Objective To investigate the current distribution of radiotherapy resources in Gansu Province, evaluate the equity of resource allocation, and provide a scientific basis for optimizing regional resource allocation. Methods A questionnaire survey was carried out to assess radiotherapy resources in medical institutions across Gansu Province, China. The equity of radiotherapy resource distribution and associated disparities were assessed using the Gini coefficient, Lorenz curve, and Theil index. Results A total of 23 medical institutions in Gansu Province provided radiotherapy services, comprising 39 radiotherapy devices and 438 professionals, of whom medical physicists accounted for 16.9%. The radiotherapy frequency was 0.47 cases per thousand population. The Gini coefficients for radiotherapy resource distribution ranged from 0.38 to 0.56 by population and from 0.52 to 0.70 by geography. The Theil index for radiotherapy resources ranged from 1.36 to 3.67. Conclusion Radiotherapy resources in Gansu Province were insufficient, and the capacity of radiotherapy service was suboptimal. The equity of radiotherapy resource allocation by geography was worse than that by population. Therefore, it is imperative to address the shortage of radiotherapy resources, strengthen the professional workforce, enhance the capacity radiotherapy service and resource utilization, optimize resource allocation, and promote regional equity in radiotherapy provision in Gansu Province.

ObjectiveTo analyze the association between sleep chronotypes and myopia among junior high school students in Changning District of Shanghai, and to provide scientific basis for myopia intervention in this population. MethodsThis study was conducted as a cross-sectional survey using stratified cluster sampling. Four junior high schools in Changning District were selected as the research sites. Two methods were used to assess chronotypes, including the sleep-wake pattern calculated based on sleep duration and the sleep chronotype evaluated by the Morningness-Eveningness Questionnaire-5 items (MEQ-5). Myopia status of students was determined through distant visual acuity examination combined with non-cycloplegic computerized refraction. The χ2 test or t test was used to compare the distribution differences of sleep chronotypes between the myopic and non-myopic groups. Furthermore, a multivariate logistic regression model was used to analyze the association between sleep chronotypes and myopia. Statistical analyses were performed using SPSS 27.0 software. ResultsA total of 801 middle school students participated in this survey, with a screening-based prevalence of myopia of 81.77%. Univariate analyses showed that both the sleep chronotype calculated based on the sleep-wake pattern (χ²=4.54, P=0.033) and the sleep chronotype assessed by the MEQ-5 (χ²=12.75, P=0.013) were statistically associated with myopia. After adjusting for grade, maternal age at birth, parental myopia, frequency of weekend outdoor activities, and sleep duration, multivariate analyses indicated that a higher grade (e.g., grade 9: OR=3.63, 95%CI=1.92‒6.87) and myopia in both parents (OR=2.73, 95%CI=1.23‒6.06) were associated with a higher risk of myopia. In contrast, older maternal age at birth (OR=0.64, 95%CI=0.49‒0.85) and more frequent weekend outdoor activities (e.g., 1‒2 times per week: OR=0.21, 95%CI=0.05‒0.88; ≥5 times per week: OR=0.16, 95%CI=0.04‒0.71) were associated with a lower risk of myopia. The association between MEQ-5 chronotype and myopia was not statistically significant after multivariate adjustment (P=0.124). However, compared with the “absolute evening type”, the odds ratios (ORs) for myopia risk across sleep chronotypes decreased from 3.22 (moderate evening type) to 0.57 (absolute morning type), with fluctuations in between (0.95 for the “intermediate type” and 0.98 for the “moderate morning type”). ConclusionA sleep chronotype tending toward the “evening type” in junior high school students may be associated with an increased risk of myopia, and this association requires further prospective studies to validate.

