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 investigate the effects of Tianma Goutengyin on the tumor necrosis factor-α （TNF-α）/signal transducer and activator of transcription 3 （STAT3）/α1-antichymotrypsin C-terminal tail fragment （α1ACT） signaling pathway and A1-type astrocytes in a rat model of vascular dementia. MethodsSeventy-two male Sprague-Dawley rats were randomly divided into six groups （n=12 per group）： Sham-operated group， model group， Tianma Goutengyin high-， medium-， and low-dose groups （5.13， 10.26， and 20.52 g·kg^-1）， and a nimodipine group （8.1 mg·kg^-1）. The vascular dementia model was established by permanent bilateral common carotid artery occlusion， followed by 4 weeks of intervention. Learning and memory ability were evaluated using the novel object recognition test， and behavioral performance was assessed using the forced swimming test. Levels of interleukin-6 （IL-6） and C-C motif chemokine ligand 2 （CCL2） in hippocampal tissue were measured by enzyme-linked immunosorbent assay （ELISA）. Hippocampal neuronal morphology was observed by Nissl staining， and apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL）. Immunohistochemistry was used to detect positive expression of brain-derived neurotrophic factor （BDNF）， glial fibrillary acidic protein （GFAP）， and myelin basic protein （MBP）. Western blot analysis was performed to measure the protein expression levels of TNF-α， TNF receptor 1 （TNFR1）， phosphorylated STAT3 （p-STAT3）， α1ACT， IL-6， complement component 3 （C3）， BDNF， S100 calcium-binding protein A10 （S100A10）， and GFAP in hippocampal tissue. ResultsCompared with the sham-operated group， the model group showed a significantly reduced relative recognition index in the novel object recognition test （P<0.01）， prolonged immobility time and increased immobility frequency in the forced swimming test （P<0.01）. Hippocampal IL-6 and CCL2 levels were significantly increased （P<0.01）. Nissl staining revealed a marked reduction in neuronal number and loss of Nissl bodies （P<0.01）. MBP-positive expression was significantly decreased （P<0.01）， apoptosis was significantly increased （P<0.01）， BDNF-positive expression was significantly reduced （P<0.05）， and GFAP-positive expression was significantly increased （P<0.01）. In addition， the protein expression levels of TNF-α， TNFR1， p-STAT3， α1ACT， IL-6， and C3 were significantly elevated （P<0.01）， while BDNF and S100A10 expression levels were significantly decreased （P<0.01）. Compared with the model group， all Tianma Gouteng yin dose groups exhibited a significant increase in the relative recognition index （P<0.05）， shortened immobility time and reduced immobility frequency （P<0.05， P<0.01）. IL-6 and CCL2 levels were significantly decreased （P<0.01）， neuronal number was significantly increased （P<0.05， P<0.01）， and MBP-positive expression was significantly enhanced （P<0.01）. Apoptosis was significantly reduced （P<0.01）， BDNF-positive expression was significantly increased （P<0.05）， and GFAP-positive expression was significantly decreased （P<0.01）. Moreover， the protein expression levels of TNF-α， TNFR1， p-STAT3， α1ACT， IL-6， and C3 were significantly decreased （P<0.01）， while BDNF and S100A10 protein expression levels were significantly increased （P<0.01）. ConclusionTianma Goutengyin may inhibit A1-type astrocyte activation in rats with vascular dementia through the TNF-α/STAT3/α1ACT signaling pathway， thereby reducing neuronal apoptosis and improving learning and memory function.

