Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.001
Yuan Gao , Zixuan Wu , Boyang Sheng , Fu Zhang , Yong Cheng , Junfeng Yan , Qinghua Peng
Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes. With the development of intelligent diagnosis in traditional Chinese medicine (TCM), artificial intelligence (AI) can improve the accuracy and efficiency of eye diagnosis. However, the research on intelligent eye diagnosis still faces many challenges, including the lack of standardized and precisely labeled data, multi-modal information analysis, and artificial intelligence models for syndrome differentiation. The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelligence. This study elaborates on the three key technologies of AI models in the intelligent application of TCM eye diagnosis, and explores the implications for the research of eye diagnosis intelligence. First, a database concerning eye diagnosis was established based on self-supervised learning so as to solve the issues related to the lack of standardized and precisely labeled data. Next, the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis. Last, the building of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome differentiation models. In summary, research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.
{"title":"The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine","authors":"Yuan Gao , Zixuan Wu , Boyang Sheng , Fu Zhang , Yong Cheng , Junfeng Yan , Qinghua Peng","doi":"10.1016/j.dcmed.2024.09.001","DOIUrl":"10.1016/j.dcmed.2024.09.001","url":null,"abstract":"<div><div>Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes. With the development of intelligent diagnosis in traditional Chinese medicine (TCM), artificial intelligence (AI) can improve the accuracy and efficiency of eye diagnosis. However, the research on intelligent eye diagnosis still faces many challenges, including the lack of standardized and precisely labeled data, multi-modal information analysis, and artificial intelligence models for syndrome differentiation. The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelligence. This study elaborates on the three key technologies of AI models in the intelligent application of TCM eye diagnosis, and explores the implications for the research of eye diagnosis intelligence. First, a database concerning eye diagnosis was established based on self-supervised learning so as to solve the issues related to the lack of standardized and precisely labeled data. Next, the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis. Last, the building of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome differentiation models. In summary, research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 101-107"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.002
Anying Xu , Tianshu Wang , Tao Yang , Han Xiao , Xiaoyu Zhang , Ziyan Wang , Qi Zhang , Xiao Li , Hongcai Shang , Kongfa Hu
Objective
To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes.
Methods
First, TCM full-body inspection data acquisition equipment was employed to collect full-body standing images of healthy people, from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire (CCMQ), and a dataset encompassing labelled constitutions was constructed. Second, heat-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged for the extraction of features such as facial complexion and body shape. In addition, a dual-branch deep network was employed to collect deep features from the full-body standing images. Last, the random forest (RF) algorithm was utilized to learn the extracted multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model.
Results
It was found that the accuracy, precision, and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842, 0.868, and 0.790, respectively. In comparison with the identification models that encompass a single feature, either a single facial complexion feature, a body shape feature, or deep features, the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105, 0.105, and 0.079, the precision increased by 0.164, 0.164, and 0.211, and the F1 score rose by 0.071, 0.071, and 0.084, respectively.
Conclusion
The research findings affirmed the viability of the proposed model, which incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.
{"title":"Constitution identification model in traditional Chinese medicine based on multiple features","authors":"Anying Xu , Tianshu Wang , Tao Yang , Han Xiao , Xiaoyu Zhang , Ziyan Wang , Qi Zhang , Xiao Li , Hongcai Shang , Kongfa Hu","doi":"10.1016/j.dcmed.2024.09.002","DOIUrl":"10.1016/j.dcmed.2024.09.002","url":null,"abstract":"<div><h3>Objective</h3><div>To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes.</div></div><div><h3>Methods</h3><div>First, TCM full-body inspection data acquisition equipment was employed to collect full-body standing images of healthy people, from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire (CCMQ), and a dataset encompassing labelled constitutions was constructed. Second, heat-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged for the extraction of features such as facial complexion and body shape. In addition, a dual-branch deep network was employed to collect deep features from the full-body standing images. Last, the random forest (RF) algorithm was utilized to learn the extracted multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model.</div></div><div><h3>Results</h3><div>It was found that the accuracy, precision, and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842, 0.868, and 0.790, respectively. In comparison with the identification models that encompass a single feature, either a single facial complexion feature, a body shape feature, or deep features, the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105, 0.105, and 0.079, the precision increased by 0.164, 0.164, and 0.211, and the F1 score rose by 0.071, 0.071, and 0.084, respectively.</div></div><div><h3>Conclusion</h3><div>The research findings affirmed the viability of the proposed model, which incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 108-119"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.004
Liqin Zhong , Guojiang Xin , Qinghua Peng , Ji Cui , Lei Zhu , Hao Liang
Objective
To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.
Methods
A total of 1 001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1 007 images of pathological (non-stained) tongue coating from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine with lung cancer, diabetes, and hypertension were collected. The tongue images were randomized into the training, validation, and testing datasets in a 7 : 2 : 1 ratio. A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets. The training period was 90 epochs. The model’s performance was evaluated by its accuracy, loss curve, recall, F1 score, confusion matrix, receiver operating characteristic (ROC) curve, and precision-recall (PR) curve in the tasks of predicting stained tongue coating images in the testing dataset. The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine (TCM).
Results
The training results showed that after 90 epochs, the model presented an excellent classification performance. The loss curve and accuracy were stable, showing no signs of overfitting. The model achieved an accuracy, recall, and F1 score of 92%, 91%, and 92%, respectively. The confusion matrix revealed an accuracy of 92% for the model and 69% for TCM practitioners. The areas under the ROC and PR curves were 0.97 and 0.95, respectively. Conclusion: The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners. This model has the potential to assist doctors in identifying false tongue coating and preventing misdiagnosis.
