Objective: To assess the nutritional supply status of school meals for primary and secondary school students in Henan Province, so as to provide a basis for scientific guidance of school meals. Methods: During 2021-2023, 115 lunch and dinner samples were collected from 25 primary and secondary schools in Zhoukou, Anyang and Luoyang of Henan Province by a direct selection method, and 13 nutrients were determined for each sample. The nutrient supply was evaluated based on Nutrition Guidelines of School Meals and Reference Intake of Dietary Nutrients for Chinese Residents (2023 Edition). Mann-Whitney U test was used to compare the differences of nutritional supply between urban and rural schools. Results: The median values for energy (709.77 kcal,1 kcal=4.18 kJ), fat energy supply ratio (0.27) and carbohydrate energy supply ratio (0.55) in the 66 lunches and dinners from primary school were within the recommended range. The supply of protein (28.39 g) and sodium (1 464.59 mg) was excessive. The median values of zinc (2.62 mg) and dietary fiber (5.19 g) were lower than the reference values. No statistically significant differences were observed in the supply of 13 nutrients between urban and rural primary schools( U = 427.00 -633.00, P > 0.05 ). Among 49 samples from secondary schools, the median value of energy supply (930.02 kcal), carbohydrate energy ratio ( 0.54 ) and fat energy supply ratio(0.25) were within the recommended range; and the median values of protein (38.82 g) and sodium (2 556.80 mg) were higher than the standard; and the median values of calcium (250.32 mg) and vitamin B1 (0.16 mg) were lower than the standard. Additionally, the differences in the level of vitamin B2 ( U =372.00) and zinc ( U =375.00) between the urban and rural secondary schools were statistically significant ( P <0.05). Conclusion Nutrient supply of primary and secondary school meals in three cities of Henan Province is inadequate and imbalanced, and the recipe need to be further optimized and improved.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Fibroblast activation protein inhibitor (FAPI) has been the focus of nuclear medicine since its introduction. With the in-depth study of FAPI tracer, its clinical application in various non-malignant diseases has also been gradually reported. Many studies have confirmed its uptake in a variety of non-malignant diseases, which indicate that FAPI tracers have good application prospects. This article reviews the latest research status and clinical application of radiolabeled FAPIs in cardiovascular diseases, rheumatic immune diseases, immunoglobulin (Ig)G4-related diseases, renal fibrosis and other non-malignant diseases at home and abroad.

Objective: To assess the exposure levels of lead, cadmium, mercury, arsenic and aluminum in the diets of residents in Henan Province, so as to provide the basis for strengthening food safety supervision. Methods: Six sampling points were selected using stratified random sampling method in Henan Province, including Hebi City, Xiangfu District of Kaifeng City, Jianxi District of Luoyang City, Yuzhou City, Baofeng County and Tanghe County. Food samples were collected and processed into mixed samples of 12 major food categories. The levels of lead, cadmium, mercury, arsenic and aluminum in the samples were measured using inductively coupled plasma mass spectrometry (ICP-MS). Dietary consumption information in Henan Province was collected. The dietary exposure risks of lead, cadmium, mercury, arsenic and aluminum were analyzed using the point estimation method and distribution point estimation method, based on the health guidance values of the Joint FAO/WHO Expert Committee on Food Additives and the margin of exposure (MOE) as the assessment criteria. Results: The dietary exposure level of lead among residents in Henan Province was 41.89 μg/d, which was equivalent to 18.62% of its provisional tolerable weekly intake (PTWI), with cereals and vegetables being the main sources; the MOE values of lead among residents aged 2 to <7 years and 7 to <13 years were both less than 1. The dietary exposure level of cadmium was 10.79 μg/d, which was equivalent to 20.55% of the provisional monthly tolerable intake, with cereals and vegetables being the main sources. The dietary exposure level of total mercury was 0.45 μg/d, which was equivalent to 1.25% of its PTWI, with cereals, vegetables, and water and beverage categories being the main sources; the dietary exposure level of methylmercury was 0.04 μg/d, which was equivalent to 0.28% of its PTWI, and it was entirely derived from aquatic products. The dietary exposure level of total arsenic was 26.65 μg/d, which was equivalent to 0.89% of the daily allowable intake, with cereals and vegetables being the main sources; the dietary exposure level of inorganic arsenic was 8.41 μg/d, which was equivalent to 6.23% of its PTWI, with an MOE value of 22.47. The dietary exposure level of aluminum was 8.27 mg/d, which was equivalent to 45.94% of its PTWI, with cereals and tubers being the main sources; the P90 and P97.5 of dietary aluminum exposure among residents aged 2 to <7 years and 7 to <13 years were both greater than PTWI. Conclusion The overall dietary exposure risks of lead, cadmium, mercury, arsenic and aluminum among residents in Henan Province are relatively low.

