Objective: To investigation and analyze the causes and characteristics of a food poisoning incident caused by Clostridium perfringens in schools, so as to provide a reference for prevention and control of food poisoning incidents. Methods: From March 11 to 12,2024,on site epidemiological and hygienic investigations were conducted by using food poisoning case questionnaires and on site hygienic investigation forms in Shenzhen. Information related to the poisoning incident was collected through field investigations and telephone interviews. A case control study was used to analyze the suspected food responsible for the incident. Risk analysis in χ 2 analysis was used to obtain OR values. Results: The Clostridium perfringens food poisoning incident involved two schools, with a cumulative total of 52 cases. School A had an attack rate of 2.21%(45/2 040), school B had an attack rate of 1.29% (7/544). The main clinical symptoms were diarrhea (100.00%) and abdominal pain (84.62%), with a minority experiencing fever ( 3.85 %). Analysis via the case control study indicated that consuming mushroom and meat sauce during lunch on March 11, 2024, was a risk factor for the poisoning incident ( OR =124.50, 95% CI =14.53-937.51, P <0.05). On site investigation revealed that the mushroom and meat sauce provided by the schools on March 11 was prepared by the catering company the previous day, stored without refrigeration, and not reheated before serving the next day. A total of 31 patient anal swabs, 16 food samples, and 14 kitchen staff anal swabs were sent for laboratory testing. Clostridium perfringens was isolated from 22 case samples. Among these, type A Clostridium perfringens (alpha toxin) was detected in 8 case samples, 4 kitchen staff anal swabs, and 2 retained samples of the mushroom and meat sauce. Conclusions The incident is a food poisoning outbreak caused by Clostridium perfringens contamination in schools. Avoiding cross contamination and ensuring proper storage are key to preventing Clostridium perfringens food poisoning.

OBJECTIVES: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the EGFR and KRAS genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility. This study aims to develop machine learning models based on radiomic features to predict EGFR and KRAS gene mutation status in NSCLC patients, thereby providing a reference for precision oncology. METHODS: Imaging and mutation data from eligible NSCLC patients were obtained from the publicly available Lung-PET-CT-Dx dataset in The Cancer Imaging Archive (TCIA). A three-dimensional-convolutional neural network (3D-CNN) was used to extract imaging features from the regions of interest (ROI). The LightGBM algorithm was employed to build classification models for predicting EGFR and KRAS gene mutation status. Model performance was evaluated using 5-fold cross-validation, with receiver operator characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity used for validation. RESULTS: The models effectively predicted EGFR and KRAS mutations in NSCLC patients, achieving an AUC of 0.95 for EGFR mutations and 0.90 for KRAS. The models also demonstrated high accuracy (EGFR 89.66%; KRAS 87.10%), sensitivity (EGFR 93.33%; KRAS 87.50%), and specificity (EGFR 85.71%; KRAS 86.67%). CONCLUSIONS A radiogenomics-machine learning predictive model can serve as a non-invasive tool for anticipating EGFR and KRAS gene mutation status in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Mutation ; Proto-Oncogene Proteins p21(ras)/genetics* ; ErbB Receptors/genetics* ; Machine Learning ; Positron Emission Tomography Computed Tomography ; Female ; Male ; Neural Networks, Computer ; Middle Aged ; Aged

The current production of Chinese herbal decoction pieces faces several issues including strong subjectivity,unstable quality,low production efficiency,and a lack of intelligent systems.In order to expedite the intelligent transformation and upgrading of Chinese medicine processing,this paper delves deeply into the problems and challenges encountered in establishing a digital and intel-ligent production model for Chinese herbal pieces.Addressing the slow progress in fundamental research on traditional Chinese medi-cine processing mechanisms,the absence of online digital quality characterization techniques,the low level of production equipment in-telligence,and the lack of evaluation standards for high-quality decoction pieces,this paper proposes a"New Quality Productive Forces"formation approach driven by technological innovation.Through interdisciplinary integration methods,the paper explores the mechanisms of Chinese medicine processing in depth,clarifies the correlation between the processing procedures and the"medicinal properties-quality"relationship,and employs bionic sensing and artificial intelligence to achieve a holistic quality characterization of decoction pieces.Additionally,the use of cloud-edge collaborative big data systems is proposed to enhance intelligent upgrades of the production lines.The paper also aims to establish a high-quality decoction piece evaluation system integrating"physical-chemical-bio-logical"multimodal data fusion.This approach aims to steer the Chinese medicine processing towards becoming more efficient,precise,and sustainable,thereby promoting high-quality sustainable development of the Chinese herbal decoction industry and providing both theoretical and practical support for the modernization of traditional Chinese medicine.

