Traditional Chinese Medicine Knowledge Research Articles

Ontology construction of traditional Chinese medicine diagnosis and treatment for myopia

Objective: To keep up with the progress of treatment for myopia in traditional Chinese medicine (TCM) through literature research, so as to determine data source of TCM diagnosis and treatment for myopia, from which entities are extracted for integration of relevant knowledge, and further formal expression and visualization of knowledge. Methods: Literature research and comparative analysis were used to determine the data source, and the seven-step method of constructing ontology was referred to for extraction of TCM diagnosis and treatment entities, which then was terminalized for development of classes and class hierarchy, and class properties, as well as creation of instances before consistency test of the ontology. Results: The ontology of TCM diagnosis and treatment for myopia includes 26 classes and 124 instances, 9 object properties and 1 data property. Conclusion: The combination of ontology technology and TCM diagnosis and treatment knowledge of myopia contributes to the construction of knowledge base of TCM diagnosis and treatment ontology for myopia, which realizes formal expression and visualization of relevant TCM knowledge, lays a foundation for the construction of TCM top-level ontology and ophthalmology ontology, and facilitates knowledge reusing and sharing.

TarKG: a comprehensive biomedical knowledge graph for target discovery.

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery. We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1143313 entities and 32806467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG. TarKG is accessible at https://tarkg.ddtmlab.org.

Objective Analysis of Traditional Chinese Medicine Syndrome Differentiation of Patients With Diabetes and Prediabetes: Protocol for a Nonrandomized, Exploratory, Observational Case-Control Study Using Digitalized Traditional Chinese Medicine Diagnostic Tools.

Diabetes and prediabetes are diagnosed differentially by Western and Chinese medicine. While Western medicine uses objective laboratory analysis of biochemical parameters to define the severity of diabetes and prediabetes, Chinese medicine uses a comprehensive approach that integrates observation, inquiry, pulse palpation, and tongue diagnosis. The medical information collected is then categorized into different syndromes. However, traditional methods of pulse and tongue diagnoses used to determine syndrome differentiation are highly subjective and skill dependent. This study aims to identify the gap in conventional traditional Chinese medicine (TCM) diagnostic techniques for syndrome differentiation analysis using contemporary diagnostic devices. We devised a protocol for a nonrandomized, exploratory, observational case-control study with equal allocations in 5 arms to investigate the syndrome differentiation of diabetes and prediabetes. We hypothesize that the TCM syndrome differentiation of diabetes and prediabetes in the tropical climate may differ from that defined based on the Chinese demographic. We also speculate that the high-frequency spectral energy may reflect a difference in pulse wave intensity and density between the healthy and diabetes groups. A total of 250 eligible participants will be equally assigned to 1 of 5 arms (healthy or subhealthy, prediabetes, diabetes, prediabetes with hypertension and dyslipidemia, and diabetes with hypertension and dyslipidemia). Participants aged 21-75 years, of any sex or race, and have been diagnosed with diabetes (fasting plasma glucose [FPG] of 7 mmol/L, or 2-hour plasma glucose [2hPG] of 11.1 mmol/L) or prediabetes (impaired FPG of 6.1-6.9 mmol/L, or impaired glucose tolerance with an 2hPG of 7.8-11 mmol/L) will be included. The Health Evaluation Questionnaire, Physical Activity Questionnaire, sugar intake assessment, Constitution in Chinese Medicine Questionnaire, radial pulse diagnosis, and tongue diagnosis will be performed in a single visit. ANOVA for continuous data and chi-square tests of independence will be used for categorical data assessments, with a level of P<.05 considered significant. The recruitment is in progress. We anticipate that the study will conclude in June 2025. As of July 15, 2024, we have enrolled 140 individuals. To the best of our knowledge, this is the first study to use contemporary TCM diagnostic instruments to map expert and empirical knowledge of TCM to its scientific equivalents for the purpose of evaluating the syndrome differentiation of diabetes. We designed this protocol with the exploratory goal to examine objectively the syndrome differentiation of patients with diabetes and those with prediabetes using TCM diagnostic technologies. The data collected and evaluated under standardized conditions using these contemporary diagnostic devices will exhibit a higher degree of stability, hence yielding dependable and unbiased results for syndrome differentiation. Thus, our findings may potentially increase the accuracy of identification, diagnosis, treatment, and prevention of diabetes and prediabetes through a system of targeted treatment. ClinicalTrials.gov NCT05563090; https://clinicaltrials.gov/ct2/show/NCT05563090. DERR1-10.2196/56024.

HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network.

Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.

Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.

The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations. This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset. These datasets underpinned the development of the Lingdan Pre-trained LLM and 2 specialized models: the Lingdan-TCPM-Chat Model, which uses a Chain-of-Thought process for symptom analysis and TCPM recommendation, and a Lingdan Prescription Recommendation model (Lingdan-PR) that proposes herbal prescriptions based on electronic medical records. The Lingdan-TCPM-Chat and the Lingdan-PR Model, fine-tuned on the Lingdan Pre-trained LLM, demonstrated state-of-the art performances for the tasks of TCM clinical knowledge answering and herbal prescription recommendation. Notably, Lingdan-PR outperformed all state-of-the-art baseline models, achieving an improvement of 18.39% in the Top@20 F1-score compared with the best baseline. This study marks a pivotal step in merging advanced LLMs with TCM, showcasing the potential of artificial intelligence to help improve clinical decision-making of medical diagnostics and treatment strategies. The success of the Lingdan Pre-trained LLM and its derivative models, Lingdan-TCPM-Chat and Lingdan-PR, not only revolutionizes TCM practices but also opens new avenues for the application of artificial intelligence in other specialized medical fields. Our project is available at https://github.com/TCMAI-BJTU/LingdanLLM.

Optimization of the extraction process for Shenshou Taiyi powder based on Box-Behnken experimental design, standard relation, and FAHP-CRITIC methods

BackgroundAncient classic prescription play a crucial role in the preservation and advancement of traditional Chinese medicine (TCM) theories. They represent a significant milestone in the ongoing development and transmission of TCM knowledge and practices and are considered one of the breakthroughs in the development of TCM inheritance. In the process of developing ancient classic prescriptions, many problems may still arise in ensuring quality consistency between traditional methods and modern production processes, among which the extraction process poses major challenges. This paper introduces a practical approach extracting an ancient classic prescription using a modern extraction process. The technique is demonstrated through the study of the extraction process of Shenshou Taiyi powder (STP).MethodsThis study focuses on optimising the STP extraction process to ensure consistency in the quality of the product obtained through ancient and modern processes using the standard relation and fuzzy analytic hierarchical process (FAHP) and criteria importance through intercriteria correlation (CRITIC) method integrated weights combined with the Box-Behnken response surface test. Using the contents of rosmarinic acid, isoimperatorin, puerarin, as well as the extract yield and fingerprint similarity as evaluation indexes of STP, the Box-Behnken response surface method was employed to examine the varying extraction parameters, including water addition ratio, extraction duration, and number of extractions. The weighted coefficients for each parameter were calculated by combining the benchmark correlation and FAHP-CRITIC method, deriving a comprehensive score.ResultsThe optimal extraction process for STP consisted of a two extractions, each using at a tenfold quantity of water, performed for one hour. Process verification across three separate batches yielded a comprehensive score of 94.7, with a relative standard deviation of 0.76%.ConclusionsThe application of the Box-Behnken response surface method combined with standard relation and FAHP-CRITIC approach proved to be stable and feasible for optimising the extraction process of STP.

Studying the Influence of School Culture on Health-related Knowledge, Attitudes and Practices of Traditional Chinese Medicine

With the development of the mobile internet and the intensification of the differentiated behaviour of communication audiences, there are many challenges and difficulties in content dissemination to younger generations about promoting Traditional Chinese Medicine (TCM) and improving health-related behaviour. Current literature lacks information on the association between knowledge, attitude, practice, and campus culture construction. This article investigates the relationship between traditiona Chinese medicine (TCM) campus culture construction and health-related knowledge, attitudes, and practices (KAP) among primary campus students in China. It employed structural equation modelling (SEM) to analyze the data. For this study, the data was collected through an online questionnaire from a group of students. Exploratory factor analysis (EFA) and Confirmatory FactorAnalysis (CFA) was conducted on data for validation and reliability. The study contributes to the existing literature by providing empirical evidence on the influence of TCM campus culture construction on health-related KAP among primary campus students.

Advances in Research Methods of Pharmacodynamic Substances in Traditional Chinese Medicines and Their Application in the Teaching of Traditional Chinese Medicine Analysis

With the continuous progress of science and technology, the research methods of pharmacodynamic substances in traditional Chinese medicine are developing, and the application of these methods in teaching is becoming more and more extensive. By introducing these research methods into the classroom, teachers can help students to deeply understand the nature and mechanism of action of pharmacodynamic substances in traditional Chinese medicine, and improve their interest in and knowledge of traditional Chinese medicine. This paper introduces the definition of pharmacodynamic substances in traditional Chinese medicine, research methods, and their application in the teaching of traditional Chinese medicine analysis.

