Objective: To analyze the association between drinking behaviors and anxiety symptoms, with the mediating role of insomnia symptoms among college freshmen, so as to provide a reference basis for reducing the occurrence of anxiety symptoms in college freshmen. Methods: From October to December 2021, 31 856 freshmen were selected by the purposive sampling method in 22 colleges across 11 provinces (Fujian, Jiangsu, Guangdong, Henan, Anhui, Hubei, Shanxi, Jiangxi, Shaanxi, Yunnan, Chongqing) in China. The Semi quantitative Food Frequency Questionnaire was used to investigate college freshmen drinking behaviors. The Depression Anxiety Stress Scale 21 and the Insomnia Severity Index were used to assess anxiety symptoms and insomnia symptoms in college freshmen. The generalized linear model was employed to analyze the association between drinking behaviors and anxiety symptoms in college freshmen, and the structural equation modeling was used to assess the mediating effect of insomnia symptoms on the association. Results: The detection rate of anxiety symptoms among college freshmen was 28.2%, the detection rates of the mild, moderate, severe and extremely severe were 6.6%, 15.9%, 3.2% and 2.6%, respectively. While 23.6% of college freshmen reported drinking in the past month, the rates were 39.8% among boys and 15.9% among girls. After adjusting for demographic variables (ethnicity, education, major, etc.) and confounding variables (self evaluation of learning burden, number of close friends, screen time, etc.), the results of generalized linear model analysis showed that beer consumption was associated with anxiety symptoms in college freshmen( β =0.09, 95% CI =0.04-0.14), girls( β =0.14, 95% CI =0.07-0.21) and those aged 19-20 years ( β =0.12, 95% CI =0.05-0.19)(all P <0.05). Red wine consumption was associated with anxiety symptoms in male students ( β =0.13, 95% CI =0.02-0.24, P <0.05). Alcohol and beer consumption were associated with insomnia in college freshmen[ β (95% CI ) =0.22(0.08-0.36),0.31(0.23-0.39),both P <0.01]. Insomnia symptoms partially mediated the association between drinking behaviors and anxiety symptoms among college freshmen with a mediating effect value of 0.05, accounting for 50.49% of the total effect. Conclusions Insomnia symptoms partially mediates the association between drinking behaviors and anxiety symptoms in college freshmen. Measures should be taken to simultaneously intervene in the drinking behaviors and insomnia symptoms of college freshmen to prevent the occurrence of their anxiety symptoms.

Ulcerative colitis （UC）， a chronic relapsing inflammatory bowel disease， involves multifaceted pathological mechanisms such as intestinal barrier dysfunction， immune dysregulation， and oxidative stress. Current therapeutic strategies remain limited in efficacy and safety. In recent years， the adenosine monophosphate-activated protein kinase （AMPK） signaling pathway has emerged as a pivotal therapeutic target for UC due to its central role in energy metabolism， inflammatory regulation， and intestinal homeostasis. This article systematically reviewed the mechanisms by which traditional Chinese medicine （TCM） prevented and treated UC through the regulation of the AMPK signaling pathway， with a focus on elucidating AMPK's multidimensional regulatory network in inflammatory signaling crosstalk， alleviating oxidative stress， restoring intestinal immune balance， repairing the intestinal barrier， and modulating gut microbiota. Leveraging its unique advantages of multi-target engagement and low toxicity， TCM demonstrates promising potential in UC treatment and has become a focal area of research. By systematically summarizing and synthesizing the existing literature on TCM-mediated AMPK pathway modulation in UC， this review aims to provide a theoretical foundation for advancing mechanistic research and clinical interventions in UC.

Endoscopic full-thickness resection (EFTR) is a minimally invasive technique that is increasingly used for gastrointestinal stromal tumors (GISTs) originating from the muscularis propria. Despite its advantages over conventional surgery, such as complete tumor resection and faster recovery, EFTR faces challenges related to its efficacy, safety, and feasibility, particularly in gastric GISTs. By summarizing the literature published over the past decade, this review provides a comprehensive overview of the clinical outcomes of EFTR and the evolution of defect closure devices.

Objective To understand the epidemiological characteristics of a clustered outbreak of severe fever with thrombocytopenia syndrome(SFTS)as well as the ecological tick monitoring results for Zibo City, and to provide a scientific basis for formulating prevention and control strategies. Methods A case definition was cited before epidemic investigations were performed. Epidemiological investigations were performed on the index cases and their close contacts. Blood samples were collected from cases and close contacts, and quantitative real-time RT-PCR was used to detect SFTS virus(SFTSV)nucleic acid sequences. A retrospective cohort study was conducted to analyze risk factors and develop prevention and control strategies. Results This clustered outbreak involved two index cases and six close contacts with no deaths. Case A exhibited symptom onset on July 26. SFTS was confirmed on August 2. Case B exhibited symptom onset on August 1, and SFTS was confirmed on August 3. Patient B reported a recent history of tick bites. In both index cases, the incubation period for SFTS was inferred to be 7-12 days. The time interval from symptom onset to clinical diagnosis in the two cases ranged from 2-8 days, with an average period of 5 days. SFTSV nucleic acid test result were positive for both patients, whereas all six close contacts tested negative. All captured ticks tested negative for SFTSV using quantitative real-time RT-PCR. The densities of parasitic and free-living ticks in the emergency monitoring area around the cases were 12.60 and 4.65 ticks/(flag·100 m). In 2024, the average parasitic tick index and free-living tick density index were 4.21 and 2.43 ticks/(flag ·100 m)in Yiyuan County, respectively. Conclusions No evidence of human-to-human transmission was found in the assessed SFTS clusters. The infections were likely acquired through tick bites during fieldwork, and the risk of a subsequent outbreak spreading was low.

