Application of data mining algorithms in research on traditional Chinese medicine formula / 中国药房

In recent years， data mining algorithms have been widely employed in scientific research within the field of traditional Chinese medicine （TCM）. The data mining algorithms are used to effectively handle and analyze the complex data in TCM formulas， providing a rational explanation for the mechanism of action. This method has proven particularly useful in uncovering patterns of compatibility and frequent combinations of herbs in TCM， thereby enhancing the reliability and accuracy of clinical diagnosis， target screening， and the study of new drugs. This paper reviews and analyzes 147 papers on TCM formula research that utilize data mining algorithms. The results indicate that data mining algorithms play a unique advantage in six sub- areas， including the study on the mechanism of action in TCM formula， the dose-efficacy of TCM formulas， the identification of core drugs pairs/groups， mining the relationships among “formulas-drug-symptom”， the discovery of new formulas， and mining the compatibility law. Notably， association rules and clustering algorithms are the most representative.

Application of Animal Models of Corneal Neovascularization Based on Data Mining / 中国实验方剂学杂志

ObjectiveTo provide a reference for the establishment of an ideal corneal neovascularization （CNV） animal model by summarizing the modeling characteristics of CNV animal models. MethodWith "CVN" as the theme word， this paper searched the China National Knowledge Infrastructure （CNKI）， Wanfang， Chinese medical journals full-text database， and PubMed database and screened out relevant literature on CNV animal experiments from 2013 to 2023. The database was established by Excel 2021， and the experimental animal strain， gender， modeling method， detection index， and application category were sorted out. The characteristics of the CNV animal model were analyzed. ResultAfter comparative analysis， it was found that the animal strains were Sprague-Dawley rats （87 times， 29.49%） and New Zealand white rabbits （52 times， 17.63%）. Male animals were recommended. Most modeling methods for efficacy verification and mechanism studies were the alkali burn method. Index detection methods included apparent index observation， histopathological detection， immunohistochemistry （IHC）， Western blot， and various polymerase chain reaction （PCR） tests. Detection indexes included apparent indication， corneal histopathology， CNV regulation， etc. ConclusionThe CNV model of SD rats induced by the alkali burn method is recommended for model replication， and the indexes are mainly selected from the growth of CNV， corneal histopathological test， and vascular endothelial growth factor （VEGF）-related test. In addition， according to the demand， the corneal apparent indication and the basic indexes related to the regulation of CNV， such as vascular endothelial growth factor receptor 2 （VEGFR2）， basic fibroblast growth factor （bFGF）， and secretogranin Ⅲ （Scg3） are also selected. Clinical treatment of CNV relies on anti-inflammatory drugs and anti-VEGF drugs， and there is a lack of application of traditional Chinese medicine （TCM）， so the model needs to be improved by adding elements of TCM syndromes.

Mining and analysis of acalabrutinib-induced ADE risk signals based on FDA adverse event reporting system / 中国药房

OBJECTIVE To provide reference for the clinically safe application of acalabrutinib by mining and analyzing the risk signals of adverse drug events （ADE）. METHODS The acalabrutinib-induced ADE reports were extracted from the U.S. FDA adverse event reporting system using the OpenVigil 2.1 platform from November 1， 2017 to March 31， 2023. The reporting odds ratio （ROR） method and composite criteria method from the Medicines and Healthcare Products Regulatory Agency （MHRA） were used for detection of ADE signals. RESULTS There were 7 869 ADE reports of acalabrutinib as the primary suspect drug and 142 ADE positive signals were detected from them， involving 20 system organ classes， which was generally consistent with the ADE recorded in the drug instruction of acalabrutinib， mainly involving general disorders and administration site conditions， various inspection， blood and lymphatic system disorders， various neurological disorders and cardiac disorders. In addition， this study identified several new potential ADE signals that were not mentioned in the drug instruction， including sudden cardiac death， pulmonary toxicity， tumor lysis syndrome， pleural effusion， dyspepsia， gastroesophageal reflux disease， bone pain， decreased blood pressure， and abnormal blood sodium， etc. CONCLUSIONS When using acalabrutinib， in addition to paying attention to the ADE recorded in its instructions， the risk of serious ADE that may lead to death， such as sudden cardiac death and pulmonary toxicity， should also be evaluated to avoid or reduce the occurrence of ADE as much as possible.