{"title":"Deep learning-based recognition of stained tongue coating images","authors":"Liqin Zhong , Guojiang Xin , Qinghua Peng , Ji Cui , Lei Zhu , Hao Liang","doi":"10.1016/j.dcmed.2024.09.004","DOIUrl":"10.1016/j.dcmed.2024.09.004","url":null,"abstract":"<div><h3>Objective</h3><div>To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.</div></div><div><h3>Methods</h3><div>A total of 1 001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1 007 images of pathological (non-stained) tongue coating from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine with lung cancer, diabetes, and hypertension were collected. The tongue images were randomized into the training, validation, and testing datasets in a 7 : 2 : 1 ratio. A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets. The training period was 90 epochs. The model’s performance was evaluated by its accuracy, loss curve, recall, F1 score, confusion matrix, receiver operating characteristic (ROC) curve, and precision-recall (PR) curve in the tasks of predicting stained tongue coating images in the testing dataset. The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine (TCM).</div></div><div><h3>Results</h3><div>The training results showed that after 90 epochs, the model presented an excellent classification performance. The loss curve and accuracy were stable, showing no signs of overfitting. The model achieved an accuracy, recall, and F1 score of 92%, 91%, and 92%, respectively. The confusion matrix revealed an accuracy of 92% for the model and 69% for TCM practitioners. The areas under the ROC and PR curves were 0.97 and 0.95, respectively. <strong>Conclusion</strong>: The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners. This model has the potential to assist doctors in identifying false tongue coating and preventing misdiagnosis.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 129-136"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.007
Kai Wang , Ping Dong , Hongzhang Guo
Objective
To explore the differential expression and mechanisms of bone formation-related genes in osteoporosis (OP) leveraging bioinformatics and machine learning methodologies, and to predict the active ingredients of targeted traditional Chinese medicine (TCM) herbs.
Methods
The Gene Expression Omnibus (GEO) and GeneCards databases were employed to conduct a comprehensive screening of genes and disease-associated loci pertinent to the pathogenesis of OP. The R package was utilized as the analytical tool for the identification of differentially expressed genes. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis and support vector machine-recursive feature elimination (SVM-RFE) algorithm were employed in defining the genetic signature specific to OP. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the selected pivotal genes were conducted. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was leveraged to examine the infiltration patterns of immune cells, with Spearman’s rank correlation analysis utilized to assess the relationship between the expression levels of the genes and the presence of immune cells. Coremine Medical Database was used to screen out potential TCM herbs for the treatment of OP. Comparative Toxicogenomics Database (CTD) was employed for forecasting the TCM active ingredients targeting the key genes. AutoDock Vina 1.2.2 and GROMACS 2020 softwares were employed to conclude analysis results, facilitating the exploration of binding affinity and conformational dynamics between the TCM active ingredients and their biological targets.
Results
Ten genes were identified by intersecting the results from the GEO and GeneCards databases. Through the application of LASSO regression and SVM-RFE algorithm, four pivotal genes were selected: coat protein (CP), kallikrein 3 (KLK3), polymerase γ (POLG), and transient receptor potential vanilloid 4 (TRPV4). GO and KEGG pathway enrichment analyses revealed that these trait genes were predominantly engaged in the regulation of defense response activation, maintenance of cellular metal ion balance, and the production of chemokine ligand 5. These genes were notably associated with signaling pathways such as ferroptosis, porphyrin metabolism, and base excision repair. Immune infiltration analysis showed that key genes were highly correlated with immune cells. Macrophage M0, M1, M2, and resting dendritic cell were significantly different between groups, and there were significant differences between different groups (P < 0.05). The interaction counts of resveratrol, curcumin, and quercetin with KLK3 were 7, 3, and 2, respectively. It shows that the interactions of resveratrol, curcumin, and quercetin with KLK3 were
目的利用生物信息学和机器学习方法探索骨质疏松症（OP）中骨形成相关基因的差异表达及其机制，并预测靶向中药（TCM）的有效成分。方法利用基因表达总库（GEO）和基因卡片数据库对与OP发病机制相关的基因和疾病相关位点进行全面筛选。利用 R 软件包作为分析工具来鉴定差异表达基因。采用最小绝对收缩和选择算子（LASSO）逻辑回归分析和支持向量机-递归特征消除（SVM-RFE）算法来定义 OP 的特异性遗传特征。对选定的关键基因进行了基因本体（GO）和京都基因组百科全书（KEGG）通路富集分析。通过估算 RNA 转录本的相对子集（CIBERSORT）算法进行细胞类型鉴定，以检查免疫细胞的浸润模式，并利用斯皮尔曼秩相关分析评估基因表达水平与免疫细胞存在之间的关系。利用 Coremine 医学数据库筛选出治疗 OP 的潜在中草药。比较毒物基因组学数据库（CTD）用于预测针对关键基因的中药活性成分。利用AutoDock Vina 1.2.2和GROMACS 2020软件总结分析结果，有助于探索中药活性成分与其生物靶标之间的结合亲和力和构象动力学。通过应用LASSO回归和SVM-RFE算法，选出了四个关键基因：衣壳蛋白（CP）、allikrein 3（KLK3）、聚合酶γ（POLG）和瞬时受体位点类香草素4（TRPV4）。GO 和 KEGG 通路富集分析表明，这些性状基因主要参与调控防御反应激活、维持细胞金属离子平衡和产生趋化因子配体 5。这些基因明显与信号通路有关，如铁蛋白沉积、卟啉代谢和碱基切除修复。免疫浸润分析表明，关键基因与免疫细胞高度相关。巨噬细胞M0、M1、M2和静息树突状细胞在组间有显著差异，不同组间差异显著（P <0.05）。白藜芦醇、姜黄素和槲皮素与 KLK3 的相互作用次数分别为 7、3 和 2。这表明白藜芦醇、姜黄素和槲皮素与 KLK3 的相互作用是实质性的。结论包括 CP、KLK3、POLG 和 TRPV4 在内的关键基因具有显著的预后价值，在 OP 的诊断评估中发挥着重要作用。中药中的天然化合物白藜芦醇、姜黄素和槲皮素显示出有效调节骨形成基因 KLK3 的潜力。这项研究为解释 OP 的发病机制和开发临床药物提供了科学依据。
