Objective To explore the clinical factors influencing complete response in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). Methods Clinical data of LARC patients treated in the Department of Radiation Oncology at Beijing Shijitan Hospital between January 2013 and December 2024 were retrospectively collected. All patients received nCRT, after which surgery or a watch-and-wait approach was adopted based on treatment response. Univariable and multivariable logistic regression analyses were performed to identify prognostic factors influencing complete response. A clinical prediction model was constructed based on the multivariable analysis results, and its predictive performance was evaluated using the receiver operating characteristic curve. Results A total of 113 eligible patients were included. After nCRT, 19 patients (16.8%) achieved complete response, including 3 with clinical complete response and 16 with pathological complete response. Univariable analysis indicated that pretreatment clinical N stage, extramural venous invasion, carcinoembryonic antigen level, and neoadjuvant treatment regimen were associated with complete response after nCRT (P<0.05). Multivariable logistic regression analysis identified pretreatment extramural venous invasion, carcinoembryonic antigen level, and neoadjuvant treatment regimen as independent influencing factors for complete response (P<0.05). A prediction model incorporating these independent factors yielded an area under the receiver operating characteristic curve of 0.813 (95% confidence interval: 0.713-0.913), with a sensitivity of 89.5% and a specificity of 60.6%, demonstrating good predictive performance. Conclusion Pretreatment extramural venous invasion, carcinoembryonic antigen level, and neoadjuvant treatment regimen are independent factors influencing complete response after nCRT in LARC patients. The prediction model combining these factors may assist in evaluating treatment efficacy following nCRT in LARC patients.

ObjectiveTo investigate and manage an imported dengue fever (DF) outbreak in Shanghai in 2024, to summarize the experience and lessons learned from the on-site management, and to provide a reference basis for future prevention and control of DF. MethodsEpidemiological investigation and case search were carried out for an imported DF outbreak in Shanghai, 2024. Real-time fluorescence polymerase chain reaction (RT-PCR) was used to detect dengue virus nucleic acid in the serum samples from cases. Meanwhile, emergency vector surveillance and mosquito control measures were carried out in the affected areas, and the effectiveness of the management was evaluated. ResultsAccording to the epidemiological investigation, it was confirmed that this epidemic was a family cluster of imported DF, with both cases infected in Thailand and developed symptoms successively after returning to Shanghai. Laboratory testing identified the pathogens as dengue virus serotype-3 (DENV-3). In the core and precautionary area, ultra-low-volume space spraying and residual spraying were combined to kill adult mosquitoes, and at the same time, comprehensive cleaning and elimination of mosquito breeding sites was carried out. After 2 weeks, the Breteau Index (BI) in the core area decreased from 20 to 5, and the mosquito net trap index decreased from 2 mosquitoes (net·hour)^-1 to 0.67 mosquitoes (net·hour)^-1. Continuous implementation of mosquito control measures kept the BI and net trap index below the safety thresholds [BI<5 and mosquito net trap index <2 mosquitoes (net·hour)^-1] both in the core and precautionary area. ConclusionEarly diagnosis and isolation of patients, combined with rapid suppression of the density of vector Aedes mosquitoes, are the key measures to prevent the transmission of imported DF cases.

Objective: To understand the mosquito infestation status in schools in Jing an District of Shanghai from 2019 to 2025, so as to provide a basis for mosquito control work in schools. Methods: Four schools of each school type (colleges and secondary specialized schools, primary and secondary schools, nurseries and kindergartens) were respectively selected from Jing an District as the survey sites by combination of stratified sampling and convenience sampling. Mosquito breeding sites surveys (including small stagnant water and medium and large static water) and adult mosquito monitoring were carried out in schools every August, from 2019 to 2025. The breeding sites surveys were investigated by visual inspection and spoon trapping methods, and the adult mosquito monitoring was investigated by the human baited trapping. Chi square test was used for inter group comparison. Results: A total of 970 small stagnant water were surveyed from 2019 to 2025, with the positive rate of 17.01% for mosquito breeding. The highest positive rate was in colleges and secondary specialized schools (25.07%). The top three positive breeding sites for mosquito were unused containers(27.46%), waste tires (16.67%), and bonsai and aquatic plants (16.52%). There were statistically significant differences in the positive rates for mosquito breeding among different years, school types and waterlogging types ( χ 2=122.70, 28.48 , 24.80, all P <0.01). A total of 640 medium and large static water were surveyed, and the positive rate for mosquito breeding was 7.34%. There was a statistically significant difference for the positive rate among different years ( χ 2= 50.23 , P <0.01). The highest positive rate for mosquito breeding was in colleges and secondary specialized schools (9.63%), and there was no statistically significant difference for the positive rate among different school types ( χ 2=4.41, P =0.11). From 2019 to 2025, a total of 390 mosquitoes were captured in the time period from 16:30 to 17:00, all of which were Aedes albopictus; the adult mosquito density in colleges and secondary specialized schools was the highest, being 2.38 mosquitoes/(0.5 h ·person). A total of 210 mosquitoes were captured in the time period from 19:00 to 19:30 during 2019-2025, among which Aedes albopictus accounted for 74.76% and Culex pipiens pallens accounted for 25.24%. The adult mosquito density in colleges and secondary specialized schools was the highest, being 1.15 mosquitoes/(0.5 h ·person). In colleges and secondary specialized schools, the area with the largest number of captured mosquitoes was around the teaching buildings and libraries(44.78%). In primary and secondary schools, as well as nurseries and kindergartens, the area with the largest number of captured mosquitoes was around the school gates and playgrounds( 49.44% , 53.60%), the difference in mosquito distribution in different regions among different types of schools was statistically significant ( χ 2=111.39, P <0.01). Conclusions The mosquito density in Jing an District schools is generally higher during summer. It is recommended that the school strengthen the management of mosquito breeding sites and the control of adult mosquitoes before the start of autumn semester, so as to prevent insect infestation of mosquitoes.