ObjectiveTo investigate the correlation between sleep patterns and myopia progression among younger school-age children at a primary school in Changning District of Shanghai, based on the data from the Shanghai Students’ Common Diseases and Health Influencing Factors Monitoring System and a sleep-specific survey, so as to provide data support for myopia prevention and control in this age group. MethodsOne primary school was selected from the common diseases and health influencing factors monitoring system for students in Changning District, Shanghai. A total of 230 first-grade students were included in the study. Myopia and refractive parameters were examined, and sleep patterns were investigated. General demographic characteristics and myopia-related behavior data of the students were also collected. Sleep patterns were evaluated in terms of sleep duration, sleep efficiency, and sleep quality, with the latter assessed using the Chinese version of the Children’s Sleep Habits Questionnaire (CSHQ). Multiple linear regression and binary logistic regression models were used to analyze the association between sleep patterns and myopia progression among these students. ResultsThe results of the regression analyses revealed that the total CSHQ score of the students at baseline survey was (48.85±7.15) points. Their sleep efficiency was (94.49±8.48)%, sleep duration was (9.58±0.93) hours, and the proportion of those with insufficient sleep (<10 hours) was 78.26%. At baseline survey, students’ higher daytime sleepiness scores were associated with lower spherical equivalent (SE) ( β=-0.18, 95%CI: -0.31 to -0.04) and an increased risk of axial length (AL) / corneal radius (CR) ratio >3 (OR=1.52, 95%CI: 1.00 to 2.29), whereas longer sleep duration and higher sleep efficiency were associated with higher SE (β=0.18, 95%CI: 0.05 to 0.32; β=0.17, 95%CI: 0.04 to 0.31, respectively), shorter (AL) (β=-0.15, 95%CI: -0.27 to -0.03; β=-0.13, 95%CI: -0.25 to 0, respectively) and a reduced risk of AL /CR>3 (OR=0.70, 95%CI: 0.51 to 0.96; OR=0.73, 95%CI: 0.53 to 0.99, respectively). At baseline survey, children’s higher propensity for sleep problems (OR=1.70, 95%CI: 1.04 to 2.78), sleep resistance (OR=2.26, 95%CI: 1.36 to 3.75), and sleep anxiety scores (OR=2.15, 95%CI: 1.33 to 3.48) were all associated with an increased risk of AL/CR >3 at follow-up (all P<0.05). Furthermore, higher sleep anxiety scores predicted prolonged AL at follow-up (β=0.03, 95%CI: 0 to 0.05). According to the mixed-effects model, higher daytime sleepiness scores and prolonged sleep duration were independently linked to reduced right-eye SE (β=-0.05, 95%CI: -0.10 to 0, P<0.05) and shorter right-eye AL (β=-0.05, 95%CI: -0.10 to 0, P<0.05). ConclusionIn this school in Shanghai, there are problems of insufficient and poor-quality sleep among young children. Sleep problems such as sleep resistance, delayed sleep onset, sleep anxiety, and daytime sleepiness among children may accelerate the risk of myopia progression, while longer sleep duration and higher sleep efficiency may serve as protective factors against the occurrence and development of myopia.

Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

Objective To analyze the relationship of capillary index score(CIS)with postoperative outcome in the patients with middle cerebral artery(MCA)M1 segment stenosis after endovascu-lar stent implantation.Methods A total of 122 eligible patients in our hospital from April 2021 to April 2023 were retrospectively recruited,and according to postoperative status,they were classi-fied into a good prognosis group(89 cases)and a poor prognosis group(33 cases).The clinical data were collected,and the preoperative and postoperative CIS values were evaluated.Spearman correlation analysis was applied to analyze the correlation of CIS with prognosis of the patients.ROC curve was plotted to evaluate the value of CIS in predicting postoperative prognosis,and multivariate logistic regression analysis was utilized to analyze the factors affecting the postopera-tive prognosis.Results The poor prognosis group exhibited significantly lower CIS,and larger proportions of aged ≥75 years,diabetes and history of stroke than the good prognosis group(P＜0.05,P＜0.01).Spearman correlation analysis found that CIS was positively correlated with prog-nosis after endovascular stent implantation in the patients with MCA M1 segment stenosis(r=0.446,P=0.000).ROC curve analysis revealed that the AUC,cut-off value,sensitivity and speci-ficity of CIS in predicting the postoperative outcomes in these patients was 0.790(95％CI:0.706-0.858),2.17,84.85％and 66.29％,respectively(P＜0.05).Multivariate logistic analysis suggested that age ≥75 years,history of stroke,and concurrent diabetes were high risk factors for poor prognosis in the patients after endovascular stent implantation(P＜0.05,P＜0.01),and CIS was a protective factor(OR=0.439,95％CI:0.294-0.655,P=0.000).Conclusion CIS is closely correlated with the prognosis of patients with MCA M1 segment stenosis after endovascular stent implantation.Early evaluation of CIS can provide certain predictive value for postoperative out-comes of these patients.