Análisis de interacciones de los actores de la red de conocimiento en salud ambiental del Observatorio Nacional de Salud de Colombia

Introducción: Las interrelaciones positivas y negativas entre el hombre y el medioambiente impactan en la salud general de la población, por esto, la gestión del conocimiento y la transformación social, orientadas a la prevención de la exposición a factores de riesgo ambiental y a la creación de ambientes propicios, deben realizarse a través de acciones multidisciplinares intersectoriales, como el trabajo colaborativo de redes del conocimiento. Objetivo: Describir las interacciones entre los actores de la Red de Conocimiento de Salud Ambiental del Observatorio Nacional de Salud de Colombia (ONS), con el fin de promover, mejorar y fortalecer la colaboración, intercambio de información y planificación conjunta de acciones. Metodología: Estudio descriptivo transversal de análisis de redes sociales mediante herramientas de minería de texto del lenguaje de programación R. Se analizaron las categorías de agua y saneamiento, clima, calidad del aire, radiaciones electromagnéticas e intoxicaciones químicas de un corpus documental de 99 textos de los actores de la red general de conocimiento en salud pública del ONS. Se calcularon medidas de centralidad y prestigio y se graficaron redes dirigidas multicapa con Power BI. Resultados: Los actores con mayor centralidad en la red fueron: Ministerio de Salud y Protección Social, Superintendencia de Salud, Profamilia, universidades de Antioquia y La Salle, ONS, Observatorio de Salud Ambiental de Bogotá, Organización Panamericana de la Salud y Organización Mundial de la Salud. Las cinco categorías analizadas presentaron bajas centralidades de grado, y las categorías de agua y clima mostraron mayor participación de los actores (más nodos e interacciones). Conclusiones: El análisis de redes sociales permitió identificar temas relevantes de salud ambiental entre los actores de la red del ONS, además de actores clave para desarrollar espacios de interacción y gestión del conocimiento. Acorde con las limitaciones del análisis, se sugiere la inclusión de aproximaciones bibliométricas para la actualización de las interacciones de la red.

Introduction: Positive and negative interactions between the human beings and the environment have an impact on the general health of the population. Therefore, it is necessary to use knowledge management and social transformation, in order to limit exposure to environmental risk factors by creating a favorable environment for healthcare. This should be carried out through multidisciplinary and intersectorial actions, such as the collaborative work of knowledge networks. Objective: To describe the interactions between the actors within the Environmental Health Knowledge Network Colombia's National Observatory of Health (ONS acronym in Spanish), in order to promote, improve and strengthen collaboration, information exchange and planning of collaborative actions. Methodology: Cross-sectional descriptive study to analyze social interactions through text mining tools by R, programmer language. Categories analyzed: Water and sanitation, climate, air quality, electromagnetic radiation and chemical poisoning. Data source: a documentary corpus of 99 texts done by actors of Environmental Health Knowledge Network of Colombia's ONS. We calculated centrality and prestige measures. We used Power BI in order to plot multi-layered directed networks. Results: Actors with greatest centrality in the network: Ministry of Health and Social Protection, Health Superintendency, Profamilia, Antioquia and La Salle universities, National Health Observatory, Bogota's Observatory of Environmental Health, the Pan American Health Organization and the World Health Organization. The five categories analyzed provides a low centrality degree, and water and climate categories presented greater participation by actors (more nodes and links). Conclusions: Social interactions analysis provides the identification of relevant environmental health issues in Colombia and key actors in order to develop interaction spaces for knowledge management. The analysis had limitations that suggest the inclusion of bibliometric approaches for updating the interactions within the network.

Modelo predictivo de enfermedad cardiovascular basado en inteligencia artificial en la atención primaria de salud / A Predictive Model for Cardiovascular Disease based on Artificial Intelligence in Primary Health Care

Introducción: En Cuba y en el resto del mundo, las enfermedades cardiovasculares son reconocidas como un problema de salud pública mayúsculo y creciente, que provoca una alta mortalidad. Objetivo: Diseñar un modelo predictivo para estimar el riesgo de enfermedad cardiovascular basado en técnicas de inteligencia artificial. Métodos: La fuente de datos fue una cohorte prospectiva que incluyó 1633 pacientes, seguidos durante 10 años, fue utilizada la herramienta de minería de datos Weka, se emplearon técnicas de selección de atributos para obtener un subconjunto más reducido de variables significativas, para generar los modelos fueron aplicados: el algoritmo de reglas JRip y el meta algoritmo Attribute Selected Classifier, usando como clasificadores el J48 y el Multilayer Perceptron. Se compararon los modelos obtenidos y se aplicaron las métricas más usadas para clases desbalanceadas. Resultados: El atributo más significativo fue el antecedente de hipertensión arterial, seguido por el colesterol de lipoproteínas de alta densidad y de baja densidad, la proteína c reactiva de alta sensibilidad y la tensión arterial sistólica, de estos atributos se derivaron todas las reglas de predicción, los algoritmos fueron efectivos para generar el modelo, el mejor desempeño fue con el Multilayer Perceptron, con una tasa de verdaderos positivos del 95,2 por ciento un área bajo la curva ROC de 0,987 en la validación cruzada. Conclusiones: Fue diseñado un modelo predictivo mediante técnicas de inteligencia artificial, lo que constituye un valioso recurso orientado a la prevención de las enfermedades cardiovasculares en la atención primaria de salud(AU)