OBJECTIVE To construct a prediction model for tacrolimus trough concentration based on population pharmacokinetics （PPK） information and machine learning， evaluate its predictive performance in clinical therapeutic drug monitoring （TDM） data， and provide evidence-based support for individualized dose adjustment of tacrolimus after renal transplantation. METHODS A retrospective cohort study design was adopted， and consecutive TDM records of kidney transplant recipients treated in the First Affiliated Hospital of University of Science and Technology of China （Anhui Provincial Hospital） from January to June 2025 were included to construct the final analysis set. Based on 29 input variables including previous TDM concentration， previous concentration/dose （ C / D ） ratio， and individual predicted exposure （IPRE）， 13 commonly used regression algorithm models and 1 weighted ensemble model were systematically compared. The dataset was split into training set and test set in an 80∶20 ratio by patient. Five-fold cross-validation based on patient grouping was performed on the training set， and the model performance was evaluated on the obtained evaluation points using the coefficient of determination （ R 2 ）， mean absolute error （MAE）， root mean square error （RMSE）， Pearson correlation coefficient （Corr） and ±20%/±30% accuracy. The SHapley Additive exPlanations （SHAP） method was used for model interpretability analysis to identify core predictive features. RESULTS A total of 809 consecutive TDM records from 83 kidney transplant recipients were included， with 66 cases in the training set and 17 cases in the test set. The SVR model showed the best performance， with R 2 of 0.649 8， MAE of 1.126 0 ng/mL， RMSE of 1.478 6 ng/mL， Corr of 0.819 1， ±20% accuracy of 59.68%， and ±30% accuracy of 79.03%. The Weighted Ensemble model and Extra Trees model ranked second and third， respectively. SHAP interpretability analysis showed that the top 6 features contributing to the prediction were IPRE， previous TDM concentration， previous C/D ratio， dose-normalized concentration， hematocrit （HCT）， and concentration-dose coefficient （ θ cd ）， among which IPRE was the core predictive feature （SHAP mean value 0.915 4）. CONCLUSIONS The machine learning model incorporating PPK information demonstrates good predictive ability for tacrolimus concentration in the current TDM cohort. SVR is the optimal model， IPRE is the core predictive feature of the model， and a few core PK-derived features （IPRE， dose-normalized concentration， θ cd ） together with clinical features （previous TDM concentration， HCT， etc.） constitute the main information sources for model prediction.

Objective To analyze the value of contrast-enhanced ultrasound (CEUS) quantitative parameters in identifying benign and malignant ovarian tumors so as to provide reference for ultrasound diagnosis of ovarian cancer. Methods A total of 116 patients with ovarian tumors admitted to the hospital between February 2021 and February 2024 were enrolled. All patients underwent CEUS examination. Pathological fingdings served as the gold standard to evaluate the diagnostic performance of CEUS quantitative parameters. Results Pathological examination identified 74 benign and 42 malignant ovarian tumors. No significant differences were observed between the two groups in terms of initial enhancement time, enhancement intensity or regression rate (P>0.05). The time to peak was significantly longer in patients with benign tumors than in those with malignant tumors (P<0.05). Whereas peak intensity and enhancement rate were significantly lower in the benign goup (P<0.05). ROC curves analysis showed that when using a single parameter for diagnosis, enhancement rate yielded the largest area under the curve (AUC) of 0.977. Its sensitivity and specificity were 95.24% and 98.65%. The combination of parameters achieved an even larger AUC of 0.998, with a sensitivity of 97.62% and a specificity of 98.65%. Conclusion The combination of CEUS quantitative (time to peak, peak intensity and enhancement rate) demonstrates good diagnostic value for benign and malignant ovarian tumors and improves diagnostic sensitivity.