Innovative behaviour profile and its associated factors among nurses in China: a cross-sectional study based on latent profile analysis

ObjectivesThis study aimed to investigate the current status of innovative behaviours among nurses in traditional Chinese medicine (TCM) hospitals using latent profile analysis, identify potential subgroups and their population characteristics...

The first translation on pediatrics: Er Ke Cuo Yao

Er Ke Cuo Yao(The Pediatric Summary) is the first monograph on pediatrics of Western medicine translated and introduced in the late Qing Dynasty. It occupies an important position in the history of the development of modern pediatrics and modern Western medicine in China. Edited by American missionary Mary Hannah Fulton and translated by Chinese Western medicine scholar Yin Duanmo, the book consists of 2 volumes and 14 books, including general discussion of pediatrics, diagnosis of pediatric diseases, and discussion on teething, throat diseases, gastrointestinal diseases, worms, liver diseases, abdominal epimesis, and new infectious diseases, etc. covering the diagnosis, treatment, maintenance and other knowledge of Western medical pediatrics. The source of Er Ke Cuo Yao is not yet available, but it may be a synthesis of various materials, and the translation uses paraphrasing, transliteration, and traditional Chinese medicine terminology, and the language is easy to understand. Er Ke Cuo Yao reflects the level of development of pediatrics in Western medicine in the early modern period, makes up for the lack of pediatrics knowledge of traditional Chinese medicine, and plays an active role in the diagnosis and treatment of common pediatric diseases, epidemiological diseases, surgical diseases, and malnutrition prevention.

Exploration and Practice of Teaching Methods of Comprehensive Knowledge and Skills of Traditional Chinese Medicine with Traditional Chinese Medicine Traditional Skills Inheritance as the Core

Traditional Chinese Medicine (TCM) is a core component of Chinese traditional medicine, and its unique therapeutic concepts and techniques have received high attention worldwide. In the current medical education system, there are still many challenges in teaching Chinese medicine, especially the insufficient transfer of practical skills. This study aims to explore an integrated teaching model that combines theoretical teaching and practical operation with traditional skills of Chinese medicine as the core. Through literature review and case studies, this paper analyzes the deficiencies of Traditional Chinese Medicine teaching and then designs a new teaching model that highlights the integration of theory and practice, the enhancement of teacher-student interaction, and the application of diverse teaching methods. The implementation results show that this teaching model effectively improves students' practical skills in Chinese medicine, enhances their understanding and application of Chinese medicine knowledge, and significantly stimulates their interest in learning Chinese medicine. The successful practice of this teaching model not only provides valuable experience for the innovation of TCM education but also provides strong educational support to promote the continuous development of TCM. The results of this study emphasize the importance and practical value of teaching integrated skills centered on traditional skills in TCM education.

Research progress on mechanism of traditional Chinese medicine intervention in angiogenesis in prevention and treatment of nontraumatic avascular necrosis of femoral head

Nontraumatic avascular necrosis of the femoral head(NANFH) is a common and refractory femoral head disease that causes bone death due to interruption of blood supply. Early clinical symptoms are atypical, such as hip pain and limited joint function. In the late stage, severe pain, shortening of the affected limb, claudication, and other serious symptoms are common, which se-riously affects the quality of life of patients. Therefore, it is of great significance to actively improve the clinical symptoms of NANFH to enhance the quality of life of patients. The pathogenesis of NANFH is complex, such as traumatic vascular circulatory disorders, the use of hormones or other drugs, alcoholism, and diabetes mellitus. These factors directly or indirectly lead to femoral head vascular damage, thrombosis, and coagulation system disorders, which reduce the blood supply to the acetabulum and femoral head, thus causing ischaemic death of the femoral head or even femoral head collapse. NANFH is mainly categorized as &quot;bone impotence&quot; and &quot;bone paralysis&quot; in traditional Chinese medicine(TCM). The treatment of NANFH with TCM has the characteristics and advantages of a long history, stable and reliable therapeutic effect, fewer adverse reactions, good patient tolerance, and high acceptance. Previous studies have shown that the promotion of angiogenesis is a key initiative in the prevention and treatment of NANFH, and TCM can promote fe-moral head angiogenesis by interfering with the expression of angiogenesis-related factors, which in turn can help to restore the blood supply of the femoral head and thus improve clinical symptoms of NANFH and prevent and treat NANFH. This article described the roles of blood supply interruption and angiogenesis in NANFH and the accumulated knowledge and experience of TCM in NANFH and summarized the role of angiogenesis-related factors in NANFH and the research progress on TCM intervention, so as to provide an idea for the subsequent research and a new basis for the clinical application of TCM in the treatment of NANFH.

