: To analyze the prevalence of anemia and iron deficiency among primary and secondary school students in Rural Nutrition Improvement Program areas of Guizhou Province in 2023, and to explore the related factors, so as to provide evidence for Rural Nutrition Improvement Program optimization. Methods: In September 2023, a stratified random cluster sampling strategy was used to select 40 rural compulsory education schools with rural nutrition improvement program in five counties of Guizhou Province. School level questionnaire was employed to collect information of basic characteristics and school meal implementation. A total of 7 826 primary and secondary school students aged 6-16 underwent anthropometry and hemoglobin (Hb) determination; serum ferritin (SF) was additionally measured in a random subsample of 1 795 pupils. Students in Grade 3 and above also completed a questionnaire covering demographic characteristics, dietary behaviours and nutrition knowledge. Group comparisons were conducted by Chi square test or Fisher s exact test, and multivariable Logistic regression models were constructed to identify factors associated with anemia and iron deficiency. Results: The overall Hb level was (133.21±12.95)g/L, with an anemia prevalence of 7.17%. The overall SF level was (69.58±59.01)μg/L, with an iron deficiency prevalence of 2.73%. Multivariable analysis showed that stunting ( OR =1.88), school menus without nutrient calculation ( OR =1.61) and absence of menu planning software in the current semester ( OR =2.34) independently increased anemia risk, whereas obesity reduced it ( OR =0.54) (all P <0.05). Girls ( OR =4.16) and Grades 7-9 ( OR =5.93) increased iron deficiency risk (both P <0.05). Compared with rarely eating fresh vegetables, students with consuming <3 kinds per day ( OR =0.08) or exactly 3 kinds per day ( OR =0.06) had lower iron deficiency risks (both P <0.05). Conclusions Anemia and iron deficiency are prevalent among primary and secondary school students in Guizhou. Targeted intervention measures should be implemented for key populations to enhance the effectiveness of nutrition improvement program.

Background: Disorders of the blood-brain barrier (BBB) arising from diabetes mellitus are closely related to diabetic encephalopathy. Previous research has suggested that neuron-glia antigen 2 (NG2)-glia plays a key role in maintaining the integrity of the BBB. However, the mechanism by which NG2-glia regulates the diabetic BBB remains unclear. Methods: Type 2 diabetes mellitus (T2DM) db/db mice and db/m mice were used. Evans-Blue BBB permeability tests and transmission electron microscopy techniques were applied. Tight junction proteins were assessed by immunofluorescence and transmission electron microscopy. NG2-glia number and signaling pathways were evaluated by immunofluorescence. Detection of matrix metalloproteinase-9 (MMP-9) in serum was performed using enzyme-linked immunosorbent assay (ELISA). Results: In T2DM db/db mice, BBB permeability in the hippocampus significantly increased from 16 weeks of age, and the structure of tight junction proteins changed. The number of NG2-glia in the hippocampus of db/db mice increased around microvessels from 12 weeks of age. Concurrently, the expression of MMP-9 increased in the hippocampus with no change in serum. Sixteen- week-old db/db mice showed activation of the Wnt/β-catenin signaling in hippocampal NG2-glia. Treatment with XAV-939 improved structural and functional changes in the hippocampal BBB and reduced MMP-9 secretion by hippocampal NG2-glia in db/db mice. It was also found that the upregulation of β-catenin protein in NG2-glia in the hippocampus of 16-week-old db/db mice was significantly alleviated by treatment with XAV-939. Conclusion The results indicate that NG2-glia can lead to structural and functional disruption of the diabetic BBB by activating Wnt/β-catenin signaling, upregulating MMP-9, and degrading tight junction proteins.

Objective: To address systemic challenges in the blood system, such as supply-demand imbalance, inefficient allocation, and inappropriate clinical use, while bridging gaps in current theories regarding the integration of social mobilization, institutional practice, and policy coordination. It sought to construct a system dynamics model spanning from macro to micro levels to analyze the blood management system holistically. Methods: Key variables were identified by a systematic literature search in both Chinese and English databases. An improved Delphi method was then employed to conduct expert consultations across different fields, leading to the selection and determination of core variable sets for each sub-model. Furthermore, by defining their logical relationships, a system dynamics model was constructed to systematically analyze the operation mechanism of the blood management system. Results: Four core sub-models were developed: 1) A macro "Dynamic Balance" model quantifying regional supply-demand equilibrium and inventory control; 2) a mobilization "Three-Layer Funnel" model analyzing how socio-cultural factors, service channels, and policy incentives influence donation behavior; 3) an institutional "Dual-Cycle Regulation" model revealing hospital blood usage is driven by both disease burden (demand cycle) and management practices (regulation cycle); and 4) an individual "Three-Layer Filter" model standardizing clinical transfusion decisions based on necessity, risk-benefit, and context. These were integrated into a "Multi-Layer Linkage and Feedback" model, elucidating bidirectional interactions among five levels: policy environment, regional supply, blood station mobilization, hospital application, and clinical decision-making. Conclusion: This study constructed a system dynamics model for blood management. By defining key variables and their logical relationships, it systematically analyzes the system′s operational mechanism. The integrated framework connects multiple levels—regional supply, voluntary donation, hospital blood use, and clinical decision-making—revealing their intrinsic linkages. Future efforts should employ systems thinking to synergistically enhance supply-side mobilization, demand-side management, systemic regulation, and decision standardization to build a safe, efficient, and sustainable blood security system.