Introduction: In Cuba and in the rest of the world, cardiovascular diseases are recognized as a major and growing public health problem, which causes high mortality. Objective: To design a predictive model to estimate the risk of cardiovascular disease based on artificial intelligence techniques. Methods: The data source was a prospective cohort including 1633 patients, followed for 10 years. The data mining tool Weka was used and attribute selection techniques were employed to obtain a smaller subset of significant variables. To generate the models, the rule algorithm JRip and the meta-algorithm Attribute Selected Classifier were applied, using J48 and Multilayer Perceptron as classifiers. The obtained models were compared and the most used metrics for unbalanced classes were applied. Results: The most significant attribute was history of arterial hypertension, followed by high and low density lipoprotein cholesterol, high sensitivity c-reactive protein and systolic blood pressure; all the prediction rules were derived from these attributes. The algorithms were effective to generate the model. The best performance was obtained using the Multilayer Perceptron, with a true positive rate of 95.2percent and an area under the ROC curve of 0.987 in the cross validation. Conclusions: A predictive model was designed using artificial intelligence techniques; it is a valuable resource oriented to the prevention of cardiovascular diseases in primary health care(AU)

Forecasting The Risk Of Chronic Kidney Disease Using Artificial Intelligence Approaches

In this Paper With the aid of AI techniques, this study aims to predict the early detection of chronic kidney disease, also known as chronic renal disease, in diabetic patients. It then suggests a decision tree to reach specific conclusions with desired accuracy by evaluating its performance in relation to its specification and sensitivity. Methods: The behaviour of learning algorithms based on a set of data mining indicators affects the models that are produced proportionately. Predicting the future is no longer a difficult task thanks to the promises of predictive analytics in big data and the use of machine learning algorithms, especially for the health sector, which has undergone significant evolution as a result of the development of new computer technologies that gave rise to numerous fields of study research. Many initiatives are made to deal with the explosion of medical data on the one hand, and to learn meaningful information from it, forecast diseases, and anticipate treatments on the other. To extract meaningful information and aid in decision-making, researchers used all the technological advancements, including big data analytics, predictive analytics, machine learning, and learning algorithms.

Application of data mining to extract knowledge about the occurrence of fistulas after palatoplasty / Aplicação de mineração de dados para extração de conhecimento sobre ocorrência de fístulas após palatoplastia

Introduction: Data mining techniques expand access to important information for the decision-making process during health care. The objective the study proposes using data mining techniques to identify variables (surgical treatment protocols, patient characteristics, post-surgical complications) associated with fistulas after primary palatoplasty in patients with unilateral transforamen incisor cleft (UTIC). Method: A data set of 222 patients with UTIC without syndromes, operated by four surgeons with Furlow's or von Langenbeck's primary palatoplasty techniques, was analyzed for this study. Two models for detecting the outcome of surgery were induced using data mining techniques (Decision Tree and Apriori). Results: Five rules were selected from a decision tree pointing to some variables as predictors of fistulas associated with primary palatoplasty: infection, cough, hypernasality, and surgeon. Analysis of the model indicates that it correctly classifies 95.9% of occurrences between the absence and presence of fistulas. The second model indicates that the absence of post-surgical complications (infection and fever) and normal speech results (absent hypernasality, without suggestive of velopharyngeal dysfunction) are related to the absence of fistulas. Regarding surgical procedures, the Furlow technique and the Vomer flap were more frequent in patients with fistulas. Conclusion: Data mining techniques, as applied in the present study, pointed to infection and cough, hypernasality, and surgeon and surgical techniques as predictors of fistulas related to primary palatoplasty.

Introdução: As técnicas de mineração de dados ampliam o acesso a informações importantes para o processo de tomada de decisão durante os cuidados com a saúde. O objetivo do estudo propõe a utilização de técnicas de mineração de dados para identificar variáveis (protocolos de tratamento cirúrgico, características do paciente, intercorrências pós-cirúrgicas) associadas à ocorrência de fístulas após palatoplastia primária em pacientes com fissura transforame incisivo unilateral (FTIU). Método: Um conjunto de dados de 222 pacientes com FTIU sem síndromes, operados por quatro cirurgiões com as técnicas de palatoplastia primária de Furlow ou von Langenbeck, foi analisado para este estudo. Dois modelos para detecção do resultado da cirurgia foram induzidos usando técnicas de mineração de dados (Árvore de Decisão e Apriori). Resultados: Cinco regras foram selecionadas de uma árvore de decisão apontando para algumas variáveis como preditivas de fístulas associadas à palatoplastia primária: infecção, tosse, hipernasalidade, cirurgião. A análise do modelo indica que ele classifica corretamente 95,9% das ocorrências entre ausência e presença de fístulas. O segundo modelo indica que a ausência de intercorrências pós-cirúrgicas (infecção e febre) e resultado de fala normal (hipernasalidade ausente, sem sugestivo de disfunção velofaríngea) estão relacionados à ausência de fístulas. Em relação aos procedimentos cirúrgicos, o uso da técnica de Furlow e retalho de Vomer foram mais frequentes nos pacientes com fístulas. Conclusão: Técnicas de mineração de dados, conforme aplicadas no presente estudo, apontaram para infecção e tosse, presença de hipernasalidade, cirurgião e técnica cirúrgica como preditores de fístulas relacionadas à palatoplastia primária.