{"title":"Integrative analysis of bone-formation associated genes and immune cell infiltration in osteoporosis, and the prediction of active ingredients in targeted traditional Chinese medicine","authors":"Kai Wang , Ping Dong , Hongzhang Guo","doi":"10.1016/j.dcmed.2024.09.007","DOIUrl":"10.1016/j.dcmed.2024.09.007","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the differential expression and mechanisms of bone formation-related genes in osteoporosis (OP) leveraging bioinformatics and machine learning methodologies, and to predict the active ingredients of targeted traditional Chinese medicine (TCM) herbs.</div></div><div><h3>Methods</h3><div>The Gene Expression Omnibus (GEO) and GeneCards databases were employed to conduct a comprehensive screening of genes and disease-associated loci pertinent to the pathogenesis of OP. The R package was utilized as the analytical tool for the identification of differentially expressed genes. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis and support vector machine-recursive feature elimination (SVM-RFE) algorithm were employed in defining the genetic signature specific to OP. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the selected pivotal genes were conducted. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was leveraged to examine the infiltration patterns of immune cells, with Spearman’s rank correlation analysis utilized to assess the relationship between the expression levels of the genes and the presence of immune cells. Coremine Medical Database was used to screen out potential TCM herbs for the treatment of OP. Comparative Toxicogenomics Database (CTD) was employed for forecasting the TCM active ingredients targeting the key genes. AutoDock Vina 1.2.2 and GROMACS 2020 softwares were employed to conclude analysis results, facilitating the exploration of binding affinity and conformational dynamics between the TCM active ingredients and their biological targets.</div></div><div><h3>Results</h3><div>Ten genes were identified by intersecting the results from the GEO and GeneCards databases. Through the application of LASSO regression and SVM-RFE algorithm, four pivotal genes were selected: coat protein (<em>CP</em>), kallikrein 3 (<em>KLK3</em>), polymerase <em>γ</em> (<em>POLG</em>), and transient receptor potential vanilloid 4 (<em>TRPV4</em>). GO and KEGG pathway enrichment analyses revealed that these trait genes were predominantly engaged in the regulation of defense response activation, maintenance of cellular metal ion balance, and the production of chemokine ligand 5. These genes were notably associated with signaling pathways such as ferroptosis, porphyrin metabolism, and base excision repair. Immune infiltration analysis showed that key genes were highly correlated with immune cells. Macrophage M0, M1, M2, and resting dendritic cell were significantly different between groups, and there were significant differences between different groups (<em>P</em> < 0.05). The interaction counts of resveratrol, curcumin, and quercetin with <em>KLK3</em> were 7, 3, and 2, respectively. It shows that the interactions of resveratrol, curcumin, and quercetin with <em>KLK3</em> were ","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 160-170"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.009
Yuzhen Chen , Yongzhi Zhao , Yiwen Zhang , Fang Chen , Muhammad Iqbal Choudhary , Xinmin Liu , Ning Jiang
Objective
To investigate the antidepressant effects of Yuanzhi (Polygalae Radix, PR) aqueous extract on chronic unpredictable mild stress (CUMS)-induced depression rat models and the underlying mechanisms.
Methods
A total of 40 male Sprague Dawley (SD) rats were randomly divided into control, model, low dose of PR (PR-L, 0.5 g/kg), high dose of PR (PR-H, 1 g/kg), and fluoxetine (10 mg/kg) groups, with 8 rats in each group. Except for the rats in control group, those in the other four groups underwent CUMS-induced depression modeling. PR and fluoxetine were administered intragastrically once daily, 30 min prior to the CUMS procedure, for 14 consecutive days until the behavioral tests were performed. After CUMS modeling, the sucrose preference test (SPT), open field test (OFT), novelty-suppressed feeding test (NSFT), forced swim test (FST), and tail suspension test (TST) were employed to assess the pharmacological effects of PR on the mitigation of depressive-like behaviors in rat models. Additionally, the enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the serum levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β in the rats. Western blot analysis was also conducted to evaluate the protein expression levels of nuclear factor kappa-B (NF-κB), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3), apoptosis-associated speck-like protein containing caspase recruitment domain (ASC), and caspase-1 in the hippocampal tissues of the rats. Immunofluorescence staining was performed to observe the morphological changes in ionized calcium-binding adapter molecule 1 positive (Iba-1+) cells in the dentate gyrus (DG) of rats with CUMS-induced depression.
