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 analyze the correlation of serum salt-induced kinase 2 (SIK2) and receptor activator of nuclear factor κB ligand (RANKL) expression levels with osteoporosis in perimenopausal women.Methods:A total of 500 perimenopausal women admitted to the Second Hospital of Tianjin Medical University from Jan. 2023 to Jun. 2023 were studied and separated into osteoporosis group and non-osteoporosis group according to with or without osteoporosis. The general data, serum SIK2 and RANKL expression levels and bone metabolism indexes [type I collagen carboxy peptide (CTX), procollagen I N-Terminal propeptide (PINP), and osteocalcin of N-MID-Osteocalcin (N-MID-OT) ] were compared between the two groups. The correlation of the expression levels of serum SIK2 and RANKL with bone metabolism indexes was analyzed by Pearson correlation; The risk factors of osteoporosis in perimenopausal women were analyzed by Logistic multi-factor model. Results:Of the 500 patients, 88 had osteoporosis (17.6%) ; The serum levels of SIK2 and RANKL, CTX, PINP and N-MID-OT in osteoporosis group were significantly higher than those in non-osteoporosis group ( t=7.53, 13.11, 19.69, 28.79, 26.14, P<0.05). Pearson correlation analysis showed that serum SIK2 and RANKL levels were significantly positively correlated with all indexes of bone metabolism ( r=0.541, 0.480, 0.447; r=0.369, 0.516, 0.482, P<0.05). Logistic regression analysis showed that the number of births, diabetes, SIK2, RANKL and bone metabolism were the factors affecting the incidence of osteoporosis in perimenopausal women ( OR=2.123, 2.243, 3.083, 4.773, 3.789, 2.927, 2.633, P<0.05) . Conclusions:SIK2 and RANKL may be involved in the pathogenesis of osteoporosis by promoting bone turnover imbalance. Together with fertility times, diabetes mellitus and bone metabolism indexes, SIK2 and RANKL are all factors in the development of osteoporosis in perimenopausal women.

Purpose To investigate the clinicopathological characteristics of the gastric oxyntic gland adenoma(GOGA).Methods We collected 18 samples of GOGA,histopathological features and immunohistochemical staining were assessed.Main features of pathological diagnosis,treatment methods and follow-up were retrospectively analyzed.Results There were 18 patients,including 9 females and 9 males,aged from 36 to 86 years old.The endoscopic im-age showed a flat lesion with whitish in color or a polypoid protrusions.The size ranged from 0.3 cm to 0.8 cm.Hema-toxylin and eosin staining showed irregular glandular structures in the mucosal lamina propria,with branched and anas-tomosed patterns.The tumour demonstrating composed of chief cells hyperplasia with mild nuclear atypia.All lesions were confined to the mucous lamina propria.There was no atrophic within the peripheral gastic mucosa.Immunohisto-chemical examination showed positive for Pepsinogen-Ⅰ and MUC6.Gene mutation were analyzed in 2 cases using next generation sequence technology,and no KRAS and GNAS mutation had been detected.Endoscopic surgical treatment was performed in 11 cases,and biopsy forceps removal was carried out in 7 cases.No recurrence or metastasis was ob-served during the follow-up period of 1 to 58 months.Conclusion GOGA is a rare lesion,and appears to behave bio-logically benign.A full understanding of its histological morphology and biological behavior can improve the diagnostic ability of clinincans,and facilitate further research in the future.

Organophosphorus pesticide(OP)is one of the most widely used pesticides in the world with the largest dosage.Acute organophosphorus pesticide poisoning(AOPP)is a common clinical disease,and AOPP accounts for 20%-50%of poisoning cases in China every year,with case fatality rate of 3%-40%.Bromophos(BDF)is a long-acting anticoagulant rodenticide,which inhibits vitamin K epoxide reductase and interferes with the synthesis of coagulation factorsⅡ,Ⅶ,Ⅸ and Ⅹ,leading to coagulation dysfunction.This article discusses the multidisciplinary diagnosis and treatment(MDT)process of a patient with combined poisoning of dichlorvos and bromadiolone.The article explores blood purification,management of coagulation abnormalities,secondary infection,atropinization and altered consciousnes in patients with organophosphorus poisoning and anticoagulant rodenticide compound poisoning,with the aim of providing clinicians with references for early diagnosis and treatment.