Design of early warning system for mental health problems based on data mining and database / Projeto de sistema de alerta precoce para problemas de saúde mental com base na mineração de dados e banco de dados / Diseño de un sistema de alerta temprana de problemas de salud mental basado en la minería de datos y en la base de datos

ABSTRACT Introduction: Data mining technology is mainly employed in the era of big data to evaluate the acquired information. Subsequently, reasoning about the data inductively is fully automated to discover possible patterns. Objective: Recently, data mining technology in the national mental health database has deepened and can be effectively used to solve various mental health early warning problems. Methods: For example, it can be applied to mine psychological data and extract the most important features and information. Results: This paper presents the design of an early warning system for mental health problems based on data mining techniques to offer some thoughts on early warning of mental health problems, including data preparation, data mining, results in analysis, and decision tree algorithm. Conclusion: The experimental results indicate that the results of the early warning system in this paper can achieve an accuracy rate of more than 96% with a high accuracy rate. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução: A tecnologia de mineração de dados é empregada principalmente na era da big data para avaliar as informações adquiridas. Posteriormente, raciocinar indutivamente sobre os dados de forma totalmente automatizada para descobrir possíveis padrões. Objetivo: Recentemente, a tecnologia de mineração de dados no banco de dados nacional de saúde mental tem se aprofundado e pode ser efetivamente utilizada para resolver vários problemas de alerta precoce da saúde mental. Métodos: Por exemplo, ela pode ser aplicada para a mineração de dados psicológicos e extrair as características e informações mais importantes. Resultados: Este documento apresenta o projeto de um sistema de alerta precoce para problemas de saúde mental baseado em técnicas de mineração de dados, com o objetivo de oferecer algumas reflexões sobre alerta precoce de problemas de saúde mental, incluindo preparação de dados, mineração de dados, análise de resultados e algoritmo de árvore de decisão. Conclusão: Os resultados experimentais indicam que os resultados do sistema de alerta precoce neste trabalho podem alcançar uma taxa de precisão de mais de 96% com uma alta taxa de precisão. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

Resumen Introducción: La tecnología de minería de datos se emplea principalmente en la era de la big data para evaluar la información adquirida. Posteriormente, razonar inductivamente sobre los datos de forma totalmente automatizada para descubrir posibles patrones. Objetivo: Recientemente, la tecnología de minería de datos en la base de datos nacional de salud mental se ha profundizado y puede ser utilizada eficazmente para resolver varios problemas de alerta temprana de salud mental. Métodos: Por ejemplo, puede aplicarse para minar datos psicológicos y extraer las características e información más importantes. Resultados: Este trabajo presenta el diseño de un sistema de alerta temprana de problemas de salud mental basado en técnicas de minería de datos, con el objetivo de ofrecer algunas reflexiones sobre la alerta temprana de problemas de salud mental, incluyendo la preparación de los datos, la minería de datos, el análisis de los resultados y el algoritmo de árbol de decisión. Conclusión: Los resultados experimentales indican que los resultados del sistema de alerta temprana de este documento pueden alcanzar un índice de precisión superior al 96% con un alto índice de precisión. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Application analysis of digital neural network-based data mining method in maximizing the performance of sports training / Análise de aplicação do método de mineração de dados baseado em rede neural digital na maximização do desempenho do treinamento esportivo / Análisis de la aplicación del método de minería de datos basado en redes neuronales digitales para maximizar el rendimiento del entrenamiento deportivo

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes' training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes' explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes' training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

TCM Treatment Rule of Lung Cancer in Hubei Cancer Hospital Based on Data Mining and Complex Network Analysis / 肿瘤防治研究

Objective To explore the medication for lung cancer treatment at Hubei Cancer Hospital. Methods The electronic cases of 3 234 hospitalized lung cancer patients in Hubei Cancer Hospital were collected. The medication of the Peiyuan Guben method in treating lung cancer was analyzed with cluster and association analyses by the Apriori association rule algorithm in IBM SPSS modeler 18.0 and Qihuang data AI workstation software system. Results In the 11 293 pieces of traditional Chinese medicine used in 3 234 patients with lung cancer, the core prescription of 16 core drugs were Astragalus, Coix seed, Poria, Coke hawthorn, Ligustrum lucidum, Wolfberry, Bran fried atractylodes, Mushroom stem, Bran fried citrus husk, Bittersweet herb, Centipede, Rhodiola, Semiaquilegia adoxoides, Seaweed, Prunella and Pseudobulbus cremastrae seu pleiones. Conclusion "Peiyuan Guben" is an effective method of traditional Chinese medicine in treating lung cancer. Traditional Chinese medicine with the effects of Yiqi Jianpi and Bushen Yijing has an evident curative effect in treating lung cancer and can improve patients' quality of life.