Objective:To evaluate the accuracy and feasibility of an automated scanning and uptake system to assist pathologists with hu-man epidermal growth factor receptor 2(HER2)FISH interpretation.Methods:HER2 gene amplification is detected using FISH,and"result interpretation by independent pathologists"is regarded as the"gold standard."The consistency of"human-machine dialogue results"(use of a CytoVision* system combined with manual interpretation)and"CytoVision*-based automated interpretation"with the"gold standard"was assessed.Results:Consistency between"human-machine dialogue results"and the"gold standard"can surpass 91%,with the former method saving up to 50%of the manual operation time.The tendency of each cell nucleus's HER2 copy number to be"underestimated"is the main reason for the low sensitivity observed in cases with low copy number amplification and HER2 heterogeneous expression cases in"human-machine dialogue interpretation."Conclusions:Automatic FISH image analysis and uptake systems simulate the process of manu-ally interpreted cell selection,ensure random cell selection,and improve work efficiency.With its accurate selection of the hybridization re-gion and"human-computer dialogue,"the system is expected to"replace"interpretation by independent pathologists.

Objective To explore the regulatory effect of long non-coding RNA HOX transcript anti-sense RNA(lnRNA HOTAIR)targeting tuberous sclerosis complex 1(TSC1)on podocyte injury in diabetic nephropathy(DN).Methods Mice podocyte MPC5 were divided into 6 groups:the control group,the high glucose group(the HG group),the HG+si-NC group,the HG+si-HOTAIR group,the HG+si-HOTAIR+sh-NC group,and the HG+si-HOTAIR+sh-TSC1 group.qPCR was used to detect the levels of lnRNA HO-TAIR and TSC1 mRNA.Cell viability was detected by cell counting kit-8(CCK-8),and apoptosis was detec-ted by flow cytometry.ELISA was used to detect the levels of inflammatory factors[IL-6,tumor necrosis fac-tor-α(TNF-α)]and the levels of oxidative stress indicators[reactive oxygen species(ROS),malondialdehyde(MDA),superoxide dismutase(SOD)]were detected by corresponding reagent kit.Results Compared with the control group,the levels of lnRNA HOTAIR,apoptosis rate,IL-6,TNF-α,ROS and MDA in the HG group were significantly increased(P<0.05),while the levels of TSC1 mRNA,cell viability and SOD were significantly decreased(P<0.05).Compared with the HG+si-NC group,the levels of lnRNA HOTAIR,ap-optosis rate,IL-6,TNF-α,ROS and MDA in the HG+si-HOTAIR group significantly decreased(P<0.05),the levels of TSC1 mRNA,cell viability and SOD were significant increased(P<0.05).Compared with the HG+si-HOTAIR+sh-NC group,the levels of apoptosis rate,IL-6,TNF-α,ROS and MDA in the HG+si-HOTAIR+sh-TSC1 group were significantly increased(P<0.05),the levels of TSC1 mRNA,cell viability and SOD were significantly decreased(P<0.05).Conclusion Silencing lnRNA HOTAIR can target TSC1 to regulate the activity,apoptosis rate,inflammatory level and oxidative stress levels of podocyte injury in DN,thereby alleviating podocyte damage in DN.

Objectives:The aim is to analyze the cost structure and coefficient of variation for hospitalized patients with malnutrition based on Diagnosis-Related Groups(DRG),providing a reference for the further application and promotion of DRG.Method:Data were collected from patients admitted to Ningxia Hui Autonomous Region People's Hospital between March 2023 and August 2023.A diagnostic system based on artificial intelligence was used to identify malnourished patients.The composition of hospitalization costs for these individuals was described and analyzed,as was the coefficient of variation for various costs within DRG groupings.A multivariate regression analysis was conducted to identify the factors that influence patient hospitalization costs.Results:The average age of hospitalized patients with malnutrition was(68.12±16.43)years,with an average length of stay of(14.55±8.47)days,with an average hospitalization cost of(32 128.89±35 345.61)yuan.Among patients within the same DRG group,the coefficient of variation for various costs was found to be lower in the malnutrition group than in the normal group.This suggests that when assessed individually,the malnutrition group exhibited a higher degree of homogeneity in their cost structures.The factors influencing total hospitalization costs were found to be:length of hospital stay(P=0.001),nutritional monitoring fees(P=0.020),number of chronic diseases(P=0.003),and Karnofsky Performance Status(KPS)score(P=0.038).Hospitalization costs were positively correlated with both length of stay and nutritional assessment fees,but negatively correlated with the number of chronic diseases and KPS scores.Conclusions:Malnutrition has a profound impact on health outcomes,medical expenses,length of hospital stay,and disease severity.The implementation of the DRG system aims to standardize and improve the nutritional diagnosis and treatment process by categorizing different stages of malnutrition.This approach can minimize variations within DRG groups,making it easier to allocate medical resources more precisely and efficiently.Furthermore,it is a valuable reference tool for promoting DRG payment reform in different regions.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.