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Acupuncture and tuina knowledge graph with prompt learning.

Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations. (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature. A robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations. The integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.

Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models

This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, extracting various entities and their relationships, providing a solid foundation for TCM learning, research, and application. The knowledge graph has significant potential in TCM, aiding in teaching, disease diagnosis, treatment decisions, and contributing to TCM modernization. In conclusion, this paper utilizes large language models to construct a knowledge graph for TCM, offering a vital foundation for knowledge representation and application in the field, with potential for future expansion and refinement.

TLDA: A transfer learning based dual-augmentation strategy for traditional Chinese Medicine syndrome differentiation in rare disease

The Traditional Chinese Medicine (TCM) has demonstrated its significant medical value over the decades, particularly during the COVID-19 pandemic. TCM-AI interdisciplinary models have been proposed to model TCM knowledge, diagnosis, and treatment experiments in clinical practice. Among them, numerous models have been developed to simulate the syndrome differentiation process of human TCM doctors for automatic syndrome diagnosis. However, these models are designed for normal scenarios and trained using a supervised learning paradigm which needs tens of thousands of training samples. They fail to effectively differentiate syndromes in rare disease scenarios where the available TCM electronic medical records (EMRs) are very limited for each unique syndrome. To address the challenge of rare diseases, this study proposes a simple yet effective method called Transfer Learning based Dual-Augmentation (TLDA). TLDA aims to augment the limited EMRs at both the sample-level and feature-level, enriching the pathological and medical information during training. Extended experiments involving 11 comparison models, including the state-of-the-art model, demonstrate the effectiveness of TLDA. TLDA outperforms all comparison models by a significant margin. Furthermore, TLDA can also be extended to other medical tasks when the EMRs for diagnosis are limited in samples.

Demand and influencing factors of "Internet + Traditional Chinese Medicine" home nursing service for older adult patients with chronic diseases: a mixed research perspective.

In the context of global aging, the characteristics of chronic diseases seriously affect the quality of life of older adults. It is urgent to carry out continuous nursing basis for older adult patients with chronic diseases. In view of the remarkable efficacy of Chinese medicine in the treatment of chronic diseases, this study may help to understand the demand for "Internet + Chinese medicine" home care service and its influencing factors of older adult chronic disease patients, and to provide a reference basis for improving the quality of life of the older adult chronic disease population. This is a mixed study. The quantitative study adopted the convenience sampling method, and a total of 308 patients in a third-grade hospital in Shaanxi Province were investigated by general data questionnaire, traditional Chinese medicine service demand questionnaire, traditional Chinese medicine knowledge questionnaire, older adult Chinese medicine attitude questionnaire, and home care demand questionnaire from March to April 2022. In the qualitative study, semi-structured interviews were adopted, and patients were interviewed until the content was saturated. Colaizzi analysis method was used to analyze and summarize the topic of the interview data. 308 valid questionnaires were collected, and the patients scored (58.42 ± 17.16) on the demand for TCM nursing services, (59.86 ± 11.54) on the knowledge of TCM, (73.03 ± 9.11) on the attitude toward TCM, and (136.84 ± 46.39) on the demand for home care. The results of multiple linear regression showed that learning about the nursing service pathway, knowledge of general knowledge of Chinese medicine, and attitude toward Chinese medicine among the older adult and home care demand were the influencing factors of the demand for Chinese medicine nursing services for older adult patients with chronic diseases (p < 0.05). The results of the in-depth interviews were summarized into three themes: facilitating factor, hindering factor, and the "Internet + Chinese medicine" multiple needs of home care. Older adult patients with chronic diseases have a high intention of home care demand and they are affected by multiple factors. Consequently, the actual demand situation of older adult patients with chronic diseases should be used as a guide to provide directed and diversified Chinese medicine home care services to meet the individualized needs of the older adult.

Internet-Based Healthcare Knowledge Service for Improvement of Chinese Medicine Healthcare Service Quality.