Analysis of medication rules of TCM for perimenopausal syndrome based on literature mining / 药学实践杂志

Objective To investigate the rules of Traditional Chinese Medicine in the treatment of perimenopausal syndrome (PMS) and provide a theoretical basis for the clinical treatment of PMS. Methods The literature related to PMS were collected from China Knowledge Network (CNKI), Wanfang database and Weipu database in the past 20 years, the herbal compound prescriptions for the treatment of PMS were screened and a relevant database were established and analyzed by SPSS. Results The relevant literatures contains 184 Chinese medicine prescriptions/proprietary Chinese medicines, 122 flavors of traditional Chinese medicines, and the drug categories were mainly tonic drugs, antipyretic drugs, astringent drugs, and tranquilizers. The core single-flavor Chinese medicines were Baishao(Radix Paeoniae Alba), Shudihuang(Rehmannia glutinosa ), Danggui(Radix Angelicae Sinensis), Fuling (Indian Buead). The property and flavor covered sweet, bitter, cold, etc.; and the channel tropism belonged to the liver, kidneys, heart, lungs, spleen and meridians. The cluster analysis of high-frequency Traditional Chinese Medicine obtained two main combinations. Conclusion TCM treatment of PMS focused on replenishing qi, soothing the liver, nourishing the kidneys, nourishing blood and calming the heart, and then according to clinical compatibility with drugs such as soothing the nerves, clearing heat and removing dampness; most of its clinical treatment were Xiaoyaosan, Liuwei Dihuang pills, and Zhibo Rehmanniae decoction and other prescriptions which were added and subtracted.

Analysis of Medication Rules of Traditional Chinese Medicine for Malaria Treatment Based on Data Mining / 中国医学科学杂志(英文版)

Objective To analyze the medication rules of traditional Chinese medicine (TCM) for malaria treatment.Methods Statistical analysis was conducted on the basic attributes of TCM drugs with regard to property, therapeutic methods, flavor, and meridian tropism. A complex network of TCM drug associations was constructed. Cluster analysis was applied to obtain the core drugs for malaria treatment. The Apriori algorithm was applied to analyze the association rules of these core drugs.Results A total of 357 herbs were used 3,194 times in 461 prescriptions for malaria treatment. Radix Glycyrrhizae (), Rhizoma Pinelliae (), Radix Bupleuri (), and Radix Dichroae () were the frequently used herbs through supplementing, exterior-releasing, heat-clearing, qi-rectifying, and damp-resolving therapeutic methods. Such herbs had warm, natural, and cold herbal properties; pungent, bitter, and sweet flavors; and spleen, lung, and stomach meridian tropisms. Cluster analysis showed 61 core drugs, including Radix Glycyrrhizae, Rhizoma Pinelliae, Radix Bupleuri, and Radix Scutellariae (). Apriori association rule analysis yielded 12 binomial rules (herb pairs) and 6 trinomial rules (herb combinations). Radix Bupleuri plus Radix Scutellariae was the core herbal pair for treating malaria. This pair could be combined with Rhizoma Atractylodis Macrocephalae () for treating warm or cold malaria, combined with Pericarpium Citri Reticulatae () or Radix Dichroae () for treating miasmic malaria, or combined with turtle shells () for treating malaria with splenomegaly.Conclusions TCM can be used to classify and treat malaria in accordance with the different stages of development. As the core herbal pair, Radix Bupleuri and Radix Scutellariae can be combined with other drugs to treat malaria with different syndrome types.

Medication rules of Chinese herbal compound prescriptions for treating angina in national patent database based on multiple data mining / 中国中药杂志

This study explored the medication rules of Chinese herbal compound prescriptions for the treatment of angina based on the Chinese herbal compound patents in the patent database of the China National Intellectual Property Administration. The data of eligible Chinese herbal compound patents for the treatment of angina were collected from the patent database of the China National Intellectual Property Administration from database inception to November 10, 2022, and subjected to data modeling, analysis of main syndromes, medication frequency analysis, cluster analysis, association rule analysis, and data visualization by using Excel 2021, IBM SPSS Statistics 26.0, IBM SPSS Modeler 18.0, Cytoscape 3.9.1, and Rstudio R 4.2.2.2 to explore the medication rules for angina. The study included 636 pieces of patent data for angina that met the inclusion criteria, involving 815 drugs, with a total frequency of 6 586. The most common main syndromes were blood stasis obstructing the heart syndrome(222, 34.91%) and Qi deficiency and blood stasis syndrome(112, 17.61%). The top 10 most frequently used drugs were Salviae Miltiorrhizae Radix et Rhizoma, Chuanxiong Rhizoma, Notoginseng Radix et Rhizoma, Astragali Radix, Angelicae Sinensis Radix, Carthami Flos, Glycyrrhizae Radix et Rhizoma, Ginseng Radix et Rhizoma, Borneolum Syntheticum, and Corydalis Rhizoma. High-frequency drugs included blood-activating and stasis-resolving drugs(1 197, 18.17%) and deficiency-tonifying drugs(809, 12.28%). Cluster analysis identified eight drug combinations, including five new prescriptions suitable for clinical use and new drug development, and three drug pairs. The core drug combination of Salviae Miltiorrhizae Radix et Rhizoma-Chuanxiong Rhizoma-Carthami Flos was identified through the complex co-occurrence network analysis of Chinese medicines. Association rule analysis yielded a total of 17 rules, including 13 drug pairs and 4 tripartite combinations. Common drug pairs included Salviae Miltiorrhizae Radix et Rhizoma-Chuanxiong Rhizoma(support degree 25.79%, confidence coefficient 69.49%, lift 1.30) and Salviae Miltiorrhizae Radix et Rhizoma-Notoginseng Radix et Rhizoma(support degree 22.01%, confidence coefficient 61.95%, lift 1.16). Common tripartite combinations included Salviae Miltiorrhizae Radix et Rhizoma-Chuanxiong Rhizoma-Astragali Radix(support degree 10.85%, confidence coefficient 73.40%, lift 1.37) and Salviae Miltiorrhizae Radix et Rhizoma-Chuanxiong Rhizoma-Notoginseng Radix et Rhizoma(support degree 10.69%, confidence coefficient 79.07%, lift 1.48). The results showed that the underlying pathogenesis of angina involved blood stasis obstructing the heart and Qi deficiency and blood stasis. The overall nature of the disease was characterized as asthenia in origin and sthenia in superficiality. In the prescription formulation, blood-activating and stasis-resolving drugs, such as Salviae Miltiorrhizae Radix et Rhizoma, Chuanxiong Rhizoma, and Carthami Flos were often used to resolve the excess manifestation, which were combined with tonifying drugs such as Astragali Radix, Angelicae Sinensis Radix, Glycyrrhizae Radix et Rhizoma, and Ginseng Radix et Rhizoma to reinforce the deficiency. The syndrome, pathogenesis, disease nature, and medication were consistent with clinical practice. Additionally, the new compound prescriptions and drug combinations derived from the multiple data mining in this study could provide references and insights for the clinical diagnosis and treatment of angina and the development of new drugs.