Background Air pollution has widespread adverse health effects on human health, but research on its impact on reproductive health and related physiological mechanisms remains insufficient. Objective To investigate the associations between short-term exposure to particulate matter and nitrogen dioxide (NO₂) and serum levels of anti-Müllerian hormone (AMH) and testosterone in healthy adults. Methods A total of 152 healthy adults were recruited for a panel study, undergoing four follow-up visits from September 2019 to January 2020. Basic demographic information and serum samples were collected. Reproductive hormone levels were measured by enzyme-linked immunosorbent assay kits. Concurrently, environmental data in the study area were monitored, including fine particulate matter (PM_2.5), black carbon (BC), NO₂, particle number concentrations in the 5-560 nm size fraction (PNC_{5_560}), and meteorological variables. Linear mixed-effects models were constructed to estimate the effects of short-term exposure to these particulate matter on serum reproductive hormone levels. Results The participants had an average age of (23.9±2.4) years, with 102 women. For each interquartile range (IQR) increment in pollutant moving average concentrations, PM_2.5, PNC_{5_25}, PNC_{100_560}, BC, and NO₂ were associated with relative decreases in female serum AMH levels by 10.2%-16.9%, 46.2%-58.0%, 16.9%-21.2%, 14.8%-26.5%, and 11.3%-17.6%, respectively. These negative associations were stronger in females with waist-to-hip ratio > 0.76 and physical activity levels ≤ 1217 metabolic equivalents (MET) min per week. Similarly, in males, an IQR increase in the same pollutants was associated with relative decreases in serum free testosterone by 11.1%-17.2%, 35.2%, 10.5%-18.9%, 18.7%-24.2%, and 10.3%-26.0%, respectively. These effects were more pronounce in male with a waist-to-hip ratios > 0.81 and physical activity levels ≤ 1498 MET-min per week. Conclusion Short-term exposure to particulate matter and NO₂ may reduce serum levels of AMH and testosterone in healthy adults. Furthermore, these adverse effects appears to be stronger in adults with higher waist-to-hip ratios.

Objective: To investigate the longitudinal associations between dietary patterns in early childhood and myopia among children, so as to provide evidence for improving early childhood dietary patterns and promoting healthy ocular development among children. Methods: Participants were derived from the Ma-anshan Birth Cohort (MABC). A total of 1 432 children were included. Dietary patterns at 2.5 years of age were collected in 2017 using a food frequency questionnaire completed by parents. In 2022, cycloplegic refraction and ocular biometric parameters were measured in children aged 7-8 years. Food components were grouped into tertiles ( T1-T 3), and latent class analysis (LCA) was used to identify dietary pattern at the age of 2.5. Log-binomial regression and multiple linear regression model were applied to examine the associations of dietary patterns and their latent classes with myopia, spherical equivalent (SE), and axial length (AL). Results: The prevalence of myopia among children was 23.9%. After adjusting for covariates, the T 2 group of cereals and tubers was negatively associated with myopia rate ( RR=0.72, 95%CI =0.57-0.90) and AL ( β=-0.15, 95%CI =-0.24 to -0.05), and positively associated with SE ( β=0.21, 95%CI =0.06-0.35)(all P ＜0.05). Linear regression models showed that the T 2 group of fruits and vegetables was positively associated with AL ( β=0.09, 95%CI =0.00- 0.19 ); the T 3 group of high sugar foods ( β=0.11, 95%CI =0.00-0.21) and the T 3 group of processed foods ( β= 0.10 , 95%CI =0.00-0.19) were positively associated with AL; total scores for high sugar foods ( β=0.07, 95%CI =0.00-0.14) and processed foods ( β=0.09, 95%CI =0.01-0.17) were positively associated with AL (all P <0.05). After adjusting for sex, age, and parental myopia, the comprehensive high intake pattern was positively associated with AL ( β=0.12, 95%CI =0.00-0.23, P <0.05). After further adjusting for daily screen time and daily outdoor activity time, the association was no longer statistically significant ( β= 0.11 , 95%CI =-0.00-0.23, P >0.05). Conclusions Moderate consumption of cereals and tubers in early childhood may reduce the risk of myopia and delay AL growth. In contrast, high sugar and highly processed diets may promote AL and increase myopia risk. Establishing healthy dietary habits in early childhood and limiting intake of high sugar and processed foods is crucial for promoting ocular development in children.