ObjectiveTo explore the possible mechanisms of Wenyang Huazhuo Formula （温阳化浊方， WHF） in improving reproductive aging from the perspective of the ovarian mechanical microenvironment. MethodsThe experiment included five groups， 3-month group （20 female mice at 3 months of age）， 6-month group （20 female mice at 6 months of age）， 6-month + WHF group （20 female mice at 5 months of age treated with WHF）， 9-month group （20 female mice at 9 months of age）， and 9-month + WHF group （20 female mice at 8 months of age treated with WHF）. The 6-month + WHF group and 9-month + WHF group were orally administered WHF 41.2 g/（kg·d） once daily for 4 consecutive weeks. The other three groups received no intervention. Reproductive hormone levels were measured by ELISA. HE staining was used to count the numbers of various stages of follicles. Ovarian hyaluronic acid （HA） content and collagen fiber content were measured to evaluate the ovarian mechanical microenvironment. Superovulation was performed to observe the number of eggs obtained， as well as the number of offspring and birth weight to assess fertility. The in vitro fertilization and blastocyst culture of oocytes from female offspring in each group were observed to evaluate the effect of WHF on offspring reproductive potential. ResultsCompared with the 3-month group， the 6-month group and 9-month group showed significantly decreased serum levels of gonadotropin-releasing hormone （GnRH）， follicle-stimulating hormone （FSH）， and luteinizing hormone （LH）， decreased ovarian collagen content， and reduced numbers of primordial and secondary follicles. In contrast， the numbers of primary follicles， antral follicles， and atretic follicles increased. The levels of anti-Müllerian hormone （AMH）， ovarian HA content， and the fertilization rate， cleavage rate， and blastocyst formation rate of oocytes from offspring were significantly lower （P＜0.05）. Compared with the 6-month group， the 6-month + WHF group showed significantly reduced serum levels of GnRH， FSH， and LH， with a significant decrease in primary follicles， antral follicles， and atretic follicles as well as increase of AMH levels， ovarian HA content， number of primordial and secondary follicle， egg count， and offspring birth weight （P＜0.05）. Compared with the 9-month group， the 9-month + WHF group exhibited reduced GnRH， FSH， and collagen fiber content， as well as reduced number of primary follicles， antral follicles， and atretic follicles. However， AMH levels， ovarian HA content， number of primordial and secondary follicle， egg count， offspring numbers， birth weight， fertilization rate， cleavage rate， and blastocyst formation rate of oocytes from offspring all significantly increased （P＜0.05）. ConclusionWHF can significantly improve the ovarian reserve， fertility， and reproductive potential in offspring during reproductive mid-life and late-life stages. Its effect may be related to the remodeling of the mechanical microenvironment of aging ovaries. Moreover， the effect on the mechanical microenvironment remodeling of late-stage ovaries and the improvement of the offspring reproductive potential is more significant.

Rheumatoid Arthritis(RA)is a critical disease that endangers the health of the Chinese pop-ulation.At present,there are still many deficiencies in the diagnosis and treatment of RA in China.The 2024 Chinese Guidelines for the Diagnosis and Treatment of Rheumatoid Arthritis addresses 10 important clinical issues in the diagnosis and treatment of RA,and provides evidence-based recommendations based on the latest domes-tic and international literature,with consideration of the actual situation of RA diagnosis and treatment in China.The guidelines comprehensively cover the diagnosis of RA,imaging examinations,treatment goals,follow-up monitoring,initial treatment plans,treatment adjustments,the application of glucocorticoids,drug tapering,and health education.They will promote the RA diagnosis and treatment levels and improve the prog-nosis of RA patients in China.

Objective To explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.