Prescription and medication rules of traditional Chinese medicine for prevention and treatment of diabetic microangiopathy based on literature mining / 中国中药杂志

This study explored the prescription and medication rules of traditional Chinese medicine(TCM) in the prevention and treatment of diabetic microangiopathy based on literature mining. Relevant literature on TCM against diabetic microangiopathy was searched and prescriptions were collected. Microsoft Excel 2021 software was used to establish a prescription database, and an analysis was conducted on the frequency, properties, flavors, meridian tropism, and efficacy classifications of drugs. Association rule analysis, cluster analysis, and factor analysis were performed using SPSS Modeler 18.0 and SPSS Statistics 26.0 software. The characteristic active components and mechanisms of action of medium-high frequency drugs in the analysis of medication rules were explored through li-terature mining. A total of 1 327 prescriptions were included in this study, involving 411 drugs, with a total frequency reaching 19 154 times. The top five high-frequency drugs were Astragali Radix, Angelicae Sinensis Radix, Poria, Salviae Miltiorrhizae Radix et Rhizoma, and Rehmanniae Radix. The cold and warm drugs were used in combination. Drugs were mainly sweet, followed by bitter and pungent, and acted on the liver meridian. The majority of drugs were effective in tonifying deficiency, clearing heat, activating blood, and resolving stasis. Association rule analysis identified the highly supported drug pair of Astragali Radix-Angelicae Sinensis Radix and the highly confident drug combination of Poria-Alismatis Rhizoma-Corni Fructus. The strongest correlation was found among Astragali Radix, Angelicae Sinensis Radix, Poria, and Salviae Miltiorrhizae Radix et Rhizoma through the complex network analysis. Cluster analysis identified nine categories of drug combinations, while factor analysis identified 16 common factors. The analysis of active components in high-frequency drugs for the treatment of diabetic microangiopathy revealed that these effective components mainly exerted their effects by inhibiting oxidative stress and suppressing inflammatory reactions. The study found that the pathogenesis of diabetic microangiopathy was primarily characterized by deficiency in origin, with a combination of deficiency and excess. Deficiency was manifested as Qi deficiency and blood deficiency, while excess as phlegm-heat and blood stasis. The key organ involved in the pathological changes was the liver. The treatment mainly focused on supplementing Qi and nourishing blood, supplemented by clearing heat, coo-ling blood, activating blood, and dredging collaterals. Commonly used formulas included Danggui Buxue Decoction, Liuwei Dihuang Pills, Erzhi Pills, and Buyang Huanwu Decoction. The mechanisms of action of high-frequency drugs in the treatment of diabetic microangiopathy were often related to the inhibition of oxidative stress and suppression of inflammatory reactions. These findings can provide references for the clinical treatment of diabetic microangiopathy and the development of targeted drugs.

Analysis and prospects of common problems in clinical data mining of traditional Chinese medicine prescriptions / 中国中药杂志

Mining data from traditional Chinese medicine(TCM) prescriptions is one of the important methods for inheriting the experience of famous doctors and developing new drugs. However, current research work has problems such as to be optimized research plans and non-standard statistics. The main problems and corresponding solutions summarized by the research mainly include four aspects.(1)The research plan design needs to consider the efficacy and quality of individual cases.(2)The significance of the difference in confidence order of association rules needs to be further considered, and the lift should not be ignored.(3)The clustering analysis steps are complex. The selection of clustering variables should comprehensively consider factors such as the frequency of TCM, network topology parameters, and practical application significance. The selection of distance calculation and clustering methods should be improved based on the characteristics of TCM clinical data. Jaccard distance and its improvement plan should be given attention in the future. A single, unexplained clustering result should not be presented, but the final clustering plan should be selected based on a comprehensive consideration of TCM clinical characteristics and objective evaluation indicators for clustering.(4)When calculating correlation coefficients, algorithms that are only suitable for continuous variables should not be applied to binary variables. This article explained the connotations of the above problems based on the characteristics of TCM clinical research and statistical principles and proposed corresponding suggestions to provide important references for future data mining research work.

