This study aims to explore the medication rules and mechanisms of treating chronic renal failure(CRF) by Jinling medical school based on data mining, network pharmacology, and experimental validation systematically and deeply. Firstly, the study selected the papers published by the inherited clinicians in Jinling medical school in Chinese journals using the subject headings named "traditional Chinese medicine(TCM) + chronic renal failure", "TCM + chronic renal inefficiency", or "TCM + consumptive disease" in China National Knowledge Infrastructure, Wanfang, and VIP Chinese Science and Technology Periodical Database and screened TCM formulas for treating CRF according to inclusion and exclusion criteria. The study analyzed the frequency of use of single TCM and the four properties, five tastes, channel tropism, and efficacy of TCM used with high frequency and performed association rule and clustering analysis, respectively. As a result, a total of 215 TCM formulas and 235 different single TCM were screened, respectively. The TCM used with high frequency included Astragali Radix, Rhei Radix et Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma, Poria, and Atractylodis Macrocephalae Rhizoma(top 5). The single TCM characterized by "cold properties, sweet flavor, and restoring spleen channel" and the TCM with the efficacy of tonifying deficiency had the highest frequency of use, respectively. Then, the TCM with the rules of "blood-activating and stasis-removing" and "diuretic and dampness-penetrating" appeared. In addition, the core combination of TCM [(Hexin Formula, HXF)] included "Astragali Radix, Rhei Radix et Rhizoma, Poria, Salviae Miltiorrhizae Radix, and Angelicae Sinensis Radix". The network pharmacology analysis showed that HXF had 91 active compounds and 250 corresponding protein targets including prostaglandin-endoperoxide synthase 2(PTGS2), PTGS1, sodium voltage-gated channel alpha subunit 5(SCN5A), cholinergic receptor muscarinic 1(CHRM1), and heat shock protein 90 alpha family class A member 1(HSP90AA1)(top 5). Gene Ontology(GO) function analysis revealed that the core targets of HXF predominantly affected biological processes, cellular components, and molecular functions such as positive regulation of transcription by ribonucleic acid polymerase Ⅱ and DNA template transcription, formation of cytosol, nucleus, and plasma membrane, and identical protein binding and enzyme binding. Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis revealed that CRF-related genes were involved in a variety of signaling pathways and cellular metabolic pathways, primarily involving "phosphatidylinositol 3-kinase(PI3K)-protein kinase B(Akt) pathway" and "advanced glycation end products-receptor for advanced glycation end products". Molecular docking results showed that the active components in HXF such as isomucronulatol 7-O-glucoside, betulinic acid, sitosterol, and przewaquinone B might be crucial in the treatment of CRF. Finally, a modified rat model with renal failure induced by adenine was used, and the in vivo experimental confirmation was performed based on the above-mentioned predictions. The results verify that HXF can regulate mitochondrial autophagy in the kidneys and the PI3K-Akt-mammalian target of rapamycin(mTOR) signaling pathway activation at upstream, so as to alleviate renal tubulointerstitial fibrosis and then delay the progression of CRF.
Data Mining ; Drugs, Chinese Herbal/chemistry* ; Network Pharmacology ; Humans ; Kidney Failure, Chronic/metabolism* ; Medicine, Chinese Traditional ; China

