Digital Chinese Medicine最新文献

{"title":"Uric acid-lowering activity and mechanisms of Chinese medicines with medicine-food homology: a systematic study","authors":"Qin Fengyi ,&nbsp;Zhu Yishuo ,&nbsp;Zhao Lewei ,&nbsp;Chen Siyu ,&nbsp;Qing Zhixing","doi":"10.1016/j.dcmed.2025.01.004","DOIUrl":"10.1016/j.dcmed.2025.01.004","url":null,"abstract":"<div><h3>Objective</h3><div>To summarize the uric acid-lowering effects and mechanisms of Chinese medicines with medicine-food homology, aiming to provide novel perspectives for the development of new anti-hyperuricemia (HUA) drugs.</div></div><div><h3>Methods</h3><div>Papers on the research of HUA prevention and treatment with medicine-food homology from December 15, 2002 to August 10, 2024 were screened and collected through China National Knowledge Infrastructure (CNKI), PubMed, ScienceDirect, and Google Scholar. Subsequently, the impact of these medications and their extracts, as well as the active compounds on HUA were assessed.</div></div><div><h3>Results</h3><div>A total of 148 relevant papers were collected, including 43 kinds of Chinese medicines and 61 active compounds, all of which have anti-HUA activity. Among them, 41 kinds of Chinese medicines could inhibit the activity of xanthine oxidase, thus leading to the inhibition of uric acid production; and 22 kinds of Chinese medicines could facilitate uric acid excretion, while 15 kinds of Chinese medicines could reduce the inflammation levels in the body and promoting renal protection. Notably, polyphenols and flavonoids are the key active components for the uric acid-lowering effects.</div></div><div><h3>Conclusion</h3><div>This study systematically summarized and analyzed the uric acid-lowering effects and mechanisms of Chinese medicines with medicine-food homology, laying a foundation for their development as HUA agents.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 405-418"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research progress in Fangjiomics: methodologies, applications, and perspectives","authors":"Bing Li ,&nbsp;Yuwen Zhao ,&nbsp;Qikai Niu ,&nbsp;Zhong Wang","doi":"10.1016/j.dcmed.2025.01.001","DOIUrl":"10.1016/j.dcmed.2025.01.001","url":null,"abstract":"<div><div>Fangjiomics is a promising paradigm that enhances research on multi-omics-based pharmacological mechanisms of Fangji from holistic and systematic perspective. We reviewed recent advances in Fangjiomics, focusing on database and analysis platform development, methodological innovations, and translational applications. Through the integration of Fangji and multi-omics data, multi-level system analysis approaches were developed, encompassing single-target analysis, signaling pathways, multi-targeted network and modules. Fangjiomics has emerged as a key strategy in various areas of Fangji research. To support the high quality development of Fangjiomics, we propose principles and perspectives from the integrated, macro-level, and practical viewpoints.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 309-319"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating proteomics and targeted metabolomics to reveal the material basis of liver-gallbladder damp-heat syndrome in chronic hepatitis B","authors":"Ni’ao Li ,&nbsp;Yuefeng Gong ,&nbsp;Jia Wang ,&nbsp;Qingqing Chen ,&nbsp;Shibing Su ,&nbsp;Hua Zhang ,&nbsp;Yiyu Lu","doi":"10.1016/j.dcmed.2025.01.005","DOIUrl":"10.1016/j.dcmed.2025.01.005","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;To elucidate the biological basis of liver-gallbladder damp-heat syndrome (LGDHS) within the framework of traditional Chinese medicine (TCM) as a complementary diagnostic and therapeutic approach in chronic hepatitis B (CHB).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;CHB patients and healthy volunteers were enrolled from Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between August 21, 2018 and December 31, 2020. They were divided into three groups: healthy group, LGDHS group, and latent syndrome (LP) group. Proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) was performed to identify differentially expressed proteins (DEPs). Metabolomic profiling via ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was applied to serum samples to detect differentially regulated metabolites (DMs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment were employed to explore dysregulated pathways. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were utilized to visualize group separation and identify key metabolites and proteins contributing to LGDHS differentiation. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic performance of key biomarkers, while logistic regression models assessed their predictive accuracy. &lt;em&gt;P&lt;/em&gt; values were corrected for multiple tests using the Benjamini-Hochberg method to control the false discovery rate (FDR). Validation of potential biomarkers was conducted using independent microarray data and real-time quantitative polymerase chain reaction (RT-qPCR).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;A total of 150 participants were enrolled, including healthy group (&lt;em&gt;n&lt;/em&gt; = 45), LGDHS group (&lt;em&gt;n&lt;/em&gt; = 60), and LP group (&lt;em&gt;n&lt;/em&gt; = 45). 254 DEPs from proteomics data and 72 DMs from metabolomic profiling were identified by PCA and OPLS-DA. DEPs were mainly enriched in immune and complement pathways, while DMs involved in amino acid and energy metabolism. The integrated analysis identified seven key biomarkers: &lt;em&gt;α&lt;/em&gt;1-acid glycoprotein (&lt;em&gt;ORM1&lt;/em&gt;), asparagine synthetase (&lt;em&gt;ASNS&lt;/em&gt;), solute carrier family 27 member 5 (&lt;em&gt;SLC27A5&lt;/em&gt;), glucosidase II alpha subunit (&lt;em&gt;GANAB&lt;/em&gt;), hexokinase 2 (&lt;em&gt;HK2&lt;/em&gt;), 5-methyltetrahydrofolate-homocysteine methyltransferase (&lt;em&gt;MTR&lt;/em&gt;), and maltase-glucoamylase (&lt;em&gt;MGAM&lt;/em&gt;). Microarray validation confirmed the diagnostic potential of these genes, with area under the curve (AUC) values for ROC analysis ranging from 0.536 to 0.759. Among these, &lt;em&gt;ORM1&lt;/em&gt;, &lt;em&gt;ASNS&lt;/em&gt;, and &lt;em&gt;SLC27A5&lt;/em&gt; showed significant differential ability in differentiating LGDHS patients (&lt;em&gt;P&lt;/em&gt; = 0.016, &lt;em&gt;P&lt;/em&gt; = 0.035, and &lt;em&gt;P&lt;/em&gt; &lt; 0.001, respectively), with corresponding AUC of 0.749, 0.743, and 0.759, respectively. A logistic regression model inc","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 320-331"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TCMLLM-PR: evaluation of large language models for prescription recommendation in traditional Chinese medicine","authors":"Tian Haoyu ,&nbsp;Yang Kuo ,&nbsp;Dong Xin ,&nbsp;Zhao Chenxi ,&nbsp;Ye Mingwei ,&nbsp;Wang Hongyan ,&nbsp;Liu Yiming ,&nbsp;Hu Minjie ,&nbsp;Zhu Qiang ,&nbsp;Yu Jian ,&nbsp;Zhang Lei ,&nbsp;Zhou Xuezhong","doi":"10.1016/j.dcmed.2025.01.007","DOIUrl":"10.1016/j.dcmed.2025.01.007","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and evaluate a fine-tuned large language model (LLM) for traditional Chinese medicine (TCM) prescription recommendation named TCMLLM-PR.</div></div><div><h3>Methods</h3><div>First, we constructed an instruction-tuning dataset containing &lt;styled-content style-type=\"number\"&gt;68654&lt;/styled-content&gt; samples (approximately 10 million tokens) by integrating data from eight sources, including four TCM textbooks, <em>Pharmacopoeia of the People’s Republic of China 2020</em> (CHP), Chinese Medicine Clinical Cases (CMCC), and hospital clinical records covering lung disease, liver disease, stroke, diabetes, and splenic-stomach disease. Then, we trained TCMLLM-PR using ChatGLM-6B with P-Tuning v2 technology. The evaluation consisted of three aspects: (i) comparison with traditional prescription recommendation models (PTM, TCMPR, and PresRecST); (ii) comparison with TCM-specific LLMs (ShenNong, Huatuo, and HuatuoGPT) and general-domain ChatGPT; (iii) assessment of model migration capability across different disease datasets. We employed precision, recall, and F1 score as evaluation metrics.</div></div><div><h3>Results</h3><div>The experiments showed that TCMLLM-PR significantly outperformed baseline models on TCM textbooks and CHP datasets, with F1@10 improvements of 31.80% and 59.48%, respectively. In cross-dataset validation, the model performed best when migrating from TCM textbooks to liver disease dataset, achieving an F1@10 of 0.155 1. Analysis of real-world cases demonstrated that TCMLLM-PR's prescription recommendations most closely matched actual doctors’ prescriptions.