Objective To investigate the impact of phase Ⅱ cardiac rehabilitation on the rehabilitation needs and physical activity status of patients after coronary heart disease intervention.Methods A total of 90 patients with coronary heart disease who underwent percutaneous coronary intervention(PCI)in the Coronary Heart Disease Center of the hospital from August 2023 to August 2024 were selected as the research subjects.They were subjected to a 12-week standardized phase Ⅱ cardiac rehabilitation training.General data survey forms,cardiac rehabilitation scales,and the International Physical Activity Questionnaire were used for scale surveys to understand the patients' needs before and after rehabilitation and their weekly physical activity en-ergy expenditure.The cardiopulmonary exercise test gold standard,which reflects exercise capacity through three indicators-maximum oxygen uptake(VO2 max),anaerobic threshold(AT),and metabolic equivalents(MET),were used to compare the physical activity status before and after cardiac rehabilitation.Results Compared with before the implementation,after the implementation of cardiac rehabilitation,the autonomy score in-creased(21.36±1.85 vs.16.73±3.28),the process anxiety(12.60±3.87 vs.14.27±2.12)and outcome anxiety scores(2.31±1.76 vs.4.56±3.56)decreased,the level of low-intensity physical activity decreased[(2 711.62±1 487.09)min/week vs.(3 845.97±2 083.71)min/week],the levels of moderate-intensity[(1 314.67±783.54)min/week vs.(686.22±126.79)min/week],high-intensity[(1 861.33±798.27)min/week vs.(112.00±40.77)min/week],and total physical activity increased[(5 887.62±2 843.54)min/week vs.(4 644.19±2 287.16)min/week].The levels of VO2 max[(28.11±14.28)mL·min-1·kg-1 vs.(23.82±12.34)mL·min-1·kg-1],AT[(16.06±5.41)mL·min-1·kg-1 vs.(13.53±4.56)mL·min-1·kg-1],and MET[(6.89±1.59)mL·min-1·kg-1 vs.(5.78±1.21)mL·min-1·kg-1]all in-creased,with statistically significant differences(P＜0.05).Conclusion Phase Ⅱ rehabilitation after PCI can effectively improve patients' physical activity levels.

Polymer magnetic microspheres are a new type of composite functional materials,which have the advantages of stable structure,diverse modification methods,and magnetic response.These characteristics make polymer magnetic microspheres have the advantages of high sensitivity,high-specificity and fast response when detecting logo carriers or modified materials,and shows great application prospects in the fields of biomedicine and medical diagnosis.In this review,the basic properties of polymer magnetic microspheres were summarized,the research progresses of magnetic microspheres in the fields of biomedicine and medical diagnosis were emphatically introduced,and the improvement of preparation methods and development trends in the future were prospected.

Objective:To explore the value of an integrated modeling approach combining radiomics, dosiomics, and clinical factors in the prediction of the locoregional recurrence (LRR) risk after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma (HPSCC), in order to provide supplementary clinical evidence and decision-making basis for personalized treatment for this rare disease characterized by low incidence and poor prognosis.Methods:The clinical images and pathological data were retrospectively enrolled from 76 HPSCC patients treated at the Peking University Cancer Hospital from October 2011 to July 2020. The planning gross tumor volumes (PGTVs) were taken as the volumes of interest (VOIs). A total of 1 316 radiomic and dosiomic features were extracted from the planning CT and dose distribution images. After stability testing, feature dimensionality reduction was achieved using least absolute shrinkage and selection operator (LASSO) regression and principal component analysis (PCA), with radiomic principal components (RPCs) and dosiomic principal components (DPCs) obtained, respectively. Using various combinations of RPCs, DPCs, and clinical variables as predictors, multivariate Cox regression models were developed after 5-fold cross-validation 100 times. The model performance was evaluated based on the Akaike information criterion (AIC) and concordance index (C-index).Results:Using two RPCs and three DPCs selected, dosiomics and radiomic Cox proportional hazards models were constructed, with C-index values of 0.781 and 0.778 and AIC values of 94.44 and 92.27, respectively. The result indicated that one RPC and three DPCs showed significant associations in Cox regression ( P < 0.05). Other prediction models were established by integrating the clinical data of patients with radiomic features, dosiomic features, or both. The prediction result demonstrated that compared to models based on individual factors or dual components, the multi-omics model yielded the highest prediction accuracy (C-index: 0.823, AIC: 84.94). Conclusions:Integrated models that combine radiomic features, dosiomic features, and clinical factors demonstrate great potential for enhancing the accuracy of LRR risk prediction. These models are expected to provide decision-making support for devising personalized treatment strategies and ultimately improve the prognosis of HPSCC patients.

Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.