Objective: To explore the effects of the school based integrated horticulture curriculum intervention on 24 hour activity behaviors among third grade primary school students, so as to provide reference for promoting children s health. Methods: In September 2023, a convenience sampling method was used to select 90 third grade primary school students from a primary school in Changsha. Participants were randomly assigned to an intervention group ( n =45) and a control group ( n =45) using a random number table. From February to May 2024, the intervention group received a 12 week integrated curriculum intervention, consisting of two 60 minute sessions per week and covering horticultural practice, home-school collaborative tasks and nutrition knowledge education. The control group continued with routine labor education courses. The triaxial accelerometer and multi sensor sleep monitoring device were used to objectively measure light intensity physical activity (LPA), moderate to vigorous physical activity (MVPA), screen based sedentary behavior (SSB) and sleep (SLP), durations in both groups. Data were analyzed using generalized estimating equations (GEE) and Mann-Whitney U tests. Results: The time, group and interaction effects of MVPA time and SLP time before and after intervention in two groups of primary school students were not statistically significant (Wald χ 2=1.54, 2.97, 0.85 ; 0.75, 1.05, 0.48), and the group effect of LPA time (Wald χ 2=1.24) and the time and group effects (Wald χ 2=3.02, 1.18 ) were not statistically significant (all P >0.05). There were statistically significant time and interaction effects for LPA time, as well as interaction effect for SSB time in two groups of primary school students before and after intervention (Wald χ 2=4.78, 3.95, 12.60, all P <0.05). After intervention, LPA time of intervention group [152.23(59.15, 245.80)min] was higher than that of control group [120.70(29.90, 201.20)min], and SSB time of intervention group [55.50(30.00, 125.50)min] was lower than that of control group [220.00(60.00, 285.00)min], with statistically significant differences ( Z =-2.46, -4.48, both P <0.05). Conclusion The school horticulture curriculum effectively enhances daily LPA and reduces SSB among third grade primary school students.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveTo explore the dose-effect relationship and safety of high， medium， and low doses of raw Astragali Radix in the modified Huangqi Chifengtang （MHCD） for treating proteinuria in immunoglobulin A （IgA） nephropathy， and to provide scientific evidence for the clinical use of high-dose Astragali Radix in the treatment of proteinuria in IgA nephropathy. MethodsA total of 120 patients with IgA nephropathy， diagnosed with Qi deficiency and blood stasis combined with wind pathogen and heat toxicity， were randomly divided into a control group and three treatment groups. The control group received telmisartan combined with a Chinese medicine placebo， while the treatment groups were given telmisartan combined with MHCD containing different doses of raw Astragali Radix （60， 30， 15 g）. Each group contained 30 patients， and the treatment period was 12 weeks. Changes in 24-hour urinary protein （24 hUTP）， traditional Chinese medicine （TCM） syndrome scores， effective rate， and renal function were observed before and after treatment. Safety was assessed by monitoring liver function and blood routine. ResultsAfter 12 weeks of treatment， 24 hUTP significantly decreased in the high， medium， and low-dose groups， as well as the control group （P<0.05， P<0.01）. The TCM syndrome scores in the high， medium， and low-dose groups also significantly decreased （P<0.01）. Comparisons between groups showed that the 24 hUTP in the high-dose group was significantly lower than in the medium， low-dose， and control groups （P<0.05， P<0.01）， and the 24 hUTP in the medium-dose group was significantly lower than in the control group （P<0.05）. The TCM syndrome scores in the high and medium-dose groups were significantly lower than in the low-dose and control groups （P<0.05， P<0.01）. The total effective rates for proteinuria in the high， medium， low-dose， and control groups were 92.59% （25/27）， 85.19% （23/27）， 60.71% （17/28）， and 57.14% （16/28）， respectively. The effective rates in the high and medium-dose groups were significantly higher than in the low-dose and control groups （χ²=13.185， P<0.05， P<0.01）. The effective rates for TCM syndrome scores in the high， medium， low-dose， and control groups were 88.89% （24/27）， 81.48% （22/27）， 71.43% （20/28）， and 46.43% （13/28）， respectively. The efficacy of TCM syndrome scores in the high and medium-dose groups was significantly higher than in the control group （χ²=14.053， P<0.01）. Compared with pre-treatment values， there was no statistically significant difference in eGFR and serum creatinine in the high and medium-dose groups. However， eGFR significantly decreased in the low-dose and control groups after treatment （P<0.05）， and serum creatinine levels increased significantly in the control group （P<0.05）. No statistically significant differences were observed in urea nitrogen， uric acid， albumin， total cholesterol， triglycerides， liver function， and blood routine before and after treatment in any group. ConclusionThere is a dose-effect relationship in the treatment of IgA nephropathy with high， medium， and low doses of raw Astragali Radix in MHCD. The high-dose group exhibited the best therapeutic effect and good safety profile.