</div></div><div><h3>Conclusion</h3><div>This study integrated LLMs into TCM prescription recommendations, leveraging a tailored instruction-tuning dataset and developing TCMLLM-PR. This study will publicly release the best model parameters of TCMLLM-PR to promote the development of the decision-making process in TCM practices (&lt;ext-link ext-link-type=\"uri\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/2020MEAI/TCMLLM\"&gt;<span><span>https://github.com/2020MEAI/TCMLLM</span><svg><path></path></svg></span>&lt;/ext-link&gt;).</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 343-355"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of cold and hot medicinal properties of Chinese herbal medicines based on graph convolutional network","authors":"Mengling Yang,&nbsp;Wei Liu","doi":"10.1016/j.dcmed.2025.01.008","DOIUrl":"10.1016/j.dcmed.2025.01.008","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a model based on a graph convolutional network (GCN) to achieve efficient classification of the cold and hot medicinal properties of Chinese herbal medicines (CHMs).</div></div><div><h3>Methods</h3><div>After screening the dataset provided in the published literature, this study included 495 CHMs and their 8 075 compounds. Three molecular descriptors were used to represent the compounds: the molecular access system (MACCS), extended connectivity fingerprint (ECFP), and two-dimensional (2D) molecular descriptors computed by the RDKit open-source toolkit (RDKit_2D). A homogeneous graph with CHMs as nodes was constructed and a classification model for the cold and hot medicinal properties of CHMs was developed based on a GCN using the molecular descriptor information of the compounds as node features. Finally, using accuracy and F1 score to evaluate model performance, the GCN model was experimentally compared with the traditional machine learning approaches, including decision tree (DT), random forest (RF), k-nearest neighbor (KNN), Naïve Bayes classifier (NBC), and support vector machine (SVM). MACCS, ECFP, and RDKit_2D molecular descriptors were also adopted as features for comparison.</div></div><div><h3>Results</h3><div>The experimental results show that the GCN achieved better performance than the traditional machine learning approach when using MACCS as features, with the accuracy and F1 score reaching 0.836 4 and 0.845 3, respectively. The accuracy and F1 score have increased by &lt;styled-content style-type=\"number\"&gt;0.8690&lt;/styled-content&gt; and &lt;styled-content style-type=\"number\"&gt;0.8120&lt;/styled-content&gt;, respectively, compared with the lowest performing feature combination OMER (only the combination of MACCS, ECFP, and RDKit_2D). The accuracy and F1 score of DT, RF, KNN, NBC, and SVM are 0.505 1 and 0.501 8, 0.616 2 and 0.601 5, 0.676 8 and 0.624 3, 0.616 2 and 0.607 1, 0.636 4 and 0.622 5, respectively.</div></div><div><h3>Conclusion</h3><div>In this study, by introducing molecular descriptors as features, it is verified that molecular descriptors and fingerprints play a key role in classifying the cold and hot medicinal properties of CHMs. Meanwhile, excellent classification performance was achieved using the GCN model, providing an important algorithmic basis for the in-depth study of the “structure-property” relationship of CHMs.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 356-364"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk assessment of coronary artery occlusion based on integrated Chinese and western medicine data","authors":"Jiyu Zhang,&nbsp;Jiatuo Xu,&nbsp;Liping Tu,&nbsp;Yu Wang","doi":"10.1016/j.dcmed.2025.01.009","DOIUrl":"10.1016/j.dcmed.2025.01.009","url":null,"abstract":"<div><h3>Objective</h3><div>To develop an integrated risk model for coronary artery occlusion based on data of both traditional Chinese medicine (TCM) and western medicine data, and to evaluate the contribution of TCM-specific indicators to conventional coronary heart disease (CHD) risk prediction.</div></div><div><h3>Methods</h3><div>Data of TCM indicators (tongue, facial, and pulse diagnostics) and clinical parameters from patients diagnosed with CHD at the Cardiology Department of Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, from October 3, 2023 to March 15, 2024, were collected. Important variables were identified using importance screening and correlation analysis with CHD risk factors and laboratory markers. Six machine learning models including logistic regression (LR), decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), and gradient boosting (GB), were applied to evaluate the risk of coronary artery obstruction by combining clinical and TCM data of CHD. Model performance was assessed using metrics such as accuracy, precision, and recall, with reliability validated through ten-fold cross-validation.