BACKGROUNDThrombolysis with recombinant tissue plasminogen activator (rt-PA) has gained international recognition, clinical outcomes following this thrombolytic therapy varied from patient to patient. Factors affecting clinical outcomes have not been well understood yet, so this retrospective case-control study aimed to investigate factors that may influence clinical outcomes of acute ischemic stroke treated with intravenous rt-PA.

METHODSOne hundred and one patients with acute ischemic stroke who received intravenous rt-PA thrombolysis within 4.5 hours from disease onset were included. Patients were divided into good or poor outcome group according to modified Rankin Scale (mRS) score, good outcome group: mRS score of 0-1; poor outcome group: mRS of 2-6. Stroke characteristics were compared between the two groups. Factors for stroke outcomes were analyzed via univariate analysis and Logistic regression.

RESULTSOf the 101 patients studied, patients in good outcome group (n = 55) were significantly younger than patients in poor outcome group (n = 46, (62.82 ± 14.25) vs. (68.81 ± 9.85) years, P = 0.029). Good outcome group had fewer patients with diabetic history (9.09% vs. 28.26%, P = 0.012), fewer patients with leukoaraiosis (7.27% vs. 28.26%, P = 0.005) and presented with lower blood glucose level ((5.72 ± 1.76) vs. (6.72 ± 1.32) mmol/L, P = 0.012), lower systolic blood pressure level ((135.45 ± 19.36) vs. (148.78 ± 19.39) mmHg, P = 0.003), lower baseline NIHSS score (12.02 ± 5.26 vs. 15.78 ± 4.98, P = 0.002) and shorter onset-to-treatment time (OTT) ((2.38 ± 1.21) vs. (2.57 ± 1.03) hours, P = 0.044) than poor outcome group. Logistic regression analysis showed that absence of diabetic history (odds ratio (OR) 0.968 (95% CI 0.941-0.996)), absence of leukoaraiosis (OR 0.835 (95% CI 0.712-0.980)), lower baseline NIHSS score (OR 0.885 (95% CI 0.793-0.989)), lower pre-thrombolysis systolic blood pressure (OR 0.962 (95% CI 0.929-0.997)), and lower blood glucose level (OR 0.699 (95% CI 0.491-0.994)) before thrombolysis were significantly associated with better outcome.

CONCLUSIONPatients with no history of diabetes, no leukoaraiosis, low blood glucose level, low systolic blood pressure level and low baseline NIHSS score before thrombolysis have a better outcome.

Aged ; Blood Pressure ; Case-Control Studies ; Female ; Fibrinolytic Agents ; therapeutic use ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Stroke ; drug therapy ; Thrombolytic Therapy ; Tissue Plasminogen Activator ; therapeutic use ; Treatment Outcome

Objective To investigate the association between interleukin-23 receptor (IL-23R) gene rs1343151 single nucleotide polymorphism and ankylosing spondylitis (AS) in Chinese Han patients. Methods The genotypes of IL-23R SNP was detected in 104 Chinese AS patients and 95 ethnically matched blood donors by TaqMan probe assays. The allele and genotype frequencies and risk factors of AS were analyzed by Chi-square test in both groups. Results The rs 1343151 genotypes in AS patients consisted of homozygote C/C (90.4%),C/T (9.6%), while The rs1343151 genotypes in the controls were. composed of C/C (91.6%), C/T (8.4%). No significant difference was found in the distribution of rs1343151 genotypes between these two groups (x2=0.086, P>0.05). The frequency of rs1343151 allele in AS patients was also not significantly increased when compared with the control group (x2 =0.082,P>0.05). Conclusion There may be no association between the IL-23R gene rs1343151 SNP and ankylosing spondylitis in Chinese Hart population.