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.

BACKGROUND:In recent years,deep learning technologies have been increasingly applied in the field of oral medicine,enhancing the efficiency and accuracy of oral imaging analysis and promoting the rapid development of intelligent oral medicine. OBJECTIVE:To elaborate the current research status,advantages,and limitations of deep learning based on oral imaging in the diagnosis and treatment decision-making of oral diseases,as well as future prospects,exploring new directions for the transformation of oral medicine under the backdrop of deep learning technology. METHODS:PubMed was searched for literature related to deep learning in oral medical imaging published from January 2017 to January 2024 with the search terms"deep learning,artificial intelligence,stomatology,oral medical imaging."According to the inclusion criteria,80 papers were finally included for review. RESULTS AND CONCLUSION:(1)Classic deep learning models include artificial neural networks,convolutional neural networks,recurrent neural networks,and generative adversarial networks.Scholars have used these models in competitive or cooperative forms to achieve more efficient interpretation of oral medical images.(2)In the field of oral medicine,the diagnosis of diseases and the formulation of treatment plans largely depend on the interpretation of medical imaging data.Deep learning technology,with its strong image processing capabilities,aids in the diagnosis of diseases such as dental caries,periapical periodontitis,vertical root fractures,periodontal disease,and jaw cysts,as well as preoperative assessments for procedures such as third molar extraction and cervical lymph node dissection,helping clinicians improve the accuracy and efficiency of decision-making.(3)Although deep learning is promising as an important auxiliary tool for the diagnosis and treatment of oral diseases,it still has certain limitations in model technology,safety ethics,and legal regulation.Future research should focus on demonstrating the scalability,robustness,and clinical practicality of deep learning,and finding the best way to integrate automated deep learning decision support systems into routine clinical workflows.

This study aims to comprehensively analyze the material basis of toad visceral oil(hereafter referred to as toad oil), and explore the pharmacological effect of toad oil on atopic dermatitis(AD). Ultra-high performance liquid chromatography-linear ion trap/orbitrap high-resolution mass spectrometry(UHPLC-LTQ-Orbitrap-MS) and gas chromatography-mass spectrometry(GC-MS) were employed to comprehensively identify the chemical components in toad oil. The animal model of AD was prepared by the hapten stimulation method. The modeled animals were respectively administrated with positive drug(0.1% hydrocortisone butyrate cream) and low-and high-doses(1%, 10%) of toad oil by gavage. The effect of toad oil on AD was evaluated with the AD score, ear swelling rate, spleen index, and pathological section results as indicators. A total of 99 components were identified by UHPLC-LTQ-Orbitrap-MS, including 14 bufadienolides, 7 fatty acids, 6 alkaloids, 10 ketones, 18 amides, and other compounds. After methylation of toad oil samples, a total of 20 compounds were identified by GC-MS. Compared with the model group, the low-and high-dose toad oil groups showed declined AD score, ear swelling rate, and spleen index, alleviated skin lesions, and reduced infiltrating mast cells. This study comprehensively analyzes the chemical composition and clarifies the material basis of toad oil. Meanwhile, this study proves that toad oil has a good therapeutic effect on AD and is a reserve resource of traditional Chinese medicine for external use in the treatment of AD.
Animals ; Dermatitis, Atopic/immunology* ; Disease Models, Animal ; Mice ; Male ; Gas Chromatography-Mass Spectrometry ; Humans ; Bufonidae ; Oils/administration & dosage* ; Chromatography, High Pressure Liquid ; Female ; Mice, Inbred BALB C