Objective:To analyze the assessment tools for quality of life of children with autism spectrum disorder (ASD) at home and abroad, so as to provide a basis for healthcare professionals to select and revise the assessment tools suitable for the quality of life of children with ASD in China.Methods:A scoping review method was used to systematically search the literature on quality of life assessment tools for children with ASD included in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc and VIP. The search period was from the establishment of the database to March 12, 2025. Literature that met the inclusion criteria was screened, basic information about the assessment tools was extracted, and the search results were reported in a normalized manner.Results:A total of 3 905 articles were retrieved and 29 articles were finally included. Eleven quality of life assessment scales for children with ASD were included, including two scales developed to characterize children with ASD.Conclusions:There is a wide variety of quality of life assessment tools for children with ASD, with good overall reliability and validity, but no standardized indigenous scales are developed in China. Existing quality of life assessment tools for children with ASD should be sinicized and improved, and localized assessment tools should be developed.

Objective To investigate the prevalence of specific IgE antibodies against Alternaria alternata and Aspergillus fumigatus in serum samples from clinical allergy patients across selected provinces in China.Methods Data on specific IgE antibodies for Alternaria A.and Aspergillus F.were collected from 20 hospital laboratories in 17 cities spanning 11 provinces.The study analyzed the levels of specific IgE and their variations across different provinces and seasons.Results A total of 27 471 cases of Alternaria A.and 32 843 cases of Aspergillus F.specific IgE data were included.The national average positive rate of Alternaria A.IgE was 10.40%,with the highest rate of 22.68%in Jiangsu and the lowest rate of 2.06%in Guangxi.For Aspergillus F.specific IgE,the average positive rate was 4.24%,with Hubei province having the highest rate(7.25%)and Hunan province the lowest(1.23%).The difference in IgE levels for both Alternaria A.and Aspergillus F.among provinces were statistically significant(H=9 955,16 993,all P<0.0001).Among patients,5.85%had Alternaria A.specific IgE levels at grade 3 or above,while only 0.57%had Aspergillus F.specific IgE levels at this level.When examining seasonal variations using data from Liaoning,Hunan and Anhui provinces,significant seasonal changes were observed for both Alternaria A.and Aspergillus F.IgE antibodies(HAlternaria A=347.6,338.0,401.3,HAspergillus F=196.6,133.7,231.7,all P<0.0001).Conclusion The sensitization to Alternaria A.and Aspergillus F.exhibits distinct geographical characteristics and vary significantly with seasons.Given the relatively high IgE levels associated with Alternaria A.,it should be given adequate clinical attention.

Objective:To analyze the correlation of serum salt-induced kinase 2 (SIK2) and receptor activator of nuclear factor κB ligand (RANKL) expression levels with osteoporosis in perimenopausal women.Methods:A total of 500 perimenopausal women admitted to the Second Hospital of Tianjin Medical University from Jan. 2023 to Jun. 2023 were studied and separated into osteoporosis group and non-osteoporosis group according to with or without osteoporosis. The general data, serum SIK2 and RANKL expression levels and bone metabolism indexes [type I collagen carboxy peptide (CTX), procollagen I N-Terminal propeptide (PINP), and osteocalcin of N-MID-Osteocalcin (N-MID-OT) ] were compared between the two groups. The correlation of the expression levels of serum SIK2 and RANKL with bone metabolism indexes was analyzed by Pearson correlation; The risk factors of osteoporosis in perimenopausal women were analyzed by Logistic multi-factor model. Results:Of the 500 patients, 88 had osteoporosis (17.6%) ; The serum levels of SIK2 and RANKL, CTX, PINP and N-MID-OT in osteoporosis group were significantly higher than those in non-osteoporosis group ( t=7.53, 13.11, 19.69, 28.79, 26.14, P<0.05). Pearson correlation analysis showed that serum SIK2 and RANKL levels were significantly positively correlated with all indexes of bone metabolism ( r=0.541, 0.480, 0.447; r=0.369, 0.516, 0.482, P<0.05). Logistic regression analysis showed that the number of births, diabetes, SIK2, RANKL and bone metabolism were the factors affecting the incidence of osteoporosis in perimenopausal women ( OR=2.123, 2.243, 3.083, 4.773, 3.789, 2.927, 2.633, P<0.05) . Conclusions:SIK2 and RANKL may be involved in the pathogenesis of osteoporosis by promoting bone turnover imbalance. Together with fertility times, diabetes mellitus and bone metabolism indexes, SIK2 and RANKL are all factors in the development of osteoporosis in perimenopausal women.

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 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.