Objective:To investigate the risk factors of cognitive dysfunction and its relationship with poor prognosis in elderly hypertensive patients with frailty.Methods:A total of 150 elderly hypertensive patients with frailty trea-ted in Eighth People's Hospital of Hebei Province from July 2019 to March 2021 were selected.According to pres-ence of cognitive dysfunction,they were divided into normal cognition group(n=92)and cognitive dysfunction group(n=58),and general data and prognosis condition were collected in two groups.Multivariate Logistic regres-sion analysis was used to analyze the risk factors of cognitive dysfunction in elderly hypertensive patients with frail-ty;Spearman correlation analysis was used to analyze the correlation between cognitive dysfunction degree and prog-nosis.Results:Compared with normal cognition group,cognitive dysfunction group was significantly older and pos-sessed significant higher proportions of diabetes,coronary heart disease,stroke,hyponatremia,multiple medication and malnutrition(P＜0.05 or＜0.01).Multivariate Logistic regression analysis indicated that age ≥75 years,stroke,multiple medication and malnutrition were independent risk factors for cognitive dysfunction in elderly hy-pertensive patients with frailty(OR=2.804～6.434,P＜0.05 or＜0.01).Incidence rate of poor prognosis events in cognitive dysfunction group was significantly higher than that of normal cognition group(51.72％vs.11.96％,P＜0.001).Spearman correlation analysis showed that cognitive dysfunction was significant positively correlated with poor prognosis in these patients(r=0.435,P＜0.001).Conclusion:Age,stroke,multiple medication and malnu-trition are independent risk factors for cognitive dysfunction in elderly hypertensive patients with frailty.Cognitive dysfunction is closely related to poor prognosis in these patients.

Objective To compare the differences in dosimetric parameters between gradient optimization(GO)method and dynamic multi leaf collimator automatic optimization(dMLC-AO)method in craniospinal irradiation(CSI)to provide references for enhancing the feasibility of CSI planning of Monaco treatment planning system.Methods Fifteen patients undergoing CSI at some hospital were retrospectively selected,and a GO plan(enrolled into a GO group)and a dMLC-AO plan(enrolled into a dMLC-AO group)were designed for volumetric modulated arc therapy for each patient by the Monaco treatment planning system.The two groups were compared in terms of the dosimetric parameters of the planning target volume,dose transition zone and organ at risk.SPSS 24.0 statistical software was used for data analysis.Results The dMLC-AO group behaved slightly better than the GO group in D98％ and homogeneity index,with the differences being significant(P＜0.05),while the two groups were not different statistically in D2％,D50％and conformity index(P＞0.05).There were no significant differences between the two groups in radiation doses to the organs at risk except intestinal Dmean(P＞0.05).The GO group gained advantages over the dMLC-AO group in dose distribution conformity at the drop zone,and the target volume length covered by the 36 Gy isodose line for the intermdediate-section plan of the GO group was significantly longer than that of the dMLC-AO group.Conclusion GO method behaves better than dMLC-AO method in controlling effectively the dose drop at the dose transition zone of CSI plan.[Chinese Medical Equipment Journal,2024,45(9):62-66]

The DNA characterization of Cannabis sativa L.has been one of the key directions of anti-drug research at home and abroad.Previous research mainly focused on the identification of cannabis-species and gender differentiation,and have constructed a number of corresponding composite amplification systems.With the rapid development of high-throughput sequencing technology,the whole genome of C.sativa and the sequences of key enzyme genes for its major physicochemical components have been sequenced successively,and intra-species differentiation studies of C.sativa based on specific molecular markers have gradually emerged.However,due to the high variability of cannabis subspecies-and variety-specific molecular markers,relevant foreign studies failed to provide ideal molecular marker support for the identification of intra-specific distinctions of Cannabis sativa in China.Based on this,this paper comprehensively analyzes the current situation and shortcomings of domestic and international research on intra-specific differentiation of C.sativa,and combines the previous research results of this group to elaborate on how to use high-throughput sequencing technology to solve the problem of the lack of intra-specific molecular markers of C.sativa in China.