Results
(i) Treatment with PR-H and fluoxetine resulted in significant enhancements in both the total distance and time the rats moved during tests (P < 0.01 and P < 0.05, respectively). Post-administration of PR-H and fluoxetine also led to statistically significant increase in sucrose preference among rats (P < 0.05). Besides, PR-L, PR-H, and fluoxetine treatment markedly decreased the latency of ingestion (P < 0.05, P < 0.05, and P < 0.01, respectively). As observed from the FST, PR-L, PR-H, and fluoxetine presented antidepressant effects on rats with CUMS-induced depression, leading to the reduction in time of their immobility (P < 0.05, P < 0.01, and P < 0.01, respectively). The results of TST indicated reduced immobility time in rats receiving PR-H and fluoxetine treatment as well (P < 0.01). (ii) Rats in model group showed an increase in the levels of Iba-1+ microglia in their left and right brains in comparison w
{"title":"Antidepressant effects of Yuanzhi (Polygalae Radix) extract on chronic unpredictable mild stress-induced depression in rats: modulation of the NLRP3 inflammasome and NF-κB pathway","authors":"Yuzhen Chen , Yongzhi Zhao , Yiwen Zhang , Fang Chen , Muhammad Iqbal Choudhary , Xinmin Liu , Ning Jiang","doi":"10.1016/j.dcmed.2024.09.009","DOIUrl":"10.1016/j.dcmed.2024.09.009","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the antidepressant effects of Yuanzhi (Polygalae Radix, PR) aqueous extract on chronic unpredictable mild stress (CUMS)-induced depression rat models and the underlying mechanisms.</div></div><div><h3>Methods</h3><div>A total of 40 male Sprague Dawley (SD) rats were randomly divided into control, model, low dose of PR (PR-L, 0.5 g/kg), high dose of PR (PR-H, 1 g/kg), and fluoxetine (10 mg/kg) groups, with 8 rats in each group. Except for the rats in control group, those in the other four groups underwent CUMS-induced depression modeling. PR and fluoxetine were administered intragastrically once daily, 30 min prior to the CUMS procedure, for 14 consecutive days until the behavioral tests were performed. After CUMS modeling, the sucrose preference test (SPT), open field test (OFT), novelty-suppressed feeding test (NSFT), forced swim test (FST), and tail suspension test (TST) were employed to assess the pharmacological effects of PR on the mitigation of depressive-like behaviors in rat models. Additionally, the enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the serum levels of tumor necrosis factor (TNF)-<em>α</em>, interleukin (IL)-6, and IL-1<em>β</em> in the rats. Western blot analysis was also conducted to evaluate the protein expression levels of nuclear factor kappa-B (NF-<em>κ</em>B), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3), apoptosis-associated speck-like protein containing caspase recruitment domain (ASC), and caspase-1 in the hippocampal tissues of the rats. Immunofluorescence staining was performed to observe the morphological changes in ionized calcium-binding adapter molecule 1 positive (Iba-1<sup>+</sup>) cells in the dentate gyrus (DG) of rats with CUMS-induced depression.</div></div><div><h3>Results</h3><div>(i) Treatment with PR-H and fluoxetine resulted in significant enhancements in both the total distance and time the rats moved during tests (<em>P</em> < 0.01 and <em>P</em> < 0.05, respectively). Post-administration of PR-H and fluoxetine also led to statistically significant increase in sucrose preference among rats (<em>P</em> < 0.05). Besides, PR-L, PR-H, and fluoxetine treatment markedly decreased the latency of ingestion (<em>P</em> < 0.05, <em>P</em> < 0.05, and <em>P</em> < 0.01, respectively). As observed from the FST, PR-L, PR-H, and fluoxetine presented antidepressant effects on rats with CUMS-induced depression, leading to the reduction in time of their immobility (<em>P</em> < 0.05, <em>P</em> < 0.01, and <em>P</em> < 0.01, respectively). The results of TST indicated reduced immobility time in rats receiving PR-H and fluoxetine treatment as well (<em>P</em> < 0.01). (ii) Rats in model group showed an increase in the levels of Iba-1<sup>+</sup> microglia in their left and right brains in comparison w","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 184-194"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.dcmed.2024.09.003
Yulin Shi , Jiayi Liu , Yi Chun , Lingshuang Liu , Jiatuo Xu
Objective
To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.
Methods
From July 1, 2020 to March 31, 2022, clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, respectively. We obtained tongue images from patients with benign lung nodules and lung cancer using the TFDA-1 digital tongue diagnosis instrument, and analyzed these images with the TDAS V2.0 software. The extracted indicators included color space parameters in the Lab system for both the tongue body (TB) and tongue coating (TC) (TB/TC-L, TB/TC-a, and TB/TC-b), textural parameters [TB/TC-contrast (CON), TB/TC-angular second moment (ASM), TB/TC-entropy (ENT), and TB/TC-MEAN], as well as TC parameters (perAll and perPart). The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis, and the overall correlation was analyzed using canonical correlation analysis (CCA).