Colorectal cancer(CRC) is one of the most common malignant tumors worldwide, primarily originating from recurrent inflammatory bowel disease(IBD). Therefore, blocking the inflammation-cancer transformation in the colon has become a focus in the early prevention and treatment of CRC. The inflammation-cancer transformation in the colon involves multiple types of cells and complex pathological processes, including inflammatory responses and tumorigenesis. In this complex pathological process, immune cells(including non-specific and specific immune cells) and non-immune cells(such as tumor cells and fibroblasts) interact with each other, collectively promoting the progression of the disease. In traditional Chinese medicine(TCM), inflammation-cancer transformation in the colon belongs to the categories of dysentery and diarrhea, with the main pathogenesis being cold and heat in complexity. This paper first elaborates on the complex molecular mechanisms involved in the inflammation-cancer transformation process in the colon from the perspectives of inflammation, cancer, and their mutual influences. Subsequently, by comparing the pathogenic characteristics and clinical manifestations between inflammation-cancer transformation and the TCM pathogenesis of cold and heat in complexity, this paper explores the intrinsic connections between the two. Furthermore, based on the correlation between inflammation-cancer transformation in the colon and the TCM pathogenesis, this paper delves into the importance of the interaction between inflammation and cancer. Finally, it summarizes and discusses the clinical and basic research progress in the TCM intervention in the inflammation-cancer transformation process, providing a theoretical basis and treatment strategy for the treatment of CRC with integrated traditional Chinese and Western medicine.
Humans ; Colon/pathology* ; Integrative Medicine ; Animals ; Cold Temperature ; Cell Transformation, Neoplastic/drug effects* ; Medicine, Chinese Traditional ; Hot Temperature ; Inflammation ; Drugs, Chinese Herbal/therapeutic use* ; Colonic Neoplasms/drug therapy*

Objective To evaluate the clinical efficacy of a novel sleep-aid decoction in treating elderly insomnia patients with different traditional Chinese medicine (TCM) constitutional types, and its effects on neurotransmitter and inflammatory factor levels. Methods A total of 200 patients with four different TCM constitutions-peaceful, Qi-deficient, Yin-deficient, and Yang-deficient-were recruited. Peripheral blood neurotransmitter and inflammatory factor levels were measured for variations among insomnia patients across different constitutions. These patients were treated using the novel sleep-aid decoction, the effects of which were evaluated based on changes in neurotransmitters and inflammatory factors. Results Compared to the peaceful constitution group, insomnia patients with Qi-deficient, Yin-deficient, and Yang-deficient constitutions exhibited significantly elevated baseline levels of neurotransmitters (5-HT, GABA) and inflammatory factors (IL-6, TNF-α, IL-1β, CRP). Following the treatment, the Qi-deficient and Yin-deficient groups showed a marked increase in 5-HT levels, restored balance of Glu, GABA, and melatonin, and significant reductions in IL-6 and TNF-α levels. The overall effective rate was 83.5%, with optimal efficacy observed in the Qi-deficient (97.72%) and Yin-deficient (95.34%) groups. Conclusion The novel sleep-aid decoction is effective in treating insomnia in elderly patients, with the best results observed in the Qi-deficient and Yin-deficient constitution groups.
Humans ; Sleep Initiation and Maintenance Disorders/blood* ; Aged ; Male ; Female ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Middle Aged ; Tumor Necrosis Factor-alpha/blood* ; Sleep Aids, Pharmaceutical/therapeutic use* ; Anti-Inflammatory Agents/therapeutic use* ; Interleukin-6/blood* ; Interleukin-1beta/blood* ; Neurotransmitter Agents/blood* ; Aged, 80 and over ; C-Reactive Protein/metabolism*