ObjectiveTo develop and evaluate an O-RADSbased multiparametric model integratingcontrast-enhanced US （CEUS） and the z-score standardized risk of ovarian malignancy algorithm （ROMA z-score） to improve the diagnostic accuracy for differentiating benign， borderline， and malignant ovarian epithelial tumors （OETs）. MethodsThis retrospective study included 129 patients with pathologically confirmed OETs between May 2018 and December 2024. Clinical variables （age， menopausal status， BMI， HE4， CA125） and sonographic features （conventional US and CEUS） were collected. Tumors were classified as benign， borderline， or malignant according to pathological findings. A random forest algorithm was used to contruct the predictive model. Receiver operating characteristic （ROC） curves were generated tocompare the diagnostic performance of O-RADS， O-RADS+CEUS， O-RADS+ROMA z-score， and O-RADS+ CEUS+ROMA z-score. ResultsAmong the 129 patients， 63 had benign tumors， 25 had borderline tumors， and 41 had malignant tumors. Compared with O-RADS alone， the combined O-RADS+CEUS+ROMA z-score model demonstrated superior diagnostic performance， with an AUC of 0.87 （95% CI： 0.79-0.95） vs. 0.64 （95% CI： 0.51-0.76）， P＜0.001， for differentiating benign from borderline tumors， and 0.90 （95% CI： 0.83-0.97） vs. 0.64 （95% CI： 0.50-0.78）， P＜0.001， for differentiating borderline from malignant tumors. The random forest analysis identified HE4， CA125 and CEUS features as the most important predictors，contributing 11.02， 8.82， 18.1 points， respectively， to the total importance score of 51.94. In the test cohort， the model achieved an overall diagnostic accuracy of 72.97%， with an AUC of 0.89（95%CI：0.77-1.00）， sensitivity of 67.06%， and specificity of 85.01%. ConclusionsO-RADS+CEUS+ROMA z-score multiparametric model demonstrated the highest diagnostic performance for in differentiating benign from borderline and borderline from malignant OETs. HE4， CA125 and CEUS features were identified as key predictive risk factors， supporting the role of this model in improving preoperative risk stratification of OETs.

Objective To analyze dynamic contrast-enhanced MRI (DCE-MRI) radiomic features using machine learning algorithms, and to develop and validate a predictive model for HER-2 status in breast cancer. Methods The DCE-MRI images of 272 treatment-naive female patients with breast cancer between 2020 and 2022 were included in this study. Regions of interest (ROIs) were manually segmented using 3d-Slicer software, and radiomic features were extracted. All patients were randomly divided into training sets or validation sets at a ratio of 4∶1. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature screening on the training set, followed by the development of predictive models using six machine learning algorithms. Internal cross-validation was performed to compare the performance differences between the models. The best-performing model was selected, trained on the training set, and evaluated on the validation set. Evaluation metrics included area under the curve (AUC), sensitivity, specificity, precision, and recall rate. Results The clinical data of patients in the training set and validation set showed no significant differences. Five features were identified by the LASSO algorithm. With these features, six machine learning models were developed on the training set, and their predictive performance was internally cross-validated using the bagging method. XGBoost model had the highest mean AUC (0.696), followed by RF model (0.690); XGBoost model had the highest mean precision (0.756), followed by LR and RF models. Therefore, XGBoost was the optimal model. An HER-2 predictive model was built using the XGBoost algorithm on the training set and applied to the validation set. The AUC, precision, sensitivity, and specificity of the predictive model on the validation set were calculated, and ROC curves, precision-recall curves, calibration curves, and decision-making curves were plotted. Conclusion This study constructed and evaluated different DCE-MRI radiomics-based machine learning models for predicting HER-2 status in breast cancer. Among them, XGBoost algorithm performed the best and has the potential to become a new non-invasive method for preoperative prediction of HER-2 status, providing reliable evidence for personalized clinical diagnosis and treatment.