</div></div><div><h3>Results</h3><div>A total of 288 patients were included in the study. Fifteen clinical risk factors, including body mass index (BMI), myoglobin, and alcohol consumption history, were incorporated into the diagnostic models. The KNN model showed good performance when combining clinical data with tongue and facial data. The SVM model performed well when clinical data was combined with pulse data. Among all the models, the KNN model with 10-fold cross-validation, which integrates the three types of TCM diagnostic data (tongue, face, and pulse) with clinical data, performs the best (accuracy: 0.837, precision: 0.814, and recall: 0.809).</div></div><div><h3>Conclusion</h3><div>Incorporating TCM diagnostic data can enhance the accuracy of coronary artery obstruction risk assessment. The KNN prediction model that integrate tongue, facial, and pulse data performs the best and can be recommended as a clinical decision support tool.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 419-428"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the facial spectrum and color characteristics of patients with essential hypertension","authors":"Fu Hongyuan ,&nbsp;Chun Yi ,&nbsp;Jiao Wen ,&nbsp;Shi Yulin ,&nbsp;Tu Liping ,&nbsp;Li Yongzhi ,&nbsp;Xu Jiatuo","doi":"10.1016/j.dcmed.2025.01.010","DOIUrl":"10.1016/j.dcmed.2025.01.010","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;To investigate the facial spectrum and color characteristics of patients with essential hypertension post administering antihypertensive drugs, establish a classification and evaluation model based on the facial colors of the enrolled patients, and perform in-depth analysis on the important characteristics of their facial spectrum.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;From September 3, 2018, to March 23, 2024, participants with essential hypertension (receiving antihypertensive medication treatment, hypertension group) and normal blood pressure (control group) were recruited from the Cardiology Department of Shanghai Hospital of Traditional Chinese Medicine, the Coronary Care Unit of Shanghai Tenth People's Hospital, the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, and the Gaohang Community Health Service Center. This study employed the propensity score matching (PSM) method to reduce study participants selection bias. Spectral information in the facial visible light spectrum of the subjects was collected using a flame spectrometer, and the spectral chromaticity values were calculated using the equal-interval wavelength method. The study analyzed the differences in spectral reflectance across various facial regions, including the entire face, forehead, glabella, nose, jaw, left and right zygomatic regions, left and right cheek regions as well as differences in parameters within the Lab color space between the two subject groups. Feature selection was conducted using least absolute shrinkage and selection operator (LASSO) regression, followed by the application of various machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGB). The reduced-dimensional dataset was split in a 7 : 3 ratio to establish a classification and assessment model for facial coloration related to primary hypertension. Additionally, model fusion techniques were applied to enhance the predictive power. The performance of the models was evaluated using metrics including the area under the curve (AUC) and accuracy. Shapley Additive exPlanations (SHAP) was used to interpret the outcomes of the models.