Objective: To investigate the prevalence of undernutrition and its associated factors among left behind and non left behind primary and secondary school students in the Nutrition Improvement Program for Rural Compulsory Education Students (NIPRCES) areas of central and western China, so as to provide evidence for improving the nutritional status of children and adolescents. Methods: A survey was conducted among 123 782 students selected by random cluster sampling method in grades 3-9 from NIPRCES in central (Hebei, Shanxi, Heilongjiang, Jilin, Anhui, Jiangxi, Henan, Hunan, Hubei, and Hainan) and western (Gansu, Guangxi, Inner Mongolia, Ningxia, Tibet, Shaanxi, Guizhou, Sichuan, Xinjiang, the Xinjiang Production and Construction Corps, Yunnan, Qinghai, and Chongqing) China in 2023. Anthropometric measurements and questionnaires were used to assess nutritional and dietary status. The prevalence of undernutrition was compared between left behind and non left behind students by Chi square test, and associated factors were analyzed by three level Logistic mixed effects model. Results: The prevalence of undernutrition was 8.5% (4 326) in left behind students and 8.1% (5 905) in non left behind students. Three level Logistic mixed effect model analysis showed that whether left behind or non left behind, the undernutrition rates of primary and secondary students in western regions were higher than those of students in central regions [ OR (95% CI )=1.72(1.57-1.87),2.25(2.07- 2.43 )]; the undernutrition risk was lower for those whose fathers had a cultural level of high school or above [ OR (95% CI )=0.69(0.62-0.77),0.90(0.82-0.98)] or junior high school [ OR (95% CI )=0.72(0.66-0.79),0.92(0.85-0.99)] compared to those with primary school or below; picky eating or selective eating increased the risk of undernutrition [ OR (95% CI )=2.36(2.07-2.68),2.28(2.04-2.55)], and primary and secondary school students without nutritional content in health education classes had higher rates of undernutrition [ OR (95% CI )=1.12(1.03-1.23),1.09(1.01-1.17)](all P <0.05). Conclusion The prevalence of undernutrition is slightly higher in left behind primary and secondary students than in non left behind primary and secondary students in central and western NIPRCES areas, with variations across different characteristics.

Childhood simple obesity has become a significant public health issue in China. Modern medicine primarily relies on lifestyle interventions and often suffers from poor long-term compliance， while pharmacological options are limited and associated with potential adverse effects. Traditional Chinese Medicine （TCM） has a long history in the prevention and management of this condition， demonstrating eight distinct advantages， including systematic theoretical foundation， diversified therapeutic approaches， definite therapeutic efficacy， high safety profile， good patient compliance， comprehensive intervention strategies， emphasis on prevention， and stepwise treatment protocols. Additionally， TCM is characterized by six distinctive features： the use of natural medicinal substances， non-invasive external therapies， integration of medicinal dietetics， simple exercise regimens， precise syndrome differentiation， and diverse dosage forms. By combining internal and external treatments， TCM facilitates individualized regimen adjustment and holistic regulation， demonstrating remarkable effects in improving obesity-related metabolic indicators， regulating constitutional imbalance， and promoting healthy behaviors. However， challenges remain， such as inconsistent operational standards， insufficient high-quality clinical evidence， and a gap between basic research and clinical application. Future efforts should focus on accelerating the standardization of TCM diagnosis and treatment， conducting multicenter randomized controlled trials， and fostering interdisciplinary integration， so as to enhance the scientific validity and international recognition of TCM in the prevention and treatment of childhood obesity.

