Objective To introduce the causes of accidents and the diagnosis and treatment of two patients with radiation-induced skin injury admitted to our hospital in 2023, and to provide a reference for the clinical treatment of subsequent radiation-induced skin injury. Methods The clinical treatment process of two patients with acute skin injury caused by external radiation exposure were summarized and analyzed. Results The exposure history of the two patients was reconstructed, the flaw detection scenario was simulated, the biological dose and hand skin exposure dose were estimated, and the infrared thermal imaging device was used for dynamic monitoring. A comprehensive analysis was conducted based on clinical manifestations and other data. The diagnosis of “Xie” was excessive exposure combined with acute radiation-induced skin injury on both hands (Grade IV for the right hand palm, index finger, and middle finger and Grade II for the left hand little finger). The diagnosis of “Hao” was acute radiation-induced skin injury on both hands (Grade I). The two patients received different clinical treatment measures: “Xie” was treated with both local and systemic therapies, while “Hao” was mainly treated with systemic therapy. Conclusion After systematic and effective treatment, the radiation-induced skin injuries healed in both patients.

Objective To investigate the association between physical activity levels and blood lipids among college freshmen, and to provide scientific evidence for the health management of college freshmen. Methods An electronic questionnaire survey on physical activity was conducted on freshmen of a university, and fasting blood biochemical indicators were detected. The International Physical Activity Questionnaire (IPAQ) short form was used to evaluate the physical activity levels of the participants. Dyslipidemia was defined as an abnormality in any one of the following serum lipid parameters: total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or non-high-density lipoprotein cholesterol. Binary logistic regression and stratified analyses were employed to explore the relationship between physical activity and blood lipids. Results A total of 3 401 participants were included, with an average age of 18.45 ± 0.92 years, and 60.5% were female. The prevalence of dyslipidemia was 17.7%, with a higher rate among males (22.1%) than females (14.8%). After adjusting for confounding factors related to blood lipids, high-intensity physical activity was negatively associated with the risk of elevated LDL-C among males (OR = 0.36, 95% CI: 0.13–0.99, P = 0.049). Conclusion Among freshmen at a medical college in Hubei Province, high-intensity physical activity is negatively associated with the risk of elevated LDL-C in males, but this association needs to be further confirmed by larger prospective cohort studies.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Background: Visual impairment is increasingly prevalent, underscoring the need for effective prevention strategies. This study investigated the association between cardiovascular health and visual impairment using three progressively developed metrics: Life’s Simple 7 (LS7), a cardiovascular health score developed by the American Heart Association based on 7 components, including physical activity, smoking status, body mass index (BMI), diet, blood glucose, blood pressure, and total cholesterol; Life’s Essential 8(LE8), which adds sleep health as an additional component; and Life’s Crucial 9 (LC9), which further incorporates mental health. We aimed to evaluate whether these expanded metrics provide additional explanatory value and to inform future public health strategies. Objective: To investigate the associations among LS7, LE8, and LC9 cardiovascular health scores and visual impairment in National Health and Nutrition Examination Survey 2013 to 2018, assess the added value of sleep and mental health components, and identify key contributing subcomponents using regression and machine learning methods. Methods: We analyzed data from 9891 participants in the 2013 to 2018 National Health and Nutrition Examination Survey. Multivariable logistic regression, restricted cubic spline modeling, and subgroup analyses were conducted to evaluate the association between cardiovascular health and visual impairment. Machine learning methods, including Random Forest and SHapley Additive exPlanations, a model interpretation approach used to assess feature importance, were applied. The Boruta algorithm, a feature selection method, was used to identify key subcomponents of cardiovascular health. Results: Participants with visual impairment were older, had lower poverty-to-income ratios, and exhibited lower cardiovascular health scores (LS7, LE8, and LC9). Higher cardiovascular health scores were negatively associated with visual impairment. Each one standard deviation increase in LS7, LE8, and LC9 was associated with approximately 8%, 2%, and 3% lower odds of visual impairment, respectively. LS7 showed the strongest negative association, whereas LE8 and LC9 emerged as the most important predictors in the machine learning models. Key subcomponents such as blood glucose, blood pressure, and BMI, as well as mental health status, were strongly associated with visual impairment. Conclusion: Higher cardiovascular health scores were negatively associated with visual impairment. LS7 demonstrated the strongest statistical association, whereas LE8 and LC9 were the most influential features in the machine learning models. Blood glucose, blood pressure, BMI, and mental health status were important correlates of visual impairment. From the perspective of the “prevention and treatment of diseases” in traditional Chinese medicine and the holistic concept, the aforementioned research findings confirm that severe visual impairment is indeed an external manifestation of overall health imbalance (such as organ dysfunction and Qi deficiency and blood stasis). The prevention strategy lies in comprehensively adjusting lifestyle, emotional factors, and metabolic indicators (corresponding to the key factors in the study) to restore the body’s internal balance and achieve prevention before disease arises.

Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Alzheimer's disease(AD)is a neurodegenerative disease with highly heterogeneous pathological and clinical manifesta-tions,and it is the most common cause of dementia.This heterogeneity poses challenges for diagnosis,treatment,and evaluating novel pharmacological efficacy.This review summarizes the latest progress in the major clinical subtypes of AD based on clinical manifesta-tions,genetic,and pathological features.Early-onset and late-onset AD clinical subtypes may share the same symptoms but differ in etiology,age of onset,mode of presentation,disease progression,and associated comorbidities.Typical and atypical AD differ signifi-cantly in clinical manifestations,pathological features,and diagnostic criteria.Research on AD subtypes based on imaging and omics data has also made considerable progress.This review also outlines the molecular pathological heterogeneity of AD.A deep understand-ing of these heterogeneities is crucial for diagnosis,the formulation of pharmacological treatment strategies,and clinical management.

Background:Postoperative nutritional status and nutritional support therapy are important driving factor for clinical outcomes in patients after lung transplantation.This study aims to evaluate the relationship between mNUTRIC scores and prognosis in patients after lung transplantation.Methods:A retrospective inclusion of 253 patients who underwent lung transplantation at Wuxi People's Hospital Affiliated to Nanjing Medical University from January 2020 to December 2022.The nutritional risk in patients after lung transplantation is much higher than in other critically ill patients.To explore the optimal threshold,patients were divided into three groups based on the tertiles of mNUTRIC scores,and clinical outcomes were compared.The predictive ability of the mNUTRIC score was analyzed using receiver operating characteristic(ROC)curve.The appropriate threshold was determined using the Youden index based on the highest combined sensitivity and specificity.Results:Among 253 patients,the 30-day mortality rate was 14.2%.The death group had higher age and BMI,with APACHE II,SOFA,and mNUTRIC scores all higher than those in the survival group.The median mNUTRIC score in the death group was 5.00(3.00～6.00).The higher the mNUTRIC score,the greater the gradual increase in 30-day mortality rate.When the mNUTRIC score was 4～6,the patient mortality rate was 21.21%,and when 7～9,it was 42.31%.The Q3 group had significantly prolonged mechanical ventilation time,was more prone to delayed weaning,had longer ICU length of stay,and higher tracheotomy rate.Multivariate Cox regression analysis and Kaplan-Meier survival curve showed that mNUTRIC score is an independent risk factor for 30-day mortality,with mortality rate increasing as the score increased(P<0.001).The area under the ROC curve(AUC)for mNUTRIC score was 0.765(95%CI:0.686,0.644).According to the Youden index,the optimal cutoff value is when mNUTRIC score equals 3.5,used to predict high nutritional risk and 30-day mortality in lung transplant patients.Conclusion:The mNUTRIC score has a good predictive effect on the prognosis of patients after lung transplantation and is expected to be applied in clinical practice as a routine assessment tool to help clinicians perform postoperative nutritional risk stratification.