With the development of new-generation information technology and increasing health needs, the requirements for Chinese medicine (CM) services have shifted toward the 5P medical mode, which emphasizes preventive, predictive, personalized, participatory, and precision medicine. This implies that CM knowledge services need to be smarter and more sophisticated. This study adopted a bibliometric approach to investigate the current state of development of CM knowledge services, and points out that accurate knowledge service is an inevitable requirement for the modernization of CM. We summarized the concept of smart CM knowledge services and highlighted its main features, including medical homogeneity, knowledge service intelligence, integration of education and research, and precision medicine. Additionally, we explored the intelligent service method of traditional Chinese medicine under the 5P medical mode to support CM automatic knowledge organization and safe sharing, human-machine collaborative knowledge discovery and personalized dynamic knowledge recommendation. Finally, we summarized the innovative modes of CM knowledge services. Our research will guide the quality assurance and innovative development of the traditional Chinese medicine knowledge service model in the era of digital intelligence.

Curative effect of medicine cake sticking ultrasound drug penetration combined with body training on hemiplegia after stroke: An in vitro ultrasound targeted drug controlled release technology

To explore the efficacy of ultrasound drugs in the treatment of hemiplegia after stroke. The evaluation included clinical symptoms and signs, the Stroke Scale, activities of daily living, sensory disorder Fugl-Meyer and Lindmark, electromyography sensory nerve amplitude, and conduction velocity indexes in both groups. There was no significant difference in the improved Fugl-Meyer and Lindmark score between treatment (26.97 ± 2.78) and the control group (27.45 ± 3.1) (t = 14.528, P = 0.593). After treatment, the observation group (37.10 ± 4.2) was significantly different from the control group (34.76 ± 4.36) (t = 11.259, P = 0.005) and (t = 10.15 ± 1.69), (40.87 ± 6.58) (t = 7.943,9.538, P = 0.564,0.826). After treatment, the observation group the Stroke Scale (4.27 ± 0.57), activities of daily living score (76.15 ± 12.38) and the control group (5.36 ± 0.89), (58.41 ± 9.69) (t = 16.274,5.379, P = 0.035,0.000) after treatment and F wave and M wave. The cure rate of the observation group was 77.50% (31/40), which was significantly better than that of the control group, 47.50% (19/40), with a significant difference (χ2 = 11.724,P = 0.000). After comparison, the total response rate of the observed group reached 92.500% (37 / 40), which was significantly higher than the 80.00% (32 / 40) of the control group. This difference was statistically significant (χ 2 = 9.458, P = 0.015). This therapy closely links the theoretical knowledge of modern medicine with the theoretical knowledge of traditional Chinese medicine, and uses the meridian theory to give full play to the unique advantages of traditional Chinese medicine.

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data.

Background: The complexity and rapid progression of lesions in diabetic kidney disease pose significant challenges for clinical diagnosis and treatment. The advantages of Traditional Chinese Medicine (TCM) in diagnosing and treating this condition have gradually become evident. However, due to the disease's complexity and the individualized approach to diagnosis and treatment in Traditional Chinese Medicine, Traditional Chinese Medicine guidelines have limitations in guiding the treatment of diabetic kidney disease. Most medical knowledge is currently stored in the process of recording medical records, which hinders the understanding of diseases and the acquisition of diagnostic and treatment knowledge among young doctors. Consequently, there is a lack of sufficient clinical knowledge to support the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine. Objective: To build a comprehensive knowledge graph for the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine, utilizing clinical guidelines, consensus, and real-world clinical data. On this basis, the knowledge of Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was systematically combed and mined. Methods: Normative guideline data and actual medical records were used to construct a knowledge graph of Traditional Chinese Medicine diagnosis and treatment for diabetic kidney disease and the results obtained by data mining techniques enrich the relational attributes. Neo4j graph database was used for knowledge storage, visual knowledge display, and semantic query. Utilizing multi-dimensional relations with hierarchical weights as the core, a reverse retrieval verification process is conducted to address the critical problems of diagnosis and treatment put forward by experts. Results: 903 nodes and 1670 relationships were constructed under nine concepts and 20 relationships. Preliminarily a knowledge graph for Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was constructed. Based on the multi-dimensional relationships, the diagnosis and treatment questions proposed by experts were validated through multi-hop queries of the graphs. The results were confirmed by experts and showed good outcomes. Conclusion: This study systematically combed the Traditional Chinese Medicine diagnosis and treatment knowledge of diabetic kidney disease by constructing the knowledge graph. Furthermore, it effectively solved the problem of "knowledge island". Through visual display and semantic retrieval, the discovery and sharing of diagnosis and treatment knowledge of diabetic kidney disease were realized.