Results
Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values. Simple correlation analysis indicated that the correlation of TB-L with TC-L, TB-b with TC-b, and TB-b with perAll in lung cancer group was higher than that in benign nodules group. Moreover, the correlation of TB-a with TC-a, TB-a with perAll, and the texture parameters of the TB (TB-CON, TB-ASM, TB-ENT, and TB-MEAN) with the texture parameters of the TC (TC-CON, TC-ASM, TC-ENT, and TC-MEAN) in benign nodules group was higher than lung cancer group. CCA further demonstrated a strong correlation between the TB and TC parameters in lung cancer group, with the first and second pairs of typical variables in benign nodules and lung cancer groups indicating correlation coefficients of 0.918 and 0.817 (P < 0.05), and 0.940 and 0.822 (P < 0.05), respectively.
Conclusion
Benign lung nodules and lung cancer patients exhibited differences in correlation in the L, a, and b values of the TB and TC, as well as the perAll value of the TC, and the texture parameters (TB/TC-CON, TB/TC-ASM, TB/TC-ENT, and TB/TC-MEAN) between the TB and TC. Additionally, there were differences in the overall correlation of the TB and TC between the two groups. Objective tongue diagnosis indicators can effectively assist in the diagnosis of benign lung nodules and lung cancer, thereby providing a scientific basis for the early detection, diagnosis, and treatment of lung cancer.
{"title":"Tongue image feature correlation analysis in benign lung nodules and lung cancer","authors":"Yulin Shi , Jiayi Liu , Yi Chun , Lingshuang Liu , Jiatuo Xu","doi":"10.1016/j.dcmed.2024.09.003","DOIUrl":"10.1016/j.dcmed.2024.09.003","url":null,"abstract":"<div><h3>Objective</h3><div>To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.</div></div><div><h3>Methods</h3><div>From July 1, 2020 to March 31, 2022, clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, respectively. We obtained tongue images from patients with benign lung nodules and lung cancer using the TFDA-1 digital tongue diagnosis instrument, and analyzed these images with the TDAS V2.0 software. The extracted indicators included color space parameters in the Lab system for both the tongue body (TB) and tongue coating (TC) (TB/TC-L, TB/TC-a, and TB/TC-b), textural parameters [TB/TC-contrast (CON), TB/TC-angular second moment (ASM), TB/TC-entropy (ENT), and TB/TC-MEAN], as well as TC parameters (perAll and perPart). The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis, and the overall correlation was analyzed using canonical correlation analysis (CCA).</div></div><div><h3>Results</h3><div>Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values. Simple correlation analysis indicated that the correlation of TB-L with TC-L, TB-b with TC-b, and TB-b with perAll in lung cancer group was higher than that in benign nodules group. Moreover, the correlation of TB-a with TC-a, TB-a with perAll, and the texture parameters of the TB (TB-CON, TB-ASM, TB-ENT, and TB-MEAN) with the texture parameters of the TC (TC-CON, TC-ASM, TC-ENT, and TC-MEAN) in benign nodules group was higher than lung cancer group. CCA further demonstrated a strong correlation between the TB and TC parameters in lung cancer group, with the first and second pairs of typical variables in benign nodules and lung cancer groups indicating correlation coefficients of 0.918 and 0.817 (<em>P</em> < 0.05), and 0.940 and 0.822 (<em>P</em> < 0.05), respectively.</div></div><div><h3>Conclusion</h3><div>Benign lung nodules and lung cancer patients exhibited differences in correlation in the L, a, and b values of the TB and TC, as well as the perAll value of the TC, and the texture parameters (TB/TC-CON, TB/TC-ASM, TB/TC-ENT, and TB/TC-MEAN) between the TB and TC. Additionally, there were differences in the overall correlation of the TB and TC between the two groups. Objective tongue diagnosis indicators can effectively assist in the diagnosis of benign lung nodules and lung cancer, thereby providing a scientific basis for the early detection, diagnosis, and treatment of lung cancer.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 2","pages":"Pages 120-128"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1016/j.dcmed.2024.04.006
Ran Li , Gao Ren , Junfeng Yan , Beiji Zou , Qingping Liu
Objective
To construct a traditional Chinese medicine (TCM) knowledge base using knowledge graph based on deep learning methods, and to explore the application of joint models in intelligent question answering systems for TCM.
Methods
Textbooks Prescriptions of Chinese Materia Medica and Chinese Materia Medica were applied to construct a comprehensive knowledge graph serving as the foundation for the intelligent question answering system. In the study, a BERT+Slot-Gated (BSG) deep learning model was applied for the identification of TCM entities and question intentions presented by users in their questions. Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user. The Flask framework and BSG model were utilized to develop the intelligent question answering system of TCM.
Results
A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn. In the question answering test assisted by a question corpus, the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9, and the accuracy rate for identifying intentions was 99.75%. This indicates that the system is both feasible and practical. Users can interact with the system through the WeChat Official Account platform.
Conclusion
The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension, indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering systems in TCM.