Objective To investigate indoor radon concentrations and environmental cumulative doses in residential and office units in Shenzhen, and estimate the average annual effective dose, and to provide data for assessing public health risks. Methods Within the 11 administrative districts of Shenzhen (including the Shenzhen-Shanwei Special Cooperation Zone), 17 residential units and 3 office units were randomly selected as monitoring sites in each district. The units selected represented buildings of different ages and various floors on which the units were located. Radon detectors and environmental cumulative dosimeters were deployed for monitoring. Results The indoor radon concentrations in Shenzhen during the two monitoring periods were (36.6 ± 16.5) Bq/m³ and (19.8 ± 15.3) Bq/m³, respectively. The environmental cumulative doses for the two monitoring periods were (0.33 ± 0.07) mSv and (0.25 ± 0.04) mSv, respectively. The estimated average annual effective dose due to indoor radon in Shenzhen was 0.92 mSv. Conclusion All monitored indoor radon concentrations in Shenzhen were below the national standard of China. The indoor radon concentrations exhibited significant regional variations, were higher in spring than in summer, and showed no statistically significant differences across buildings of different ages or units of various floors. The trends in indoor radon concentrations and environmental cumulative doses were highly consistent. The average indoor radon concentration in Shenzhen was lower than both the global and national levels, indicating a low risk of internal radiation exposure from radon.

Objective:To investigate the differences in clinical outcomes of septic children with varying serum zinc levels,and to analyze the relationship between reduced serum zinc levels and organ dysfunction as well as 28-day mortality in septic children.Methods:This study conducted a retrospective analysis of clinical data from pediatric patients diagnosed with sepsis or septic shock in the Department of critical care medicine of the children's Hospital of Soochow University between January 2017 and December 2022.Clinical characteristics,organ dysfunction,and prognosis were compared between two groups:children with low serum zinc levels and those with normal zinc levels.Results:The serum zinc level of septic children within 24 hours of admission was 9.60(5.52,13.80)μmol/L,with 50.54%(94/186)of the children exhibiting low serum zinc levels(<10.07 μmol/L).Compared to the normal serum zinc group,the low serum zinc group had a significantly lower Pediatric Critical Illness Score(PCIS)[(78.71±9.35)vs.(85.12±8.51),P=0.005]and higher 28-day mortality(46.80%vs.14.13%,P<0.001).The low serum zinc group also had a higher proportion of invasive mechanical ventilation(64.89%vs.47.82%,P=0.019),renal replacement therapy(15.59%vs.3.26%,P=0.003),and use of vasoactive drugs(56.38%vs.30.43%,P<0.001).The rate of underlying conditions in the low serum zinc group was significantly higher than that in the normal serum zinc group(57.44%vs.36.95%,P=0.005).Additionally,the low serum zinc group had a higher incidence of disseminated intravascular coagulation(DIC),respiratory failure,acute kidney injury,shock,and multiple organ dysfunction syndrome(MODS)compared to the normal serum zinc group(P<0.05).Serum zinc levels had predictive value for 28-day mortality in septic children(AUC=0.813;95%CI:0.725～0.902;P<0.001).A serum zinc level of less than 6.950 μmol/L predicted the death of septic children with a sensitivity of 0.618 and a specificity of 0.902.Conclusion:Sepsis in children is commonly associated with low serum zinc levels,especially in those with underlying conditions such as hematologic and oncologic disorders.Sepsis patients hypozincemia with a higher incidence of DIC,respiratory failure,acute kidney injury,shock,and MODS.A serum zinc level below 6.95 μmol/L serves as a significant predictor of 28-day mortality in children with severe sepsis.

Objective:To retrospectively seek any correlation between hemoglobin (Hb) levels and post-stroke cognitive impairment (PSCI), and to explore the value of Hb levels in PSCI diagnosis and prediction.Methods:The Mini-mental State Examination (MMSE) was used to sort elderly persons who had suffered a first stroke into a PSCI group showing cognitive impairment and a PSNCI group which was not cognitively impaired. Healthy counterparts undergoing physical examinations were selected into a healthy group. Everyone′s white blood cell count (WBC), neutrophil count (NC), lymphocyte count (LC), monocyte count (MONO), platelet count (PLT) and Hb, total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (LDL-C) were recorded. Monocyte to high density lipoprotein cholesterol ratios (MHRs), neutrophil to lymphocyte ratios (NLRs), and platelet to lymphocyte ratios (PLRs) were calculated. The differences among the three groups were compared seeking to identify risk factors predicting PSCI.Results:There were no significant differences in age, gender, WBC, MONO, PLT, TG or MHR among the three groups, on average. But there were significant differences in NC, LC, Hb, TC, HDL-C, LDL-C, NLR and PLR. Spearman correlation analysis showed that NC, NLR and PLR were negatively correlated with the average MMSE score, while LC, Hb, TC, HDL-C and LDL-C were positively correlated with it. Logistic regression analysis showed that a low level of Hb was an independent risk factor for PSCI for the elderly stroke survivors. The area under the receiver operating characteristics curve (AUC) for Hb was 0.794 for predicting PSCI in elderly stroke patients, with a maximum Youden index of 0.496. The critical value of Hb was estimated as 138.5g/L.Conclusions:Hb levels can significantly predict the cognitive functioning of elderly stroke survivors. An Hb level below 138.5g/L is an independent risk factor for PSCI, while higher Hb levels may indicate cognitive benefits.