Objective To investigate the effects of combined exposure to benzene series, dust and noise on occupational high-frequency hearing loss (HFHL) in the electronics manufacturing industry. Methods A total of 740 employees from an electronic manufacturing enterprise were selected as the study subjects. According to the detection of occupational hazard factors, they were divided into noise group, noise benzene group, and noise dust group, with 325, 167, and 248 people, respectively. Occupational health examinations were conducted on the study participants to analyze their HFHL detection status and influencing factors. Results Among all participants (n=740), 456 (61.62%) were male, 284 (38.38%) were female, and 123 (15.62%) were detected with HFHL. The detection rate of HFHL increased with age and length of service (P<0.01). According to the exposure factor grouping, the detection rates of HFHL in the noise group, noise benzene group, and noise dust group were 12.00%, 19.16%, and 20.97%, respectively (P<0.05). Multivariate logistic regression analysis showed that workers aged ≥ 40 years, with 10-20 years of work experience, ≥ 20 years of work experience, exposure to noise and benzene series, and exposure to noise and dust had an increased risk of developing HFHL to 1.734, 1.923, 3.071, 1.706, and 1.527, respectively (P<0.05). Conclusion Combined exposure to noise and benzene series, combined exposure to noise and dust, as well as age and extended length of service may increase the risk of HFHL among workers.

ObjectiveTo investigate the current status of ethical sensitivity among clinical nursing interns in the context of artificial intelligence （AI） and analyze its influencing factors. MethodsAdopting mixed research methods and convenience sampling， a total of 237 nursing interns from a tertiary A hospital in Xi’an were selected as research subjects. Data were collected using a general information questionnaire and the Nursing Students’ Ethical Sensitivity Scale， and semi-structured interviews were conducted with 8 nursing interns. ResultsThe total score of ethical sensitivity among nursing interns was （33.98±6.32）， which was at a moderate level. Multiple linear regression analysis revealed that academic degree （undergraduate： β=0.147， P<0.05； postgraduate： β=0.139， P<0.05）， affinity for the nursing profession （β=0.118， P<0.05）， whether they had taken nursing ethics courses （β=-0.124， P<0.05）， and familiarity with artificial intelligence （β=-0.159，P<0.05） were influencing factors of ethical sensitivity among nursing interns. Four themes were extracted from semi-structured interviews， namely， the incomplete mastery of nursing ethics-related knowledge among nursing interns， weakened ethical sensitivity in the AI teaching scenario of nursing interns， insufficient professional identity affecting the development of nursing interns’ ethical sensitivity， and technological dependence leading to the lack of humanistic care among nursing interns. ConclusionEthical sensitivity of nursing interns represents a moderate level， primarily influenced by educational background， degree of fondness for the nursing profession， learning experience of nursing ethics courses， and familiarity with AI. Nursing educators and clinical administrators urgently need to establish a sustainable school-hospital collaborative model of nursing ethics education， attach importance to the cultivation of ethical awareness and value guidance for nursing interns， and strengthen the intensity and depth of nursing ethics education among nursing interns， thereby improving their level of ethical sensitivity in the context of AI.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

Objective To investigate the value of ¹⁸F-FDG PET/CT metabolic parameters in the prognostic assessment of nasopharyngeal cancer patients. Methods The clinical data and PET/CT metabolic parameters of 185 nasopharyngeal cancer patients were retrospectively analyzed. The collected parameters were SUV_max, MTV, TLG, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG). The ROC curve was used to determine the optimal cut-off values of PET/CT metabolic parameters. Univariate and multivariate Cox regression models were used to screen the independent prognostic factors. Kaplan–Meier curves were used to analyze the survival differences. Results The results of univariate Cox regression analysis showed that age, pathologic type, WTLG, TMTV, MTV, and TLG were closely associated with OS and PFS; and SUV_max was associated with PFS (P<0.05). Multivariate Cox regression analysis results showed that age, TMTV, and WTLG were the independent prognostic factors for OS and PFS (P<0.05). The combination of WTLG with T/N staging (AUC=0.781 and 0.781) and TMTV with T/N staging (AUC=0.800 and 0.790) yielded greater predictive accuracy than that of WTLG and TMTV alone (AUC=0.724 and 0.719) or T/N staging (AUC=0.593 and 0.575). Conclusion TMTV and WTLG are important prognostic predictors of nasopharyngeal carcinoma. TLG and MTV of primary lesions are prognostic factors for patients’ PFS and OS. SUV_max has limited prognostic value. Systemic metabolic indexes (TMTV and WTLG), when combined with T/N staging, can optimize prognostic stratification.