{"title":"Intelligent question answering system for traditional Chinese medicine based on BSG deep learning model: taking prescription and Chinese materia medica as examples","authors":"Ran Li , Gao Ren , Junfeng Yan , Beiji Zou , Qingping Liu","doi":"10.1016/j.dcmed.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.dcmed.2024.04.006","url":null,"abstract":"<div><h3>Objective</h3><p>To construct a traditional Chinese medicine (TCM) knowledge base using knowledge graph based on deep learning methods, and to explore the application of joint models in intelligent question answering systems for TCM.</p></div><div><h3>Methods</h3><p>Textbooks <em>Prescriptions of Chinese Materia Medica</em> and <em>Chinese Materia Medica</em> were applied to construct a comprehensive knowledge graph serving as the foundation for the intelligent question answering system. In the study, a BERT+Slot-Gated (BSG) deep learning model was applied for the identification of TCM entities and question intentions presented by users in their questions. Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user. The Flask framework and BSG model were utilized to develop the intelligent question answering system of TCM.</p></div><div><h3>Results</h3><p>A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn. In the question answering test assisted by a question corpus, the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9, and the accuracy rate for identifying intentions was 99.75%. This indicates that the system is both feasible and practical. Users can interact with the system through the WeChat Official Account platform.</p></div><div><h3>Conclusion</h3><p>The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension, indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering systems in TCM.</p></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 1","pages":"Pages 47-55"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589377724000247/pdfft?md5=a22431df2b6bfa13b9ef1cab13e2e4d5&pid=1-s2.0-S2589377724000247-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the theories of pulse disgnosis in traditional Chinese medicine (TCM), it is emphasized that pulse manifestations at the radial artery within the wrist (called Cunkou) signify the physiological and pathological conditions of different internal organs in the human body. However, different opinions exist among researchers about the objectiveness of the pulse diagnosis technique. Some researchers mentioned that no significant differences were observed in pulse manifestations at various Cunkou areas, hence there might be some difficulty in evaluating the status of different organs through checking pulse manifestations at Cunkou. This research aims to analyze the pulse response at Cunkou from the aspect of the characteristics of tactile sensing, thus to give a preliminary explanation to the above question.
Methods
This research utilized the Weber-Fechner law to model the tactile sensing as a dynamic low-pass signal filter with varying bandwidths under different compression levels during pulse diagnosis. The model was applied to analyzing the clinical data collected previously by our group. The arterial pressures measured invasively with equipment from 14 patients with aorta coarctation were processed to simulate different pulse manifestations at Cun, Guan, and Chi positions of Cunkou when different compression levels were applied.
Results
Due to the characteristics of tactile sensing, significant variations were observed in pulse manifestations at different pulse-depths under the application of changing compression levels; while no such changes in pulse manifestations were observed from the employment of transducer only, without tactile sensing involved. The results explained why different understandings on pulse manifestations were formed between direct pulse-taking technique in TCM and modern sphygmography using transducers. The features of pulse manifestations at Cunkou, using direct pulse-taking technique but at different depths, superficial, middle, and deep positions, respectively, predicted by the developed tactile sensing model were in line with those described in TCM pulse theories.
Conclusion
Based on the developed tactile sensing model, this study preliminarily explains the phenomenon that pulse manifestation at Cunkou changes in response to the compression force applied during TCM pulse-taking. Integrating the tactile sensing model with the study of TCM pulse diagnosis opens a new chapter for visualizing and quantitatively interpreting pulse manifestations. This not only expands the scope of pulse diagnosis study effectively, but also provide a scientific basis for further technical upgrading and optimization of existing pulse diagnosis equipment.
{"title":"Differences in pulse manifestations at Cunkou based on simplified modeling of tactile sensing","authors":"S.H.I. Yubing , Y.A.N.G. Hongyi , Y.E.O. Joon Hock","doi":"10.1016/j.dcmed.2024.04.004","DOIUrl":"https://doi.org/10.1016/j.dcmed.2024.04.004","url":null,"abstract":"<div><h3>Objective</h3><p>In the theories of pulse disgnosis in traditional Chinese medicine (TCM), it is emphasized that pulse manifestations at the radial artery within the wrist (called Cunkou) signify the physiological and pathological conditions of different internal organs in the human body. However, different opinions exist among researchers about the objectiveness of the pulse diagnosis technique. Some researchers mentioned that no significant differences were observed in pulse manifestations at various Cunkou areas, hence there might be some difficulty in evaluating the status of different organs through checking pulse manifestations at Cunkou. This research aims to analyze the pulse response at Cunkou from the aspect of the characteristics of tactile sensing, thus to give a preliminary explanation to the above question.</p></div><div><h3>Methods</h3><p>This research utilized the Weber-Fechner law to model the tactile sensing as a dynamic low-pass signal filter with varying bandwidths under different compression levels during pulse diagnosis. The model was applied to analyzing the clinical data collected previously by our group. The arterial pressures measured invasively with equipment from 14 patients with aorta coarctation were processed to simulate different pulse manifestations at Cun, Guan, and Chi positions of Cunkou when different compression levels were applied.</p></div><div><h3>Results</h3><p>Due to the characteristics of tactile sensing, significant variations were observed in pulse manifestations at different pulse-depths under the application of changing compression levels; while no such changes in pulse manifestations were observed from the employment of transducer only, without tactile sensing involved. The results explained why different understandings on pulse manifestations were formed between direct pulse-taking technique in TCM and modern sphygmography using transducers. The features of pulse manifestations at Cunkou, using direct pulse-taking technique but at different depths, superficial, middle, and deep positions, respectively, predicted by the developed tactile sensing model were in line with those described in TCM pulse theories.</p></div><div><h3>Conclusion</h3><p>Based on the developed tactile sensing model, this study preliminarily explains the phenomenon that pulse manifestation at Cunkou changes in response to the compression force applied during TCM pulse-taking. Integrating the tactile sensing model with the study of TCM pulse diagnosis opens a new chapter for visualizing and quantitatively interpreting pulse manifestations. This not only expands the scope of pulse diagnosis study effectively, but also provide a scientific basis for further technical upgrading and optimization of existing pulse diagnosis equipment.</p></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 1","pages":"Pages 29-39"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589377724000223/pdfft?md5=b7c42f620580affd1cad2c5b27fa558c&pid=1-s2.0-S2589377724000223-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1016/j.dcmed.2024.04.009
Yarong Ding , Chenlei Xie , Shuihua Feng , Zhonghang Yuan , Wei Wang , Mulin Liu , Zhongzhi Zhou , Li Chen
Objective
To explore the mechanism of Wenyang Shengji Ointment (温阳生肌膏, WYSJO) in the treatment of diabetic wounds from the perspective of network pharmacology, and to verify it by animal experiments.