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;A total of 114 participants were included in both hypertension and control groups. Reflectance analysis across the entire face and eight predefined areas revealed that the hypertensive group exhibited significantly higher reflectance of corresponding color light in the blue-violet region (&lt;em&gt;P&lt;/em&gt; &lt; 0.05) and a lower reflectance in the red region (&lt;em&gt;P&lt;/em&gt; &lt; 0.05) compared with control group. Analysis of Lab color space parameters across the entire face and eight predefined areas showed that hypertensive group had significantly lower a and b values than control group (&lt;em&gt;P&lt;/em&gt; &lt; 0.05). LASSO regression analysis identified a ","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 4","pages":"Pages 429-440"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145876027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of needling at Sanyinjiao (SP6) acupuncture point on blood glucose levels and cardiovascular functions in patients with type 2 diabetes mellitus: a randomized placebo-controlled study","authors":"S. Priyadharshini ,&nbsp;A. Mooventhan ,&nbsp;Venkatalakshmi Saravanan ,&nbsp;N. Mangaiarkarasi","doi":"10.1016/j.dcmed.2024.12.002","DOIUrl":"10.1016/j.dcmed.2024.12.002","url":null,"abstract":"<div><h3>Objective</h3><div>To determine the effect of needling at Sanyinjiao (SP6) on random blood glucose (RBG) levels and cardiovascular function in patients with type 2 diabetes mellitus (T2DM).</div></div><div><h3>Methods</h3><div>In this randomized placebo-controlled study, T2DM patients (aged 35 – 65 years) were recruited from the Government Yoga and Naturopathy Medical College and Hospital, Chennai, India, between January 5, 2022 and March 15, 2023. Participants were randomly assigned to either acupuncture group or sham acupuncture group. The acupuncture group received bilateral needling at Sanyinjiao (SP6) while sham acupuncture group received needling at a non-acupuncture point [1.5 cun lateral to Sanyinjiao (SP6)] for 30 min. Primary outcome was RBG, and secondary outcomes included systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), pulse pressure (PP), mean arterial pressure (MAP), rate pressure product (RPP), and double product (Do-P). All parameters were assessed immediately before and after intervention.</div></div><div><h3>Results</h3><div>A total of 100 patients with T2DM were enrolled in the study, and blinded to acupuncture group (<em>n</em> = 50) and sham acupuncture group (<em>n</em> = 50). Intergroup analysis showed that significant reductions in RBG (<em>P &lt;</em> 0.001), SBP (<em>P</em> = 0.035), DBP (<em>P</em> = 0.008), and MAP (<em>P</em> = 0.009) were found in acupuncture group compared with sham acupuncture group. Within-group analysis showed significant reductions in RBG (<em>P</em> &lt; 0.001), SBP (<em>P</em> &lt; 0.001), DBP (<em>P</em> = 0.008), PP (<em>P</em> = 0.023), MAP (<em>P</em> &lt; 0.001), RPP (<em>P</em> &lt; 0.001), and Do-P (<em>P</em> = 0.002) in acupuncture group, whereas sham acupuncture group showed a significant decrease in PR (<em>P</em> = 0.023) only in the post-test assessment compared with pre-test assessment.</div></div><div><h3>Conclusion</h3><div>A period of 30 min of needling at the Sanyinjiao (SP6) acupuncture point reduces RBG and promotes cardiovascular function in patients with T2DM as compared with needling at non-acupuncture points. Sanyinjiao (SP6) acupuncture may offer an immediate, non-pharmacological intervention to strengthen glycemic control management and cardiovascular health in T2DM patients.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 3","pages":"Pages 224-230"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143262518","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}

{"title":"The gas discharge visualization (GDV) order parameter model based on the principle of mastering both permanence and change","authors":"Yu Xin ,&nbsp;Lei Zhang ,&nbsp;Qiancheng Zhao ,&nbsp;Yurong She ,&nbsp;Zhensu She ,&nbsp;Shuna Song","doi":"10.1016/j.dcmed.2024.12.003","DOIUrl":"10.1016/j.dcmed.2024.12.003","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the human body’s complex system, and classify and characterize the human body’s health states with “a comprehensive integrated method from qualitative to quantitative”.