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 analyze the characteristics of global surgical site infection(SSI)outbreaks and provide references for targeted prevention and control measures.METHODS SSI outbreak events from Jan.1,1990 to Dec.31,2023 were searched from Wanfang Med,CNKI,VIP and PubMed.Data on department distribution dur-ing the outbreak,duration,investigation methods,main causes,transmission modes,pathogen composition and outbreak outcomes were summarized for analysis.RESULTS A total of 111 SSI outbreaks in 20 countries were identi-fied,involving 1 382 patients and 24 deaths.The source of the outbreak was identified in 78 cases,mainly involving med-ical personnel in 27 cases(34.62％),hospital equipment in 19 cases(24.36％),environmental factors in 11 cases(14.10％),workflow factors in 10 cases(12.82％),hospital water factors in 7 cases(8.97％)and disinfectant fac-tors in 4 cases(5.13％).The duration of SSI outbreaks abroad was 5.00(1.50,12.00)months,longer than that in China[1.00(1.00,2.00)month](P＜0.05).In China,the outbreaks mainly occurred in neurosurgery,cardiac surger-y,orthopedics,obstetrics and general surgery,while at abroad,they mainly occurred in cardiac surgery,orthopedics,and involved multiple surgical departments.Environmental hygiene methods were adopted in up to 90.63％of cases in China.Compared with China,analytical epidemiological methods and molecular epidemiological methods were more com-monly used abroad.Targeted measures and strengthened basic measures were implemented for different outbreak sources to terminate the SSI outbreaks.Pulsed-field gel electrophoresis(34 times)was a widely used molecular typing method in outbreak investigations.CONCLUSIONS Based on the analysis of SSI outbreak characteristics and risk factors,it is neces-sary to strengthen medical personnel training and monitoring of special pathogens.At the same time,the epidemiologi-cal investigation capabilities of infection control professionals should be strengthened.

OBJECTIVE To analyze the characteristics of global surgical site infection(SSI)outbreaks and provide references for targeted prevention and control measures.METHODS SSI outbreak events from Jan.1,1990 to Dec.31,2023 were searched from Wanfang Med,CNKI,VIP and PubMed.Data on department distribution dur-ing the outbreak,duration,investigation methods,main causes,transmission modes,pathogen composition and outbreak outcomes were summarized for analysis.RESULTS A total of 111 SSI outbreaks in 20 countries were identi-fied,involving 1 382 patients and 24 deaths.The source of the outbreak was identified in 78 cases,mainly involving med-ical personnel in 27 cases(34.62％),hospital equipment in 19 cases(24.36％),environmental factors in 11 cases(14.10％),workflow factors in 10 cases(12.82％),hospital water factors in 7 cases(8.97％)and disinfectant fac-tors in 4 cases(5.13％).The duration of SSI outbreaks abroad was 5.00(1.50,12.00)months,longer than that in China[1.00(1.00,2.00)month](P＜0.05).In China,the outbreaks mainly occurred in neurosurgery,cardiac surger-y,orthopedics,obstetrics and general surgery,while at abroad,they mainly occurred in cardiac surgery,orthopedics,and involved multiple surgical departments.Environmental hygiene methods were adopted in up to 90.63％of cases in China.Compared with China,analytical epidemiological methods and molecular epidemiological methods were more com-monly used abroad.Targeted measures and strengthened basic measures were implemented for different outbreak sources to terminate the SSI outbreaks.Pulsed-field gel electrophoresis(34 times)was a widely used molecular typing method in outbreak investigations.CONCLUSIONS Based on the analysis of SSI outbreak characteristics and risk factors,it is neces-sary to strengthen medical personnel training and monitoring of special pathogens.At the same time,the epidemiologi-cal investigation capabilities of infection control professionals should be strengthened.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

ObjectiveTo assess the direct economic burden and influencing factors of road traffic injuries in Shanghai， providing a basis for future prevention of road traffic injuries. MethodsInformation of inpatient cases caused by road traffic injuries was collected from 22 pilot hospitals in Shanghai that participated in the registration and reporting system of injury hospitalizations from 2017 to 2020. The main content of the collected information included basic demographic information， total hospitalization costs for injuries， mode of transportation， cause of injury， nature of injury， location of injury， and hospital level. Univariate analysis was used to analyze the average hospitalization costs and length of hospital stay between different years. Kruskal-Wallis test was used to analyze the median hospitalization costs and the median length of hospital stay between different years， and the median hospitalization costs of different groups. A full subset regression model in multivariate regression was used to explore the main influencing factors of hospitalization costs. ResultsA total of 4 655 patients were hospitalized with road traffic injuries， including 2 675 males （57.47%） and 1 980 females （42.53%）. The average age was （53.84±15.37） years old， with a minimum age of 4 years old and a maximum age of 97 years old. 1 907 patients （40.97%） had non-motor vehicle accidents， and 2 748 patients （59.03%） had motor vehicle accidents. The average hospitalization cost is 23 245.71 yuan， with a median of 9 793.94 yuan. The average length of stay was 13.61 days， with a median stay of 10.00 days. The main factors affecting hospitalization costs included year， nature of injury， location of injury， length of hospital stay， and hospital level. ConclusionRoad traffic injuries seriously affect the health of middle-aged and young people， especially vulnerable road user such as pedestrians and cyclists， causing serious injury and an increase of direct economic burden year by year. Therefore， road traffic injury prevention projects should be actively carried out to reduce the occurrence of injuries.

Objective: To examine the association between serum vitamin D level and thyroid function indicators among elderly patients with type 2 diabetes mellitus (T2DM), so as to provide the evidence for the prevention and treatment of thyroid function abnormality among elderly patients with T2DM. Methods: Inpatients aged 60 years and older and admitted to the department of endocrinology of Zhejiang Hospital were selected as the study subjects. Gender, age, course of disease and other basic information were collected through questionnaire surveys. The serum 25-hydroxyvitamin D[25-(OH) D], thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3), and total thyroxine (TT4) were measured. The correlation between serum vitamin D level and thyroid function indicators in elderly patients with T2DM was evaluated by a multiple linear regression model. Results: A total of 402 elderly patients with T2DM were surveyed, including 210 males (52.24%) and 192 females (47.76%), and had a median age of 70.00 (interquartile range, 12.00) years and a median course of disease of 14.00 (interquartile range, 14.00) years. There were 162 patients with insufficiency of vitamin D (40.30%) and 182 patients with deficiency (45.27%). The levels of TSH and glycated hemoglobin in the vitamin D deficiency group were (2.34±1.66) μIU/mL and (8.83±2.14) %, respectively, which were higher than those in the normal group [(1.74±1.10) μIU/mL and (8.11±1.75) %; P<0.05]. The levels of FT3 and FT3/FT4 in the vitamin D deficiency group were (2.86±0.48) μIU/mL and 2.85±0.71, respectively, which were lower than those in the vitamin D insufficiency group [(3.09±0.47) pg/mL and 3.14±0.81, P<0.05]. Multiple linear regression analysis showed a negative correlation between 25- (OH) D and TSH (β'=-0.159, P=0.001). Conclusion The vitamin D deficiency may be associated with the increase of TSH level among the elderly patients with T2DM.