Methods
The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and related literature were used to screen active compounds in WYSJO and their corresponding targets. GeneCards, Online Mendelian Inheritance in Man (OMIM), DrugBank, PharmGkb, and Therapeutic Target Database (TTD) databases were employed to identify the targets associated with diabetic wounds. Cytoscape 3.9.0 was used to map the active ingredients in WYSJO, which was the diabetic wound target network. Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) platform was utilized to construct protein-protein interaction (PPI) network. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed to identify signaling pathways between WYSJO and diabetic wounds. AutoDock 1.5.6 was used for molecular docking of core components in WYSJO to their targets. Eighteen rats were randomly divided into control, model, and WYSJO groups (n = 6). The model and WYSJO groups were used to prepare the model of refractory wounds in diabetes rats. The wound healing was observed on day 0, 5, 9, and 14 after treatment, and the wound tissue morphology was observed by hematoxylin-eosin (HE) staining. The expression levels of core genes were detected by quantitative real-time polymerase chain reaction (qPCR).
Results
A total of 76 active compounds in WYSJO, 206 WYSJO drug targets, 3 797 diabetic wound targets, and 167 diabetic wound associated WYSJO targets were screened out through network pharmacology. With the use of WYSJO-diabetic wound target network, core targets of seven active compounds encompassing quercetin, daidzein, kaempferol, rhamnetin, rhamnocitrin, strictosamide, and diisobutyl phthalate (DIBP) in WYSJO were found. GO enrichment analysis showed that the treatment of diabetes wounds with WYSJO may involve lipopolysaccharide, bacteria-derived molecules, metal ions, foreign stimuli, chemical stress, nutrient level, hypoxia, and oxidative stress in the biological processes. KEGG enrichment analysis showed that the treatment of diabetes wounds with WYSJO may involve advanced glycation end products (AGE-RAGE), p53, interleukin (IL)-17, tumor necrosis factor (TNF), hypoxia inducible factor-1 (HIF-1), apoptosis, lipid, atherosclerosis, etc. The results of animal experiments showed that WYSJO could significantly accelerate the healing process of diabetic wounds (P < 0.05), alleviate inflammatory response, promote the growth of granulation tissues, and down-regulate the expression levels of eight core genes [histone crotonyltransferase p300 (EP300), protoc gene-oncogene c-Jun (JUN), myelocytomatosis (MYC),
{"title":"Mechanism of Wenyang Shengji Ointment in treating diabetic wounds based on network pharmacology and animal experiments","authors":"Yarong Ding , Chenlei Xie , Shuihua Feng , Zhonghang Yuan , Wei Wang , Mulin Liu , Zhongzhi Zhou , Li Chen","doi":"10.1016/j.dcmed.2024.04.009","DOIUrl":"https://doi.org/10.1016/j.dcmed.2024.04.009","url":null,"abstract":"<div><h3>Objective</h3><p>To explore the mechanism of Wenyang Shengji Ointment (温阳生肌膏, WYSJO) in the treatment of diabetic wounds from the perspective of network pharmacology, and to verify it by animal experiments.</p></div><div><h3>Methods</h3><p>The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and related literature were used to screen active compounds in WYSJO and their corresponding targets. GeneCards, Online Mendelian Inheritance in Man (OMIM), DrugBank, PharmGkb, and Therapeutic Target Database (TTD) databases were employed to identify the targets associated with diabetic wounds. Cytoscape 3.9.0 was used to map the active ingredients in WYSJO, which was the diabetic wound target network. Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) platform was utilized to construct protein-protein interaction (PPI) network. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed to identify signaling pathways between WYSJO and diabetic wounds. AutoDock 1.5.6 was used for molecular docking of core components in WYSJO to their targets. Eighteen rats were randomly divided into control, model, and WYSJO groups (<em>n</em> = 6). The model and WYSJO groups were used to prepare the model of refractory wounds in diabetes rats. The wound healing was observed on day 0, 5, 9, and 14 after treatment, and the wound tissue morphology was observed by hematoxylin-eosin (HE) staining. The expression levels of core genes were detected by quantitative real-time polymerase chain reaction (qPCR).</p></div><div><h3>Results</h3><p>A total of 76 active compounds in WYSJO, 206 WYSJO drug targets, 3 797 diabetic wound targets, and 167 diabetic wound associated WYSJO targets were screened out through network pharmacology. With the use of WYSJO-diabetic wound target network, core targets of seven active compounds encompassing quercetin, daidzein, kaempferol, rhamnetin, rhamnocitrin, strictosamide, and diisobutyl phthalate (DIBP) in WYSJO were found. GO enrichment analysis showed that the treatment of diabetes wounds with WYSJO may involve lipopolysaccharide, bacteria-derived molecules, metal ions, foreign stimuli, chemical stress, nutrient level, hypoxia, and oxidative stress in the biological processes. KEGG enrichment analysis showed that the treatment of diabetes wounds with WYSJO may involve advanced glycation end products (AGE-RAGE), p53, interleukin (IL)-17, tumor necrosis factor (TNF), hypoxia inducible factor-1 (HIF-1), apoptosis, lipid, atherosclerosis, etc. The results of animal experiments showed that WYSJO could significantly accelerate the healing process of diabetic wounds (<em>P</em> < 0.05), alleviate inflammatory response, promote the growth of granulation tissues, and down-regulate the expression levels of eight core genes [histone crotonyltransferase p300 (<em>EP300</em>), protoc gene-oncogene c-Jun (<em>JUN</em>), myelocytomatosis (<em>MYC</em>),","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 1","pages":"Pages 79-89"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589377724000284/pdfft?md5=f0f08cbc2e5dce716eac393f21342de7&pid=1-s2.0-S2589377724000284-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1016/j.dcmed.2024.04.007
Yue Luo , Jianfu Lu , Yunsong Zheng , Lei Bao , Chuanbiao Wen
Objective
This study aimed to explore the influencing factors of dynamic changes in traditional Chinese medicine (TCM) constitution based on general statistics, Apriori-DEMATEL algorithm, and DoWhy causal inference framework methods.