Medication Rules and Academic Experience of Professor WANG Xingkuan in Treatment of Chest Stuffiness and Pain Based on Data Mining / 中国实验方剂学杂志

ObjectiveTo study the medication rules of Professor. WANG Xingkuan and inherit his academic experience in the treatment of chest stuffiness and pain with the aid of the Traditional Chinese Medicine Inheritance Computing Platform V3.0 （TCMICS V3.0）. MethodThe original medical records of patients with angina pectoris in coronary heart disease （CHD） diagnosed and treated by Prof. WANG in the outpatient department of Hunan University of Chinese Medicine from 2017 to 2020 were collected and entered into the TCMICS V3.0. The rules of prescriptions and drugs were analyzed by the software. ResultA total of 1 044 prescriptions of Prof. WANG for the treatment of chest stuffiness and pain were collected. Most of the drugs were sweet and bitter in flavor and mainly acted on the lung meridian， followed by heart， spleen， liver， stomach， and kidney meridians. Among the prescriptions， Shengmaisan was the most commonly used classic prescription， and Xintongling No. Ⅲ was the top experienced prescription. High-frequency drugs mainly included Ophiopogonis Radix， Pinelliae Rhizoma， Salviae Miltiorrhizae Radix et Rhizoma， Trichosanthis Pericarpium， Coptidis Rhizoma， Schisandrae Chinensis Fructus， and Bupleuri Radix. The common doses of drugs were 3， 5， 10， and 15 g. The analysis of formulation rules revealed 129 combinations of common drugs， 58 combinations with confidence > 0.99， and the core drugs of common syndromes. Six core drug combinations were obtained by drug clustering. ConclusionProfessor WANG treats chest stuffiness and pain based on syndrome differentiation following the principles of benefiting Qi， nourishing Yin， eliminating phlegm， resolving stasis， soothing liver， and promoting bile secretion， reflecting his academic idea of "regulation of multiple organs and comprehensive treatment". The core prescriptions can be used for reference by clinical practitioners， but further clinical and experimental studies are still needed to verify their efficacy.

Signal detection and analysis of the adverse events of ustekinumab based on OpenFDA database / 中国药房

OBJECTIVE To analyze the risk of adverse drug reaction of ustekinumab， so as to provide reference for rational drug use in clinic. METHODS The adverse events （AE） reports related to ustekinumab included in the FDA public data program （OpenFDA） database were analyzed after marketing （from September 25th 2009 to December 30th 2021）. The risk signals were mined for top 100 AE by the method of reporting odds ratio （ROR） and proportional reporting ratio （PRR）. RESULTS A total of 62 356 AE reports related to ustekinumab were retrieved， male patients （51.79%） were more than female patients （39.51%）. Results of ROR method and PRR method showed that 31 suspicious signals were mined， mainly infections and infectious diseases （9 kinds）， general disorders and administration site conditions （5 kinds）， skin and subcutaneous tissue disorders diseases （4 kinds）， musculoskeletal and connective tissue disorders （4 kinds）， etc. Fourteen suspicious signals were not included in the instructions， such as hepatic enzyme increase， basal cell carcinoma， pericarditis， pemphigus， hair loss， synovitis， glossodynia， etc. CONCLUSIONS During clinical dosing of ustekinumab， in addition to ADR mentioned in package inserts， great attention should be paid to the patient’s liver function， skin status， hair loss and cardiovascular-related risks，which is helpful to discover AE early and ensure the safety medication of patients.

ADE signal mining and analysis of axitinib based on FAERS database / 中国药房

OBJECTIVE To provide references for the clinical safe use of axitinib. METHODS Adverse drug event （ADE） data for axitinib were collected from the US FDA Adverse Event Reporting System （FAERS） database from the first quarter of 2012 to the fourth quarter of 2022. The data were mined and analyzed by utilizing the ratio-of-reporting-ratio （ROR） method and comprehensive standard method of the United Kingdom’s Medicines and Healthcare Products Regulatory Agency （MHRA） of proportional imbalance measurement. RESULTS A total of 13 962 reports of axitinib-related ADEs were obtained， with patients’ age concentrated in 65-85 years （43.25%）， gender predominantly male （65.23%）， country of reporting predominantly US （60.01%）， and serious ADE outcomes mostly hospitalization or prolonged hospitalization （31.51%）. A total of 172 ADE risk signals were detected， involving 18 system and organ classifications （SOC）， mainly systemic diseases and various reactions at the site of administration （3 749 cases， 30.84%） and gastrointestinal system diseases （2 067 cases， 17.00%）. ADE risk signals that occurred more frequently were generally consistent with the drug instruction， such as diarrhea， fatigue， and hypertension； new ADE risk signals requiring clinical attention were death， immune-mediated nephritis， and PT signals contained in the SOC of various benign， malignant， and tumors of undetermined nature （including cysts and polyps）. CONCLUSIONS For ADEs that occur frequently with axitinib and are already contained in the drug instruction （e.g. hypertension， diarrhea）， they should be adequately evaluated before administration， especially for patients with combined use of immune checkpoint inhibitors and patients with underlying hypertension； for ADEs with stronger signals and newer ADEs （e. g. death， disease progression， tumor progression）， the patient’s disease progression should be closely monitored during the treatment period for potentially fatal ADEs； for its rare ADEs （e. g.immune-mediated nephritis， scrotal ulcer， non-infectious encephalitis）， clinical validation should be further strengthened.