Huopo Xialingtang is a famous classical formula for treating dampness and warmth， which is included in the Catalogue of Ancient Famous Classical Formulas（The First Batch）. In this paper， bibliometric methods was used to collect the literature related to Huopo Xialingtang， and 16 items of related literature were retrieved， involving five medical books， which were used to textual research on the origin， name， composition， drug dosage， preparation method， processing and main treatment symptoms of this formula. The results indicated that Huopo Xialingtang was originated from Yiyuan written by Shi Funan in the Qing dynasty， and and was later named and extended by He Lianchen. The composition of the proposed formula was consistent with the record of Yiyuan， and the origin of each Chinese materia medica was basically clear. Houpo was the dried bark and root bark of Magnolia officinalis， Zexie was the dried tubers of Alisma orientale， Kuxingren was the dried mature seeds of Prunus armeniaca， Doukou was the dried mature fruits of Amomum kravanh， the origin of Tuhuoxiang was consistent with the 2018 edition of Shanghai Standards of Processing Chinese Crud Drugs， and the origins of the remaining Chinese medicines were consistent with the 2020 edition of Chinese Pharmacopoeia. The converted dose of each Chinese medicine was 7.46 g for Agastache rugosa， 3.73 g for Magnoliae Officinalis Cortex， 8.39 g for Pinelliae Rhizoma Praeparatum cum Zingibere et Alumine， 11.19 g for Poria， 11.19 g for Armeniacae Semen Amarum， 14.92 g for Coicis Semen， 2.61 g for Amomi Fructus Rotundus， 5.60 g for Polyporus， 5.60 g for Alismatis Rhizoma， 14.92 g for Tetrapanacis Medulla. Huopo Xialingtang was initially used for the treatment of dampness and warmth at the beginning of the disease， and was later expanded to treat dampness obstruction， dampness-warming dysentery and so on， but always with the dampness-heat in the lungs and spleen as the pathogenesis. In modern times， the clinical application is more extensive， used in digestive， respiratory， endocrine， nervous system and other types of diseases， especially for chronic gastritis， stomach pain and fever. By combing the ancient literature of Huopo Xialingtang， we verified the origin of the formula and determined the key information of the prescription， which can provide literature reference for the clinical application and drug development of this formula.

BACKGROUND:Prolyl hydroxylase domain 2(PHD2)inhibitors can regulate bone metabolism and relieve osteoporosis in ovariectomized rats.cpd17 is a small molecule oral PHD2 inhibitor newly developed by China Pharmaceutical University.It is effective in the treatment of renal anemia with few side effects,but its effect on bone formation and bone resorption is still unclear. OBJECTIVE:To investigate the effects of cpd17 on mouse osteogenic precursor cells. METHODS:Osteogenic precursor cells were treated with cpd17.Alkaline phosphatase activity and extracellular matrix mineralization were measured,and the expression levels of osteogenesis-and osteoclastogenesis-related markers,as well as PHD2 and hypoxia-inducible factor 1α,were detected.After inhibition of the hypoxia-inducible factor 1α pathway using LW6(a hypoxia-inducible factor 1α pathway inhibitor),alkaline phosphatase activity and extracellular matrix mineralization were detected again,as well as the expression levels of osteogenesis-and osteoclastogenesis-related markers,PHD2 and hypoxia-inducible factor 1α. RESULTS AND CONCLUSION:cpd17 significantly enhanced alkaline phosphatase activity and extracellular matrix mineralization,up-regulated the expression of osteogenesis-related markers,down-regulated the expression of osteoclastogenesis-related markers,up-regulated the expression of hypoxia-inducible factor 1α,down-regulate the expression of PHD2.However,cpd17's effects were significantly attenuated by LW6.To conclude,the PHD2 inhibitor cpd17 promotes osteogenic differentiation and inhibits osteoclastic differentiation through activation of the hypoxia-inducible factor 1α signaling pathway.

BACKGROUND:Osteosarcoma has a complex pathogenesis and a poor prognosis.While advancements in medical technology have led to some improvements in the 5-year survival rate,substantial progress in its treatment has not yet been achieved. OBJECTIVE:To screen key molecular markers in osteosarcoma,analyze their relationship with osteosarcoma treatment drugs,and explore the potential disease mechanisms of osteosarcoma at the molecular level. METHODS:GSE99671 and GSE284259(miRNA)datasets were obtained from the Gene Expression Omnibus database.Differential gene expression analysis and Weighted Gene Co-expression Network Analysis(WGCNA)on GSE99671 were performed.Functional enrichment analysis was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes separately for the differentially expressed genes and the module genes with the highest positive correlation to the disease.The intersection of these module genes and differentially expressed genes was taken as key genes.A Protein-Protein Interaction network was constructed,and correlation analysis on the key genes was performed using CytoScape software,and hub genes were identified.Hub genes were externally validated using the GSE28425 dataset and text validation was conducted.The drug sensitivity of hub genes was analyzed using the CellMiner database,with a threshold of absolute value of correlation coefficient|R|>0.3 and P<0.05. RESULTS AND CONCLUSION:(1)Differential gene expression analysis identified 529 differentially expressed genes,comprising 177 upregulated and 352 downregulated genes.WGCNA analysis yielded a total of 592 genes with the highest correlation to osteosarcoma.(2)Gene Ontology enrichment results indicated that the development of osteosarcoma may be associated with extracellular matrix,bone cell differentiation and development,human immune regulation,and collagen synthesis and degradation.Kyoto Encyclopedia of Genes and Genomes enrichment results showed the involvement of pathways such as PI3K-Akt signaling pathway,focal adhesion signaling pathway,and immune response in the onset of osteosarcoma.(3)The intersection analysis revealed a total of 59 key genes.Through Protein-Protein Interaction network analysis,8 hub genes were selected,which were LUM,PLOD1,PLOD2,MMP14,COL11A1,THBS2,LEPRE1,and TGFB1,all of which were upregulated.(4)External validation revealed significantly downregulated miRNAs that regulate the hub genes,with hsa-miR-144-3p and hsa-miR-150-5p showing the most significant downregulation.Text validation results demonstrated that the expression of hub genes was consistent with previous research.(5)Drug sensitivity analysis indicated a negative correlation between the activity of methotrexate,6-mercaptopurine,and pazopanib with the mRNA expression of PLOD1,PLOD2,and MMP14.Moreover,zoledronic acid and lapatinib showed a positive correlation with the mRNA expression of PLOD1,LUM,MMP14,PLOD2,and TGFB1.This suggests that zoledronic acid and lapatinib may be potential therapeutic drugs for osteosarcoma,but further validation is required through additional basic experiments and clinical studies.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.