Methods
Dynamic collection of TCM constitution identification data was conducted from the population aged 18 − 60, containing collection time and constitution type, and 11 constitution influencing factors including dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, stress level, living environment, work/life calamity, family atmosphere, business trip frequency, and overtime situation. General statistical analysis was used to analyze the relative percentage of corresponding influencing factors of different types of constitution changes, the Apriori-DEMATEL algorithm was used to analyze the correlation between 11 constitution influencing factors such as dietary habit and constitution changes, and the DoWhy causal inference framework was used to analyze the causality between dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level, explore the frequency of constitution type transformation-change factors, and determine the key influencing factors causing dynamic changes in constitution type.
Results
After preprocessing, 13536 valid data points were obtained. Based on the Apriori-DEMATEL algorithm, the factors were divided into six original factors including dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level, and five result factors including living environment, work/life calamity, family atmosphere, business trip frequency, and overtime situation. Combining with general statistics, we found that among the original factors, changes in dietary habit, sleeping habit, sleeping duration, and stress level had a greater impact on other factors. In the process of constitution conditioning, attention should be paid to these four factors to maintain constitution balance. Among the five result factors, the absolute values of work/life calamity and family atmosphere were relatively large, indicating that these two factors were easily influenced by other factors. The dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level have higher centrality in changes, indicating that these six factors had important in constitution changes. According to the statistical frequency of constitution changes corresponding to each factor, we found that the changes of these six factors accounted for a large proportion of the constitution transformation frequency among Qi deficiency constitution, balanced constitution, and allergic constitution, indicating that the changes of these six factors played an important role in the changes of the three constitution types. Combined with the results of the Apriori-DEMATEL algorithm, and DoWhy
{"title":"Influencing factors analysis of dynamic change of TCM constitution based on multiple methods","authors":"Yue Luo , Jianfu Lu , Yunsong Zheng , Lei Bao , Chuanbiao Wen","doi":"10.1016/j.dcmed.2024.04.007","DOIUrl":"https://doi.org/10.1016/j.dcmed.2024.04.007","url":null,"abstract":"<div><h3>Objective</h3><p>This study aimed to explore the influencing factors of dynamic changes in traditional Chinese medicine (TCM) constitution based on general statistics, Apriori-DEMATEL algorithm, and DoWhy causal inference framework methods.</p></div><div><h3>Methods</h3><p>Dynamic collection of TCM constitution identification data was conducted from the population aged 18 − 60, containing collection time and constitution type, and 11 constitution influencing factors including dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, stress level, living environment, work/life calamity, family atmosphere, business trip frequency, and overtime situation. General statistical analysis was used to analyze the relative percentage of corresponding influencing factors of different types of constitution changes, the Apriori-DEMATEL algorithm was used to analyze the correlation between 11 constitution influencing factors such as dietary habit and constitution changes, and the DoWhy causal inference framework was used to analyze the causality between dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level, explore the frequency of constitution type transformation-change factors, and determine the key influencing factors causing dynamic changes in constitution type.</p></div><div><h3>Results</h3><p>After preprocessing, 13536 valid data points were obtained. Based on the Apriori-DEMATEL algorithm, the factors were divided into six original factors including dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level, and five result factors including living environment, work/life calamity, family atmosphere, business trip frequency, and overtime situation. Combining with general statistics, we found that among the original factors, changes in dietary habit, sleeping habit, sleeping duration, and stress level had a greater impact on other factors. In the process of constitution conditioning, attention should be paid to these four factors to maintain constitution balance. Among the five result factors, the absolute values of work/life calamity and family atmosphere were relatively large, indicating that these two factors were easily influenced by other factors. The dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level have higher centrality in changes, indicating that these six factors had important in constitution changes. According to the statistical frequency of constitution changes corresponding to each factor, we found that the changes of these six factors accounted for a large proportion of the constitution transformation frequency among Qi deficiency constitution, balanced constitution, and allergic constitution, indicating that the changes of these six factors played an important role in the changes of the three constitution types. Combined with the results of the Apriori-DEMATEL algorithm, and DoWhy","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 1","pages":"Pages 56-67"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589377724000259/pdfft?md5=6bb35028a5f985897367a78915e65a48&pid=1-s2.0-S2589377724000259-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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