ObjectiveTo clarify the anti-inflammatory and lung-protective effects of Yantiao prescription on lipopolysaccharide （LPS）-induced acute lung injury （ALI）， and to explore the impact of Yantiao prescription on the metabolic pathways of arachidonic acid （AA） in vivo. MethodsThirty male C57BL/6J mice were randomly divided into the following groups based on body weight： normal group， model group， dexamethasone group （2 mg·kg^-1）， low-dose Yantiao prescription group （18 g·kg^-1）， and high-dose Yantiao prescription group （36 g·kg^-1）， with 6 mice in each group. The ALI mouse model was established by intraperitoneal injection of LPS. The treatment groups received oral gavage once a day for 7 consecutive days， and serum and lung tissue were collected at the end of the experiment. The content of pro-inflammatory cytokines tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in serum was detected by enzyme-linked immunosorbent assay （ELISA）. Hematoxylin-eosin （HE） staining was used to assess lung tissue pathology. The wet/dry weight ratio （W/D） and myeloperoxidase （MPO） activity in lung tissue were measured. The content of AA metabolites in serum and lung tissue was measured by liquid chromatography triple quadrupole-mass spectrometry （LC-MS/MS）. ResultsCompared with the conditions in the normal group， the content of serum pro-inflammatory cytokines TNF-α， IL-1β， and IL-6 in the model group was significantly increased （P<0.01）. The alveolar structure in mice was severely damaged， with markedly thickened alveolar walls and extensive inflammatory cell infiltration. The W/D ratio and MPO activity in lung tissue were significantly increased （P<0.01）. The content of AA metabolites， including prostaglandin D₂ （PGD₂）， prostaglandin E₂ （PGE₂）， 11（S）-hydroxy-eicosatetraenoic acid ［11（S）-HETE］， and 5-hydroxy-eicosatetraenoic acid （5-HETE） in serum and lung tissue was significantly increased （P<0.05）， while the content of 11，12-epoxyeicosatrienoic acid （11，12-EET） and 14，15-epoxyeicosatrienoic acid （14，15-EET） in serum was significantly decreased （P<0.01）. Compared with the results in the model group， the content of serum pro-inflammatory cytokines TNF-α， IL-1β， and IL-6 in the dexamethasone group， low-dose Yantiao prescription group， and high-dose Yantiao prescription group was significantly reduced （P<0.05）. Mild thickening of alveolar walls， scattered inflammatory cell infiltration， and relatively intact tissue structure with improved alveolar architecture were observed. The W/D ratio and MPO activity in lung tissue were significantly reduced （P<0.01）. The content of AA metabolites PGD₂， PGE₂， 11（S）-HETE， and 5-HETE in serum from the dexamethasone group was significantly decreased （P<0.05）， while the content of 14，15-EET in serum significantly increased （P<0.01）， and the content of 5-HETE in lung tissue significantly decreased （P<0.01）. In the low-dose and high-dose Yantiao prescription groups， the content of AA metabolites PGD₂， PGE₂， 11（S）-HETE， and 5-HETE in serum and lung tissue was significantly decreased （P<0.05）， while the content of 11，12-EET in both serum and lung tissue was significantly increased （P<0.05）. ConclusionYantiao prescription has significant protective effects against LPS-induced ALI， which are related to its regulation of AA metabolic pathways in vivo.

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

Objective To analyze the implementation of MPOWER tobacco control policies in different regions and populations in China,as well as the impact of perceptions of tobacco control policies on individual smoking behavior and quit intentions,to promote the fairness of policy implementation and protection for vulnerable groups.Methods A multivariable regression model was constructed utilizing raw data from the China Adult Tobacco Survey to analyze disparities in perceived MPOWER policy implementation among various social demographics and its impact on smoking behavior and quitting intentions.Results Regarding protection from tobacco smoke(P),local economic level,urban-rural divide were significantly correlated with awareness of comprehensive smoking bans.For offering help to quit smoking(O),local tobacco industry revenue and individual age were associated with the doctor's advice for quitting.As to the warning about the harm of tobacco(W),economic level,geography and urban-rural disparity were correlated with the visibility of health warnings.About the tobacco advertising,promotion and sponsorship(E),geography was related to the exposure to tobacco advertisements,local tobacco industry revenue was associated with the tobacco promotion.For tobacco taxes(R),education level and age were significantly correlated with tobacco affordability.People who perceived comprehensive smoking bans(OR=0.69,95%CI:0.59-0.81)was associated with less smoking behavior,while people perceiving tobacco promotional activities(OR=2.51,95%CI:2.00-3.17)were more likely to smoke.Additionally,people who perceived comprehensive smoking bans(OR=1.70,95%CI:1.25-2.31)and health warning(OR=2.09,95%CI:1.48-3.01)had higher intention to quit smoking.Conclusion In economically disadvantaged regions and among specific socially vulnerable groups(such as low-income individuals,rural residents,and the elderly)in China,the perception of tobacco control policy implementation is relatively low,the perception of tobacco control policies can influence smoking behavior and quitting intentions.Legislative and enforcement efforts should be increased targeting these groups with lower perceptions of the policies to enhance the fairness of tobacco control measures.