Evaluation of Animal Models of Acute Lung Injury Caused by Non-physical Factors Based on Data Mining / 中国实验方剂学杂志

ObjectiveTo study the characteristics of animal models of acute lung injury caused by non-physical factors， so as to provide a reference for the standardization of the preparation of such animal models and lay a foundation for the research on the pathogenesis and the diagnosis and treatment of acute lung injury. MethodThe articles about the animal experiments of acute lung injury published in the last decade were retrieved from China National Knowledge Infrastructure （CNKI）， Wanfang， SinoMed， VIP， and PubMed with the theme terms of "acute lung injury" and "animal model". The animal species， drugs used in modeling， modeling period， methods used in molding， model standards， and model evaluation indicators were summarized， and Excel was used for the frequency analysis. ResultA total of 338 articles were included in this study. The results of the frequency analysis showed that SD rats/C57BL/6 mice were mainly used to establish the animal models of acute lung injury. Male mice were mostly used for modeling， and the commonly used modeling agent was lipopolysaccharides （LPS）. In most cases， the modeling lasted for 6 h after drug administration. Hematoxylin-eosin staining was mainly used for the observation of histological changes in the lungs， which were taken as the criteria for modeling. The established models were mainly evaluated based on lung dry/wet weight ratio， lung index， morphological changes in the lung tissue， myeloperoxidase （MPO）， superoxide dismutase （SOD）， and levels of inflammatory cytokines in the serum and bronchoalveolar lavage fluid （BALF）. ConclusionThe models of acute lung injury were mostly prepared by intraperitoneal injection of LPS （5 mg·kg-1） in SD rats and tracheal instillation of LPS （5 mg·kg-1） in C57BL/6 mice， which were praised for the simple operation， high success rate， and consistent with the pathogenesis of acute lung injury. This study provides a reference for the basic research on acute lung injury by animal experiments.

Mining and analysis for adverse drug event signals of liver failure in underage population based on the FAERS database / 中国药房

OBJECTIVE To conduct data mining on drugs causing liver failure in underage populations based on the FDA Adverse Event Reporting System （FAERS） database， so as to provide reference for clinical use of related drugs. METHODS The data on reported adverse drug event （ADE） of liver failure in this population （under 18 years old） from the first quarter of 2013 to the third quarter of 2022 were retrieved from the FAERS database for mining and analysis； they were divided into infants（≤1 year old）， young children（＞1-＜6 years old）， children（6-＜12 years old） and adolescents（12-＜18 years old） according to the age. The reporting odds ratio （ROR）， proportional reporting ratio and Bayesian confidence propagation neural network of the proportional imbalance method were used to screen ADE signals. RESULTS A total of 1 051 ADE reports of liver failure were collected from the underage population involving 60 drugs. The highest incidence was found in adolescents （410 cases， 39.01%）， followed by young children （297 cases， 28.26%）. The instructions of 14 drugs did not mention hepatobiliary system injury and liver failure risk， including 31 cases of levetiracetam （2.95%），18 cases of metronidazole （1.71%）， 16 cases of each of topiramate and methylprednisolone （1.52% each）， 12 cases of dexamethasone （1.14%）， 11 cases of tisagenlecleucel （1.05%）， 10 cases of each of ferrous sulfate， metformin and busulfan （0.95% each）， 9 cases of propofol （0.86%）， 8 cases of onasemnogene abeparvovec （0.76%）， 5 cases of each of diphenhydramine and omeprazole （0.48% each）， 4 cases of sebeliesterase α （0.38%）， totaling 165 cases， accounting for 15.70% of the total reported cases. Metformin was contrary to the known liver safety， and E-mail：libingchemical@163.com metronidazole and levetiracetam were new risk signals， which caused more serious clinical outcomes. CONCLUSIONS Fourteen new pharmacovigilance signals which cause liver failure in the underage population are found in this study； the liver function of patients should be closely monitored when using these drugs. Among those drugs， metformin neither undergoes liver metabolism nor has been reported in the relevant literature， and the liver-related ADE caused by metformin deserves further attention. The clinical outcomes caused by metronidazole and levetiracetam are relatively serious and need to be given sufficient attention.