantitative method. The consistency was analyzed by the Kappa test. Results C_TWA values of lead, arsenic, and cadmium in all positions were lower than the occupational exposure limit (OEL). The levels of blood lead, urine arsenic, and urine cadmium, as well as the prevalence of multiple systems in the exposure group were significantly higher than those in the non-exposure group (P<0.05). The proportions of chronic lead, arsenic, and cadmium poisoning were increasing year by year in the exposure group (P<0.05). The qualitative assessment method mainly indicated low and medium risk, while the EPA and MOM methods mainly indicated medium and high risk, with good agreement between the two methods (Kappa=0.676, P<0.05). Conclusion Although the enterprise meets the C_TWA standards, there are still occupational health risks of lead, arsenic, and cadmium. The EPA inhalation risk assessment method is more applicable.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Aconitum (Ranunculaceae) has a long-standing history in traditional Chinese medicine (TCM), where it has been widely used to treat conditions such as rheumatoid arthritis (RA), myocardial infarction, and heart failure. However, the potency of Aconitum alkaloids, the primary active components of Aconitum, also confers substantial toxicity. Therefore, assessing the efficacy and toxicity of these Aconitum alkaloids is crucial for ensuring clinical effectiveness and safety. Metabolomics, a quantitative method for analyzing low-molecular-weight metabolites involved in metabolic pathways, provides a comprehensive view of the metabolic state across multiple systems in vivo. This approach has become a vital investigative tool for facilitating the evaluation of their efficacy and toxicity, identifying potential sensitive biomarkers, and offering a promising avenue for elucidating the pharmacological and toxicological mechanisms underlying TCM. This review focuses on the applications of metabolomics in pharmacological and toxicological studies of Aconitum alkaloids in recent years and highlights the significant role of metabolomics in exploring compatibility detoxification and the mechanisms of TCM processing, aiming to identify more viable methods for characterizing toxic medicinal plants.
Aconitum/metabolism* ; Metabolomics/methods* ; Alkaloids/metabolism* ; Humans ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional

Rice is the world's largest food crop, and its yield and quality are directly related to food security and human health. Grain size, as one of the important factors determining the rice yield, has been widely concerned by breeders and researchers for a long time. To decipher the regulatory mechanism of rice grain size, we obtained a multi-tiller, dwarf, and small-grain mutant htd1 by ethyl methanesulfonate (EMS) mutation from the Japonica rice cultivar 'Zhonghua 11' ('ZH11'). Genetic analysis indicated that the phenotype of htd1 was controlled by a single recessive gene. Using the mutation site map (Mutmap) method, we identified the candidate gene OsHTD1, which encoded a carotenoid cleavage dioxygenase involved in the biosynthesis of strigolactone (SL). The SL content in htd1 was significantly lower than that in 'ZH11'. Cytological analysis showed that the grain size of the mutant decreased due to the reductions in the length and width of glume cells. The function of htd1 was further verified by the CRISPR/cas9 gene editing technology. The plants with the gene knockout exhibited similar grain size to the mutant. In addition, gene expression analysis showed that the expression levels of multiple grain size-related genes in the mutant changed significantly, suggesting that HTD1 may interact with other genes regulating grain size. This study provides a new theoretical basis for research on the regulatory mechanism of rice grain size and potential genetic resources for breeding the rice cultivars with high yields.
Oryza/growth & development* ; Mutation ; Edible Grain/growth & development* ; Alleles ; Plant Proteins/genetics* ; Dioxygenases/genetics* ; Lactones/metabolism* ; Gene Expression Regulation, Plant ; Genes, Plant ; Gene Editing ; CRISPR-Cas Systems ; Phenotype

Objective To investigate whether the PI3K/AKT/Cdkn1a/GPX4 signaling axis participates in the pathogenesis of radiation-induced heart disease (RIHD) through the ferroptosis pathway. Methods An RIHD mouse model was established by irradiating C57BL/6J mice with 20 Gy X-rays. Transcriptomic sequencing, the FerrDb ferroptosis-related gene set, and weighted gene co-expression network analysis were used to identify hub genes associated with ferroptosis in RIHD. KEGG enrichment analysis was employed to determine key signaling pathways. An AC16 cardiomyocyte model of RIHD was constructed, and the optimal modeling conditions were determined using CCK-8 assays and flow cytometry. Reverse transcription-quantitative PCR and Western blotting were applied to validate the expression changes of key genes and pathways in cardiomyocytes. Results Compared with the control group, myocardial tissues from irradiated mice exhibited typical RIHD pathological alterations, including structural disorganization and degeneration. Bioinformatics analysis identified Cdkn1a and Ddit4 as potential hub genes, with the PI3K/AKT pathway as the key signaling pathway. The optimal conditions for establishing the RIHD cell model were determined to be 10 Gy irradiation and 48 hours of incubation. Cellular experiments confirmed that, compared with the control group (0 Gy), irradiated cardiomyocytes (10 Gy) showed significantly elevated CDKN1A expression (P < 0.01), inhibited phosphorylation of the PI3K/AKT signaling pathway (P < 0.05), downregulated GPX4 expression (P < 0.05), and induction of ferroptosis. Conclusion This study preliminarily clarifies the potential role of the PI3K/AKT/Cdkn1a/GPX4 signaling axis in regulating ferroptosis in RIHD cardiomyocytes, providing new therapeutic targets and strategies for the prevention and treatment of RIHD.