Radiotherapy is a cornerstone in the treatment of malignant tumors. It induces DNA damage through high-energy radiation, preferentially eliminating rapidly proliferating tumor cells. However, its clinical efficacy is often limited by tumor radioresistance and collateral damage to normal tissues. Consequently, elucidating the cellular response mechanisms to radiation stress and identifying key targets that can both sensitize tumor cells and protect normal tissues have become critical strategies for improving radiotherapy outcomes. Radiation stress triggers structural remodeling of the nucleolus, leading to the dissociation of certain ribosomal proteins from the ribosome and enabling them to acquire extra-ribosomal functions. Among these, RPL11 can be released and specifically binds to MDM2, thus inhibiting its E3 ubiquitin ligase activity, stabilizing p53, and mediating cell cycle arrest and apoptosis. The RPL11-MDM2-p53 pathway, acting as a signaling hub that links nucleolar dysfunction to cell fate determination, plays a pivotal role in maintaining genomic stability and regulating cellular responses to radiation. This review first introduces the basic characteristics of RPL11 and elucidates the molecular basis of radiation-induced ribosomal stress. It then outlines the core regulatory mechanisms of the cell cycle. On this basis, it focuses on the mechanisms by which radiation-induced RPL11 regulates the cell cycle and analyzes the specific effects of RPL11 on cell cycle. Furthermore, it discusses the role of the RPL11-MDM2-p53 pathway in cell cycle regulation. Finally, it explores the role of this pathway in maintaining genomic stability and determining cell fate, and highlights its potential value as a target for radiosensitization, aiming to provide new perspectives for enhancing tumor radiosensitivity and reducing damage to normal tissues.

Radiotherapy is a cornerstone in the treatment of malignant tumors. It induces DNA damage through high-energy radiation, preferentially eliminating rapidly proliferating tumor cells. However, its clinical efficacy is often limited by tumor radioresistance and collateral damage to normal tissues. Consequently, elucidating the cellular response mechanisms to radiation stress and identifying key targets that can both sensitize tumor cells and protect normal tissues have become critical strategies for improving radiotherapy outcomes. Radiation stress triggers structural remodeling of the nucleolus, leading to the dissociation of certain ribosomal proteins from the ribosome and enabling them to acquire extra-ribosomal functions. Among these, RPL11 can be released and specifically binds to MDM2, thus inhibiting its E3 ubiquitin ligase activity, stabilizing p53, and mediating cell cycle arrest and apoptosis. The RPL11-MDM2-p53 pathway, acting as a signaling hub that links nucleolar dysfunction to cell fate determination, plays a pivotal role in maintaining genomic stability and regulating cellular responses to radiation. This review first introduces the basic characteristics of RPL11 and elucidates the molecular basis of radiation-induced ribosomal stress. It then outlines the core regulatory mechanisms of the cell cycle. On this basis, it focuses on the mechanisms by which radiation-induced RPL11 regulates the cell cycle and analyzes the specific effects of RPL11 on cell cycle. Furthermore, it discusses the role of the RPL11-MDM2-p53 pathway in cell cycle regulation. Finally, it explores the role of this pathway in maintaining genomic stability and determining cell fate, and highlights its potential value as a target for radiosensitization, aiming to provide new perspectives for enhancing tumor radiosensitivity and reducing damage to normal tissues.

Radiotherapy is a cornerstone in the treatment of malignant tumors. It induces DNA damage through high-energy radiation, preferentially eliminating rapidly proliferating tumor cells. However, its clinical efficacy is often limited by tumor radioresistance and collateral damage to normal tissues. Consequently, elucidating the cellular response mechanisms to radiation stress and identifying key targets that can both sensitize tumor cells and protect normal tissues have become critical strategies for improving radiotherapy outcomes. Radiation stress triggers structural remodeling of the nucleolus, leading to the dissociation of certain ribosomal proteins from the ribosome and enabling them to acquire extra-ribosomal functions. Among these, RPL11 can be released and specifically binds to MDM2, thus inhibiting its E3 ubiquitin ligase activity, stabilizing p53, and mediating cell cycle arrest and apoptosis. The RPL11-MDM2-p53 pathway, acting as a signaling hub that links nucleolar dysfunction to cell fate determination, plays a pivotal role in maintaining genomic stability and regulating cellular responses to radiation. This review first introduces the basic characteristics of RPL11 and elucidates the molecular basis of radiation-induced ribosomal stress. It then outlines the core regulatory mechanisms of the cell cycle. On this basis, it focuses on the mechanisms by which radiation-induced RPL11 regulates the cell cycle and analyzes the specific effects of RPL11 on cell cycle. Furthermore, it discusses the role of the RPL11-MDM2-p53 pathway in cell cycle regulation. Finally, it explores the role of this pathway in maintaining genomic stability and determining cell fate, and highlights its potential value as a target for radiosensitization, aiming to provide new perspectives for enhancing tumor radiosensitivity and reducing damage to normal tissues.

Objective To explore the independent risk factors for early failure of internal mammary artery grafts after coronary artery bypass grafting (CABG), and to construct and preliminarily evaluate a risk prediction model for the decline of internal mammary artery bridges, optimizing postoperative risk stratification and management strategies for patients. Methods Patients who underwent CABG at Fuwai Hospital, Chinese Academy of Medical Sciences from January 2016 to January 2020 were retrospectively enrolled. The primary endpoint was the failure of the internal mammary artery bridge one year after surgery, and the secondary endpoint was major adverse cardiac and cerebrovascular events (MACCE) within five years after surgery, including all-cause death, myocardial infarction, stroke, and revascularization. Patients were divided into a failure group and a non-failure group based on whether there was early failure of the internal mammary artery bridge after surgery. Independent risk factors for the failure of the internal mammary artery bridge were explored through the least absolute shrinkage and selection operator regression and multivariate logistic regression, and a failure risk prediction model was constructed and cross-validated. Patients were stratified for MACCE risk according to the total score of independent risk factors, and the 5-year cumulative MACCE-free rate was drawn using the Kaplan-Meier method. Results A total of 657 patients were included, among whom there were 54 patients in the failure group, including 38 males and 16 females, with an average age of (61.85±8.03) years; there were 603 patients in the non-failure group, including 467 males and 136 females, with an average age of (60.45±8.23) years. Multivariate logistic regression analysis showed that non-left main lesion [OR=3.28, 95%CI (1.41, 7.62), P=0.006], pulsatility index (PI)>3.0 [OR=2.63, 95%CI (1.20, 5.75), P=0.016], quantitative flow ratio (QFR)>0.80 [OR=5.57, 95%CI (2.98, 10.41), P<0.001] and in-hospital complications [OR=4.02, 95%CI (1.59, 10.19), P=0.003] were independent risk factors for the failure of internal mammary artery grafts after CABG. Compared with the prediction model in previous literature [area under receiver operating characteristic curve was 0.632, 95%CI (0.588, 0.688)], the risk prediction model constructed with QFR>0.80, PI>3.0, non-left main lesion and in-hospital complications had a higher predictive ability for early failure of internal mammary artery grafts [area under curve: 0.758, 95%CI (0.707, 0.817); net reclassification index: 0.272, 95%CI (0.180, 0.370); comprehensive discriminative improvement index: 0.109, 95%CI (0.059, 0.158); P<0.05]. The risk score of independent risk factors for internal mammary artery graft failure demonstrated significant MACCE risk stratification efficiency in the 5-year patient follow-up, with the high-risk group showing a significantly higher incidence of MACCE compared to the medium and low-risk groups (P=0.001). Conclusion QFR>0.80, PI>3.0, non-left main lesions, and in-hospital complications are independent risk factors for early failure of internal mammary artery grafts after CABG. The constructed risk prediction model based on this has preliminary capabilities in predicting the risk of internal mammary artery graft failure and MACCE risk stratification, which is beneficial for the postoperative management of CABG patients and improving their mid-long term prognosis.

ObjectiveEarly blight is a common destructive disease in the growth process of Solanaceae crops, which can lead to crop failure and serious losses. Traditional crop disease detection methods are difficult to detect disease characteristics in a timely manner during the incubation period of disease, and thus take scientific and effective prevention and control measures. This study obtained hyperspectral images of early blight of peppers at different infection stages through continuous monitoring with a hyperspectral imager. The earliest identifiable time during the incubation period of early blight in peppers (the earliest identifiable time during the incubation period in this experiment was 24 h after inoculation) was determined using the spectral angle cosine-correlation coefficient and Chebyshev distance. MethodsTaking the symptoms of the latent period of early blight in peppers as the research object, 13 characteristic wavelengths were selected using a genetic algorithm. An identification model of crop disease latent period symptoms based on spectral features was established through optimized combinations of characteristic wavelengths combined with a logistic regression model. Simultaneously, a recognition model of the latent period of early blight in peppers based on image texture features was established using local binary patterns. ResultsThe experiment was tested with 120 samples. The accuracy of the identification model of crop disease latent period symptoms based on spectral features reached over 93% in both the training set and the test set. The accuracy of the identification model of crop disease latent period symptoms based on texture features reached 98.96% and 100% in the training set and test set, respectively. ConclusionBoth spectral features and texture features can be used to detect and identify crop disease latent period symptoms. Texture features more significantly revealed the characteristics of the latent period of the disease compared to spectral features, effectively improving the detection performance of the model. The research results in this article can provide theoretical references for monitoring and identifying other crop disease latent period symptoms.

ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.