BACKGROUND: Quantitative flow ratio (QFR) holds significant value in guiding drug-coated balloon (DCB) treatment and enhancing outcomes. However, the predictive capability of post-angioplasty QFR for long-term clinical events in patients with de novo lesions who receive DCB treatment remains uncertain. The aim of this study was to explore the potential significance of post-angioplasty QFR measurements in predicting clinical outcomes in patients underwent DCB treatment for de novo lesions. METHODS: Patients who underwent DCB-only intervention for de novo lesions were enrolled. QFR was conducted after DCB treatment. The patients were then categorized based on post-angioplasty QFR. The primary endpoint was major adverse cardiac events (MACE), encompassing all-cause death, cardiovascular death, nonfatal myocardial infarction, stroke, and target vessel revascularization. RESULTS: A total of 553 patients with 561 lesions were included. The median follow-up period was 505 days, during which 66 (11.8%) MACEs occurred. Based on post-procedural QFR grouping, there were 259 cases in the high QFR group (QFR > 0.93) and 302 cases in the low QFR group (QFR ≤ 0.93). Kaplan-Meier analysis revealed a significantly higher cumulative incidence of MACE in the low QFR group (log-rank P = 0.004). The multivariate Cox proportional hazards model demonstrated a significant inverse correlation between QFR and the occurrence of MACEs (HR = 0.522, 95%CI: 0.289-0.942, P = 0.031). Landmark analysis indicated that high QFR had a significant reducing effect on the cumulative incidence of MACEs within 1 year (log-rank P = 0.016) and 1-5 years (log-rank P = 0.026). CONCLUSIONS In patients who underwent DCB-only treatment for de novo lesions, higher post-procedural QFR values (> 0.93) were identified as an independent protective factor against adverse prognosis.

Objective: To explore the efficacy of vonoprazan fumarate in the treatment of laryngopharyngeal reflux disease（LPRD） evaluated by the Chinese version of the RSS-12 scale. Methods:A total of 100 LPRD patients treated in the otolaryngology-head and neck surgery outpatient clinic of our hospital were randomly divided into two groups（50 cases each）. The observation group was treated with vonoprazan fumarate（20 mg, once daily）, and the control group was treated with esomeprazole enteric-coated capsules（20 mg, twice daily） for 12 weeks. The selected observation indicators in this study included RSI, the Chinese version of RSS-12, and RFS scores before and after treatment. Results:Prior to treatment, there was no statistically significant difference in the Chinese version of RSS-12, RSI, and RFS between the two groups（P>0.05）. After 8 and 12 weeks of antacid treatment, both the Chinese version of RSS-12 and RSI significantly decreased in both group （P<0.05）. The changes in symptoms were particularly noticeable between 0-8 weeks of treatment according to the Chinese version of RSS-12 and RSI. After 12 weeks of treatment, the RFS scores significantly decreased compared to pretreatment levels （P<0.05）. After 12 weeks of antacid treatment, according to the Chinese version of RSS-12, 80% of patients in the observation group showed a good therapeutic response, compared to 64% in the control group; according to RSI, 90% of patients in the observation group showed a good therapeutic response, compared to 84% in the control group. There was no statistically significant difference in the treatment effect between the two groups after 12 weeks of treatment（P>0.05）. Conclusion:Vonoprazan fumarate can significantly improve the symptoms and signs of laryngopharyngeal reflux, and their treatment effect is not inferior to proton pump inhibitors. Compared with RSI, the Chinese version of RSS-12 can serve as a new screening tool for clinical diagnosis of LPRD in China.
Humans ; Sulfonamides/therapeutic use* ; Male ; Pyrroles/therapeutic use* ; Female ; Laryngopharyngeal Reflux/drug therapy* ; Treatment Outcome ; Middle Aged ; Esomeprazole/therapeutic use* ; Proton Pump Inhibitors/therapeutic use* ; Adult