</div></div><div><h3>Methods</h3><div>This paper introduces the concept of “order parameters” and proposes a method for establishing an order parameter model of gas discharge visualization (GDV) based on the principle of “mastering both permanence and change (MBPC)”. The method involved the following three steps. First, average luminous intensity (<span><math><mrow><mover><mi>I</mi><mo>¯</mo></mover></mrow></math></span>) and average area (<span><math><mrow><mover><mi>S</mi><mo>¯</mo></mover></mrow></math></span>) of the GDV images were calculated to construct the phase space, and the score of the health questionnaire was calculated as the health deviation index (<em>H</em>). Second, the k-means++ clustering method was employed to identify subclasses with the same health characteristics based on the data samples, and to statistically determine the symptom-specific frequencies of the subclasses. Third, the distance (<em>d</em>)&lt;italic/&gt; between each sample and the “ideal health state”, which determined in the phase space of each subclass, was calculated as an order parameter describing the health imbalance, and a linear mapping was established between the <em>d</em> and the <em>H</em>. Further, the health implications of GDV signals were explored by analyzing subclass symptom profiles. We also compare the mean square error (MSE) with classification methods based on age, gender, and body mass index (BMI) indices to verify that the phase space possesses the ability to portray the health status of the human body.</div></div><div><h3>Results</h3><div>This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants. Based on the discovered linear law, the current model can use <em>d</em> calculated by measuring the GDV signal to predict <em>H</em> (<em>R</em>² &gt; 0.77). Combined with the symptom profiles of the subclasses, we explain the classification basis of the phase space based on the pattern identification. Compared with common classification methods based on age, gender, BMI, etc., the MSE of phase space-based classification was reduced by an order of magnitude.</div></div><div><h3>Conclusion</h3><div>In this study, the GDV order parameter model based on MBPC can identify subclasses and characterize individual health levels, and explore the TCM health meanings of the GDV signals by using subjective-objective methods, which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 3","pages":"Pages 231-240"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143262514","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}

{"title":"Lightweight and polarized self-attention mechanism for abnormal morphology classification algorithm during traditional Chinese medicine inspection","authors":"Zhang Qi ,&nbsp;Hu Kongfa ,&nbsp;Wang Tianshu ,&nbsp;Yang Tao","doi":"10.1016/j.dcmed.2024.12.005","DOIUrl":"10.1016/j.dcmed.2024.12.005","url":null,"abstract":"<div><h3>Objective</h3><div>To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine (TCM) inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost.</div></div><div><h3>Methods</h3><div>First, this paper establishes a dataset of abnormal morphology for Chinese medicine diagnosis, with images from public resources and labeled with category labels by several Chinese medicine experts, including three categories: normal, shoulder abnormality, and leg abnormality. Second, the key points of human body are extracted by Light-Atten-Pose algorithm. Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention (PSA) mechanism on the basis of AlphaPose, which reduces the computation amount by using EfficientNet network, and the data is finely processed by using PSA mechanism in spatial and channel dimensions. Finally, according to the theory of TCM inspection, the abnormal morphology standard based on the joint angle difference is defined, and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculating the angle between key points. Accuracy, frames per second (FPS), model size, parameter set (Params), and giga floating-point operations per second (GFLOPs) are chosen as the evaluation indexes for lightweighting.</div></div><div><h3>Results</h3><div>Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%, which is close to the original AlphaPose model. However, the FPS of the improved model reaches 41.6 fps from 16.5 fps, the model size is reduced from 155.11 MB to 33.67 MB, the Params decreases from 40.5 M to 8.6 M, and the GFLOPs reduces from 11.93 to 2.10.</div></div><div><h3>Conclusion</h3><div>The Light-Atten-Pose algorithm achieves lightweight while maintaining high robustness, resulting in lower complexity and resource consumption and higher classification accuracy, and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.</div></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":"7 3","pages":"Pages 256-263"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143262517","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}