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.

Aim To explore the mechanism of clar-ithromycin in treating inflammatory bowel disease(IBD)by inhibiting Kv1.3 channel protein in colonic epithelial cells.Methods A chronic IBD rat model was induced using dextran sulfate sodium(DSS)in vi-vo experiments,with clarithromycin intervention.The physical signs of each group of rats were observed,and the disease activity index(DAI)score and colonic mu-cosal damage index(CMDI)score were calculated.RT-qPCR was used to detect the levels of relevant cyto-kines in colonic tissue of rats.Flow cytometry was em-ployed to detect the relative proportions of immune cells in the peripheral blood and colonic tissue of each group of rats.Lipopolysaccharide(LPS)was used to establish an inflammation model of colon epithelial cells(NCM460)to clarify the inhibitory effect of clar-ithromycin on Kv1.3 channel protein.Results In vi-vo experiments:compared to the model group,the clar-ithromycin intervention group exhibited a reduced de-gree of weight loss(P＜0.01),and a significant de-crease in DAI scores(P＜0.01).There was an in-crease in colon length,a reduction in weight,and a de-crease in CMDI scores(P＜0.05).Levels of TNF-α,IL-1 β,and IL-6 in colon tissue were significantly re-duced(P＜0.01).The numbers of peripheral blood and colonic regulatory T lymphocytes(Th),cytotoxic T lymphocytes(CTL),natural killer cells(NK),B lym-phocytes(B),and dendritic cells(DC)were signifi-cantly decreased(P＜0.05).Clarithromycin reduced the expression of Kv1.3 channel protein in colon tissue(P＜0.05).In vitro experiments:compared to the model group,the clarithromycin group significantly pro-moted the proliferation of NCM460 cells(P＜0.01)and simultaneously significantly reduced the levels of TNF-α and IL-6 in cells(P＜0.05).Clarithromycin also reduced the expression of Kv1.3 channel protein in NCM460 cells(P＜0.05).Conclusions Clar-ithromycin may play an immunomodulatory role by in-hibiting the expression of Kv1.3 channel protein,re-ducing inflammation in the body,and playing a role in the treatment of IBD.

Aim To explore the mechanism of clar-ithromycin in treating inflammatory bowel disease(IBD)by inhibiting Kv1.3 channel protein in colonic epithelial cells.Methods A chronic IBD rat model was induced using dextran sulfate sodium(DSS)in vi-vo experiments,with clarithromycin intervention.The physical signs of each group of rats were observed,and the disease activity index(DAI)score and colonic mu-cosal damage index(CMDI)score were calculated.RT-qPCR was used to detect the levels of relevant cyto-kines in colonic tissue of rats.Flow cytometry was em-ployed to detect the relative proportions of immune cells in the peripheral blood and colonic tissue of each group of rats.Lipopolysaccharide(LPS)was used to establish an inflammation model of colon epithelial cells(NCM460)to clarify the inhibitory effect of clar-ithromycin on Kv1.3 channel protein.Results In vi-vo experiments:compared to the model group,the clar-ithromycin intervention group exhibited a reduced de-gree of weight loss(P＜0.01),and a significant de-crease in DAI scores(P＜0.01).There was an in-crease in colon length,a reduction in weight,and a de-crease in CMDI scores(P＜0.05).Levels of TNF-α,IL-1 β,and IL-6 in colon tissue were significantly re-duced(P＜0.01).The numbers of peripheral blood and colonic regulatory T lymphocytes(Th),cytotoxic T lymphocytes(CTL),natural killer cells(NK),B lym-phocytes(B),and dendritic cells(DC)were signifi-cantly decreased(P＜0.05).Clarithromycin reduced the expression of Kv1.3 channel protein in colon tissue(P＜0.05).In vitro experiments:compared to the model group,the clarithromycin group significantly pro-moted the proliferation of NCM460 cells(P＜0.01)and simultaneously significantly reduced the levels of TNF-α and IL-6 in cells(P＜0.05).Clarithromycin also reduced the expression of Kv1.3 channel protein in NCM460 cells(P＜0.05).Conclusions Clar-ithromycin may play an immunomodulatory role by in-hibiting the expression of Kv1.3 channel protein,re-ducing inflammation in the body,and playing a role in the treatment of IBD.

Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

Aim To investigate the effect of oroxylin A(OA)on apoptosis in Ishikawa cell line of endometrial cancer and the underlying mechanism through the phosphatidylinositol-3 kinase/protein kinase B(PI3K/AKT)signaling pathway.Methods Ishikawa cells were treated with different concentrations of OA(0,4,8,10,12,and 20 μmol·L-1)for 24 h-72 h,the cell viability was detected by CCK-8 assay,apoptosis was detected by flow cytometry,and the protein ex-pression levels of B-cell lymphoma-2(Bcl-2),Bcl-2-associated X protein(Bax),PI3K/AKT,recombinant cytochrome P450 1B1(CYP1B1),and catechol-O-methyltransferase(COMT)were detected by Western blot technique.Results OA inhibited the prolifera-tion of Ishikawa cells in a concentration-and time-de-pendent manner.Compared with the blank control group,the expression of Bax protein increased signifi-cantly,while the expression of Bcl-2 protein decreased significantly with the increase of OA concentration.The expression of COMT protein increased significant-ly,while the expression of CYP1B1 protein decreased significantly.PI3K/AKT:IGF-1(PI3 K agonist)sup-plementation reversed the effect,the expression of COMT protein significantly decreased,and the expres-sion of CYP1B1 protein significantly increased.Con-clusions OA exerts anti-tumor effects in Ishikawa cells of endometrial cancer,which may be related to cell apoptosis mediated by the inhibition of the PI3K/AKT signaling pathway.

With the deepening of molecular biology and cell biology research,the regulatory mechanism of autophagy has been gradually revealed,providing new ideas for the treatment of numerous diseases.Autophagy may be closely related to pathological changes such as apoptosis resistance of fibroblast-like synoviocytes,disturbances in bone metabolic homeostasis,and antigen presentation,the regulation of autophagy homeostasis may be an important approach for the treatment of rheumatoid arthritis(RA).In this paper,we provide a review on the pathological mechanism of autophagy in RA,with a view to providing a theoretical basis for later studies.

Objective:Based on outpatient and emergency visit data,this study employs a staged VaDE model to uncover the heterogeneity structure of residents'healthcare-seeking behaviors and reveal cross-district mobility patterns.Methods:Outpatient and emergency visit records of permanent residents in District A,Shanghai,in 2024 were used.Each individual visit served as the analytical unit.A staged VaDE approach was applied for nonlinear dimensionality reduction and clustering,followed by feature analysis with cross-district visit rates.Results:The proposed model outperformed K-Means and VAE+K-Means in clustering performance,identifying seven typical healthcare-seeking patterns with significant differences in cross-district rates,demographic characteristics,care structures,and disease profiles.Cluster 1 comprised elderly patients with severe conditions and high cross-district rates;Cluster 2,young and middle-aged cross-district commuters;Cluster 3,mild-case or maternity-oriented visits;Cluster 4,infrequent visits by middle-aged and elderly patients;and Clusters 5～7,local chronic disease or low-burden visits.Conclusion:The staged VaDE model demonstrates strong capability in characterizing behavioral heterogeneity and identifying key cross-district mobility types,providing evidence for refined healthcare service management and regional coordination.

Objective:Based on outpatient and emergency visit data,this study employs a staged VaDE model to uncover the heterogeneity structure of residents'healthcare-seeking behaviors and reveal cross-district mobility patterns.Methods:Outpatient and emergency visit records of permanent residents in District A,Shanghai,in 2024 were used.Each individual visit served as the analytical unit.A staged VaDE approach was applied for nonlinear dimensionality reduction and clustering,followed by feature analysis with cross-district visit rates.Results:The proposed model outperformed K-Means and VAE+K-Means in clustering performance,identifying seven typical healthcare-seeking patterns with significant differences in cross-district rates,demographic characteristics,care structures,and disease profiles.Cluster 1 comprised elderly patients with severe conditions and high cross-district rates;Cluster 2,young and middle-aged cross-district commuters;Cluster 3,mild-case or maternity-oriented visits;Cluster 4,infrequent visits by middle-aged and elderly patients;and Clusters 5～7,local chronic disease or low-burden visits.Conclusion:The staged VaDE model demonstrates strong capability in characterizing behavioral heterogeneity and identifying key cross-district mobility types,providing evidence for refined healthcare service management and regional coordination.