OBJECTIVES: To explore the association of the expression of WW domain-containing ubiquitin E3 ligase 1 (WWP1) with immune infiltration in tumor microenvironment (TME) of ovarian cancer. METHODS: Ovarian cancer patient data from The Cancer Genome Atlas (TCGA) were used to analyze the association of WWP1 expression with patient prognosis. TISCH2 was utilized to analyze the changes in immune cell subtypes in TME of metastatic tumor and after chemotherapy. The impact of WWP1 on immune cell infiltration, somatic copy number alterations of WWP1 and evolution of immune cell subtypes was evaluated using TIMER and TIGER pseudo-time analysis. A deep learning model was used to analyze TCGA pathological images to investigate the effect of WWP1 on TME of ovarian cancer. RNA-seq analysis was conducted to identify the differentially expressed genes in WWP1-overexpressing SKOV3 cells and validate immune infiltration. Multicolor immunofluorescence assay was used to analyze the immune markers in SKOV3 and SKOV3/DDP cell xenografts in nude mice. RESULTS: The patients with high WWP1 expression levels had significantly lower overall survival rate (P=0.0012). High WWP1 expression levels and Stage IV disease were both associated with a poor prognosis (P<0.05). In metastatic ovarian cancer or after chemotherapy, the percentages of malignant tumor cells and tumor-associated fibroblasts increased in the TME, accompanied by elevated WWP1 levels. WWP1 expression level was positively correlated with pro-tumorigenic immunosuppressive cells (r=0.1323-0.3955, P<0.05) and negatively with tumor-inhibiting immune cells (r=-0.1949- -0.1333, P<0.05). Specific copy number alterations of WWP1 also influenced CD8⁺ T cell percentage and neutrophil infiltration levels in the TME. RNA-seq analysis of WWP1-overexpressing SKOV3 cells and immunofluorescence assay of the tumor-bearing mice yielded findings consistent with those of bioinformatics analysis. CONCLUSIONS WWP1 may serve as a prognostic biomarker and a potential target for immune regulation in the TME of ovarian cancer.
Female ; Ovarian Neoplasms/genetics* ; Humans ; Ubiquitin-Protein Ligases/metabolism* ; Tumor Microenvironment/immunology* ; Animals ; Mice ; Cell Line, Tumor ; Mice, Nude ; Prognosis ; Gene Expression Regulation, Neoplastic

OBJECTIVE To study the efficacy of Tianjiang xueshuantong pills in the treatment of coronary heart disease. METHODS In accordance with the common pathogenesis of coronary heart disease， acute myocardial ischemia model， hyperlipidemia model， blood stasis model， and carotid artery thrombosis model were established using Wistar rats or SD rats as the experimental subjects. The effects of Tianjiang xueshuantong pills administered at high， medium， and low doses （0.6， 1.2 and 2.4 g/kg） on hemodynamic parameters and myocardial enzyme markers [lactate dehydrogenase （LDH）， creatine kinase-MB （CK- MB）]， oxidative stress factors [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione （GSH）]， inflammatory cytokines [tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， IL-1β， monocyte chemotactic protein-1 （MCP-1）， intercellular adhesion molecule-1 （ICAM-1）]， myocardial infarction percentage， serum lipid indexes [total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein （LDL）， high-density lipoprotein （HDL）]， platelet aggregation function 话：022-84845240。E-mail：jiangx@tjipr.com [maximum aggregation rate （MAR）]， and thrombus formation indexes [thrombosis time， thrombus mass， thrombus protein content， plasminogen activator inhibitor-1 （PAI-1）， and tissue-type plasminogen activator （t-PA）] were evaluated in the rat models. RESULTS In myocardial ischemia tests， Tianjiang xueshuantong pills significantly reduced the percentage of myocardial infarction and the levels of CK-MB， LDH， MDA， GSH， IL-6， TNF-α， IL- 1β， and MCP-1 in serum （P＜0.05 or P＜0.01）. In hyperlipidemia tests， high dose of Tianjiang xueshuantong pills significantly reduced the serum levels of TC， LDL and significantly increased the level of HDL in rats after 2 weeks and 4 weeks of administration. In blood stasis tests， different doses of Tianjiang xueshuantong pills significantly reduced MAR of rats （P＜0.01）. In artery thrombosis tests， high dose of Tianjiang xueshuantong pills significantly prolonged the time of thrombosis formation （P＜ 0.01）， significantly reduced the weight and protein content of thrombus and the level of PAI-1 in serum （P＜0.01）. CONCLUSIONS Tianjiang xueshuantong pills exert therapeutic effects on coronary heart disease through multi-dimensional synergistic actions， including anti-myocardial ischemia， lipid-lowering， and anti-thrombotic effects.