Objective:To investigate the efficacy of azacitidine combined with CAG regimen in the treatment of acute myeloid leukemia (AML) patients who are not suitable for intensive chemotherapy.Methods:A retrospective case-series study was conducted. A total of 67 AML patients with newly diagnosed elderly, treatment-related secondary and myelodysplastic syndromes or myeloproliterative neoplasms primary transformation who were not suitable for intensive chemotherapy were selected from Heze Municipal Hospital from January 2020 to December 2023. Azacitidine combined with CAG regimen was given for treatment, and the efficacy and adverse reactions of the patients were observed.Results:Among the 67 patients, there were 32 females and 35 males with the median age [ M ( Q1, Q3)] of 68 (65, 72) years old. There were 40 cases in the high-risk group, 13 cases in the medium-risk group, and 14 cases in the low-risk group. After 1 course of treatment with azacitidine combined with CAG regimen, the overall response rate (ORR) was 38.8% (26/67), with a complete remission (CR) rate of 20.9% (14/67), a complete remission rate with incomplete recovery of blood cell count (CRi) of 11.9% (8/67), and a partial remission (PR) rate of 6.0% (4/67). After 4 courses of treatment, the ORR was 59.7% (40/67), with a CR rate of 56.7% (38/67) and a CRi rate of 3.0% (2/67). There were no PR patients. All patients in the low-risk and medium risk groups achieved at least CRi, while the ORR in the high-risk group was 40.0% (16/40). There was a statistically significant difference in efficacy between different risk groups ( P < 0.001). The patient had mild adverse reactions, mainly pain and grade 1-2 hematological adverse reactions. Conclusions:AML patients who are intolerant to intensive chometherapy are effectively treated with azacitidine combined with CAG regimen, and the adverse reactions are mild.

The NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, a high-molecular-weight protein complex in the cytoplasm, is composed of three core components: the sensor protein NLRP3, the adaptor protein apoptosis-associated speck-like protein containing a caspase-recruitment domain (ASC) and the effector protein caspase-1. It plays a critical role in regulating host immune and inflammatory responses. Studies have shown that the NLRP3 inflammasome has increasingly become a focal point in tumor molecular biology field. A growing body of evidence indicates that the increased expression and activation of the NLRP3 inflammasome is closely associated with the pathogenesis of head and neck squamous cell carcinoma (HNSCC) and the tumor microenvironment (TME). It may promote tumor proliferation, invasion, migration, and other biological behaviors through various regulatory mechanisms while influencing tumor immune evasion and therapy resistance, which holds promise as a prognostic biomarker for patients. This review explores the current effect and mechanism of the NLRP3 inflammasome and its signaling pathways in head and neck cancer, providing insights into clinical targeted drug development and molecular immunotherapy.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Inflammasomes/metabolism* ; Head and Neck Neoplasms/pathology* ; Squamous Cell Carcinoma of Head and Neck/metabolism* ; Tumor Microenvironment ; Signal Transduction ; Animals

BACKGROUND: Lung cancer is the leading malignancy in China in terms of both incidence and mortality. With increased health awareness and the widespread use of low-dose computed tomography (CT), early diagnosis rates have been steadily improving. Surgical intervention remains the primary treatment option for early-stage lung cancer, and video-assisted thoracoscopic surgery (VATS) has become a common approach due to its minimal invasiveness and rapid recovery. However, post-discharge recovery remains incomplete, underscoring the importance of postoperative care. Traditional follow-up methods, lack standardization, consume significant medical resources, and increase the burden of the patients. Artificial intelligence (AI)-driven disease management platforms offer a novel solution to optimize postoperative follow-up. This study followed 463 lung cancer surgery patients using an AI-based platform, aiming to identify common postoperative issues, propose solutions, improve quality of life, reduce recurrence-related costs, and promote AI integration in healthcare. METHODS: Using the AI disease management platform, this study integrated educational videos, collaboration between healthcare teams and AI assistants, daily health logs, health assessment forms, and personalized interventions to monitor postoperative recovery. The postoperative rehabilitation status of the patients was assessed by the Leicester Cough Questionnaire (LCQ-MC). Two independent t-test and one-way ANOVA were used to analyze the causes of postoperative cough in lung cancer. RESULTS: Most issues occurred within 7 d post-discharge, significantly declined on 14 d post-discharge. Factors such as gender, smoking history, and surgical approaches were found to influence cough recovery. The incidence of cough on 7 d post-discharge in females was higher than that in males (P<0.01), while the incidence of cough on 14 d post-discharge in elderly patients was lower than that in young patients (P=0.03). The AI-based platform effectively addressed cough, pain, and sleep disturbances through phased interventions. CONCLUSIONS The AI-based platform significantly enhanced postoperative management efficiency and the self-care capabilities of the patients, particularly in phased cough management. Future integration with wearable devices could enable more precise and personalized postoperative care, further advancing the application of AI technology across multidisciplinary healthcare domains.
Humans ; Lung Neoplasms/rehabilitation* ; Male ; Female ; Middle Aged ; Aged ; Patient Discharge ; Artificial Intelligence ; Adult ; Postoperative Care ; Postoperative Period ; Disease Management ; Quality of Life

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve