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.

Eleven sesquiterpenoids were isolated from the petroleum ether and ethyl acetate extracted fraction of 95% ethanol extract of fresh Centipeda minima by using modern chromatographic separation techniques such as silica gel, MCI, gel, and semi-preparative liquid chromatography. Their structures were identified using spectroscopy and nuclear magnetic resonance(NMR) calculation as minimin A(1), brevilin A(2), minimolide L(3), minimolide A(4), minimolide B(5), arnicolide D(6), microhelenin C(7), 2β-hydroxyl-2,3-dihydrogen-6-O-angeloylplenolin(8), 11α,13-dihydroarnifolin(9),(1S,2R,5R,6S,7S,8S,10R)-6-hydroxy-2-ethoxy-4-oxopseudoguai-11(13)-en-12,8-olide(10), and pulchellin-2-O-isovalerate(11), among which compound 1 was a new compound, and compounds 9-11 were isolated from Centipeda for the first time. The evaluation results of in vitro anti-inflammatory activity showed that compounds 1-11 possessed significant anti-inflammatory activity, with IC_(50) values ranging from(0.13±0.03) to(13.11±0.17) μmol·L~(-1).
Sesquiterpenes/pharmacology* ; Animals ; Asteraceae/chemistry* ; Mice ; Drugs, Chinese Herbal/pharmacology* ; Molecular Structure ; Magnetic Resonance Spectroscopy ; Macrophages/immunology*

This study aimed to explore the effects and mechanisms of total extracts from Anthriscus sylvestris on pulmonary hypertension in rats. Sixty male SD rats were divided into normal(NC) group, model(M) group, positive drug sildenafil(Y) group, low-dose A. sylvestris(ES-L) group, medium-dose A. sylvestris(ES-M) group, and high-dose A. sylvestris(ES-H) group. On day 1, rats were intraperitoneally injected with monocrotaline(60 mg·kg~(-1)) to induce pulmonary hypertension, and the rat model was established on day 28. From days 15 to 28, intragastric administration of the respective treatments was performed. After modeling and treatment, small animal echocardiography was used to detect the right heart function of the rats. Arterial blood gas was measured using a blood gas analyzer. Hematoxylin and eosin(HE) staining and Masson staining were performed to observe cardiopulmonary pathological damage. Flow cytometry was used to detect apoptosis in the lung and myocardial tissues and reactive oxygen species(ROS) levels. Western blot was applied to detect the expression levels of transforming growth factor-β1(TGF-β1), phosphorylated mothers against decapentaplegic homolog 3(p-Smad3), Smad3, tissue plasminogen activator(t-PA), and plasminogen activator inhibitor-1(PAI-1) in lung tissue. A blood routine analyzer was used to measure inflammatory immune cell levels in the blood. Enzyme-linked immunosorbent assay(ELISA) was used to detect the expression levels of P-selectin and thromboxane A2(TXA2) in plasma. The results showed that, compared with the NC group, right heart hypertrophy index, right ventricular free wall thickness, right heart internal diameter, partial carbon dioxide pressure(PaCO_2), apoptosis in cardiopulmonary tissue, and ROS levels were significantly increased in the M group. In contrast, the ratio of pulmonary blood flow acceleration time(PAT)/ejection time(PET), right cardiac output, change rate of right ventricular systolic area, systolic displacement of the tricuspid ring, oxygen partial pressure(PaO_2), and blood oxygen saturation(SaO_2) were significantly decreased in the M group. After administration of the total extract of A. sylvestris, right heart function and blood gas levels were significantly improved, while apoptosis in cardiopulmonary tissue and ROS levels significantly decreased. Further testing revealed that the total extract of A. sylvestris significantly decreased the levels of interleukin-1β(IL-1β), interleukin-6(IL-6), and PAI-1 proteins in lung tissue, while increasing the expression of t-PA. Additionally, the extract reduced the levels of inflammatory cells such as leukocytes, lymphocytes, granulocytes, and monocytes in the blood, as well as the levels of P-selectin and TXA2 in plasma. Metabolomics results showed that the total extract of A. sylvestris significantly affected metabolic pathways, including arginine biosynthesis, tyrosine metabolism, and taurine and hypotaurine metabolism. In conclusion, the total extract of A. sylvestris may exert an anti-pulmonary hypertension effect by inhibiting the TGF-β1/Smad3 signaling pathway, thereby alleviating immune-inflammatory responses and thrombosis.
Animals ; Male ; Smad3 Protein/metabolism* ; Transforming Growth Factor beta1/metabolism* ; Rats, Sprague-Dawley ; Rats ; Signal Transduction/drug effects* ; Hypertension, Pulmonary/genetics* ; Thrombosis/immunology* ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Apoptosis/drug effects*