Objective To establish a stable liquid chromatography-tandem mass spectrometry(LC-MS/MS)method for detecting gamma-aminobutyric acid(GABA)in plasma,and to evaluate the value of GABA detection in the diagnosis of sleep disorders.Methods GABA was detected using a UPLC Xevo TQs system.The method was pre-validated and its performance was verified to establish a reference range for healthy individuals.The difference in plasma GABA levels between apparently healthy individuals and patients with sleep disorders was compared.Results We employed deuterated compounds as isotopic internal standards and utilized an Amide chromatographic column for separation.The mobile phase was 0.050%formic acid in water and 90%acetonitrile in water containing 0.175%formic acid and 5 mmol/L ammonium acetate with gradient elution in the column temperature of 35℃.The linear range for the detection of GABA by LC-MS/MS was 0.05-10.00 μmol/L,with a lower limit of quantification of 0.02 μmol/L,the inter-day CV<3.00%and intra assay CV<4.00%,respectively,and the recovery rate was 101.06%-109.02%.The reference ranges for plasma GABA were established by analyzing 300 healthy controls stratified by age:18-34 years(0.08-0.15 μmol/L),35-49 years(0.10-0.20 μmol/L),and≥50 years(0.12-0.23 μmol/L).Then plasma GABA was used as a biomarker for auxiliary diagnosis of sleep disorders in analyzing 221 patients and 300 healthy controls,which revealed that AUC values were 0.510(P=0.850),0.686(P=0.002),and 0.890(P<0.001)in the groups of 18-34 years,35-49 years,and≥50 years,respectively,with optimal cut-off values of 0.09,0.10 and 0.11 μmol/L.Conclusion A reliable LC-MS/MS method for detecting GABA has been established,which can detect plasma GABA levels sensitively and accurately and can be used in assisting the clinical diagnosis of sleep disorders.

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease(CKD).However,existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes.This study presents a smartphone-integrated handheld automated biochemical analyzer(sHABA)designed for point-of-care testing of urinary albumin.The sHABA features a pre-loaded,disposable reagent cassette with re-agents for the albumin assay arranged in the order of their addition within a hose.The smartphone-integrated analyzer can drive the reagents following a preset program,to enable automatic sequential addition.The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L.The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity(95.78％)and specificity(90.16％).This research advances the field by providing an automated detection method for albumin in a portable device,allowing even untrained individuals to monitor CKD in real time at the patient's bedside.In the context of promoting tiered diagnosis and treatment,the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

Acute respiratory distress syndrome(ARDS)is a common respiratory emergency,but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures.Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS,but the application of hydrogen has flammable and explosive safety concerns.Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance,thus improving ARDS in patients and animal models.Coral calcium hydrogenation(CCH)is a new solid molecular hydrogen carrier prepared from coral calcium(CC).Whether and how CCH affects acute lung injury in ARDS re-mains unstudied.In this study,we observed the therapeutic effect of CCH on lipopolysaccharide(LPS)induced acute lung injury in ARDS mice.The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable,demonstrating a significant improvement compared to the untreated ARDS model group.CCH treatment significantly reduced pulmonary hemorrhage and edema,and improved pulmonary function and local microcirculation in ARDS mice.CCH promoted mitochon-drial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2(Trx2),improved lung mitochondrial dysfunction induced by LPS,and reduced oxidative stress damage.The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

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/.

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955