This study explored the prescription pattern of traditional Chinese medicine(TCM) in the treatment of hypertensive left ventricular hypertrophy(LVH), so as to provide a relevant theoretical basis for the clinical diagnosis and treatment of hypertensive LVH. The study systematically searched the databases of CNKI, Wanfang, VIP, and SinoMed to screen out the qualified literature on TCM treatment of hypertensive LVH and used Microsoft Excel 2021 to establish the relevant prescription database. It also counted the frequency, property, flavor, and meridian affiliation of TCM in the prescriptions and classified their efficacy. The study used Lantern 5.0 and Rstudio software to analyze the hidden structural models and association rules of the high-frequency TCM with a frequency of >3.50% and adopted Origin 2024 software to visualize the data, so as to explore the prescription pattern of TCM in treating hypertensive LVH. The results showed that a total of 128 TCM prescriptions were included, involving 163 TCM with a total frequency of 1 242. The high-frequency TCM included Salviae Miltiorrhizae Radix et Rhizoma, Uncariae Ramulus Cum Uncis, Gastrodiae Rhizoma, Poria, and Chuanxiong Rhizoma, with the main efficacy from blood-activating and stasis-resolving herbs, tonic herbs, and liver-calming and wind-extinguishing herbs. The latent structure analysis(LSA) identified 10 latent variables, 20 latent classes, 7 comprehensive clustering models, and 23 core prescriptions. It was speculated that the common syndromes of hypertensive LVH included blood stasis obstructing the collaterals, ascending hyperactivity of liver Yang, Yin deficiency with Yang hyperactivity, and intermingled phlegm and blood stasis. The association rule analysis yielded 33 strong association rules, with the highest comprehensive association rule being Gastrodiae Rhizoma→Uncariae Ramulus Cum Uncis. Hypertensive LVH is characterized by asthenia in origin and asthenia in superficiality, with Yin deficiency and Qi deficiency as the origin and blood stasis and phlegm as the superficiality. Clinical treatment focuses on activating blood circulation, resolving stasis, tonifying Qi, and nourishing Yin, combined with syndrome-specific therapies such as calming wind and stopping convulsions, clearing heat, eliminating dampness and resolving phlegm, and promoting diuresis and reducing swelling.
Drugs, Chinese Herbal/therapeutic use* ; Data Mining ; Humans ; Hypertension/complications* ; Hypertrophy, Left Ventricular/physiopathology* ; Medicine, Chinese Traditional ; Drug Prescriptions

Data space, as an innovative data management and sharing model, is emerging in the medical and health sectors. This study expounds on the conceptual connotation of data space and delineates its key technologies, including distributed data storage, standardization and interoperability of data sharing, data security and privacy protection, data analysis and mining, and data space assessment. By analyzing the real-world cases of data spaces within medicine and health, this study compares the similarities and differences across various dimensions such as purpose, architecture, data interoperability, and privacy protection. Meanwhile, data spaces in these fields are challenged by the limited computing resources, the complexities of data integration, and the need for optimized algorithms. Additionally, legal and ethical issues such as unclear data ownership, undefined usage rights, risks associated with privacy protection need to be addressed. The study notes organizational and management difficulties, calling for enhancements in governance framework, data sharing mechanisms, and value assessment systems. In the future, technological innovation, sound regulations, and optimized management will help the development of the medical and health data space. These developments will enable the secure and efficient utilization of data, propelling the medical industry into an era characterized by precision, intelligence, and personalization.
Humans ; Computer Security ; Information Dissemination ; Data Management ; Information Storage and Retrieval ; Data Mining

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

To explore the differences in biological gas production in the waterlogged zone of a coal seam fire-affected area, in this study the in-situ gas production experiment was conducted with the mine water from aquitard layers in coal seam fire zones in Xinjiang. The results showed that the biogas production first increased and then decreased with the increase in distance, and the highest gas production reached 216.55 mL. The changes in key metabolic pathways during the anaerobic fermentation of coal were analyzed, which showed that as the distance from the aquitard layer in the coal seam fire zone increased, the methanogenesis pathways gradually shifted from acetic acid decarboxylation and carbon dioxide reduction to acetic acid decarboxylation and methylamine methanogenesis. The significant variability in the in-situ mine water reservoir conditions contributed to the differences. In addition, the reservoir pressure and temperature increased as the distance from the fire zone became longer, and the salinity of the farthest mine water in the reverse fault was the highest due to the lack of groundwater supply. Pearson correlation analysis revealed significant correlations of microbial communities with key functional genes and the types and concentrations of ions. The ions significantly influencing microbial enzymatic metabolic activities included Al³⁺, Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺, Mg²⁺, PO₄^3-, and Mo⁶⁺. The differences in metabolic pathways were attributed to the integrated effects of a co-occurring environment with multiple ions. The gas production simulation experiments and metagenomic analyses provide data support for the practical application of in-situ biogas experiments, laying a foundation for engineering applications.
Biofuels ; Coal ; Methane/biosynthesis* ; Fires ; Groundwater ; Coal Mining ; Fermentation ; China ; Anaerobiosis

BACKGROUND: It is inaccurate to reflect the level of dust exposure through working years. Furthermore, identifying a predictive indicator for lung function decline is significant for coal miners. The study aimed to explored whether club cell secretory protein (CC16) levels can reflect early lung function changes. METHODS: The cumulative respiratory dust exposure (CDE) levels of 1,461 coal miners were retrospectively assessed by constructed a job-exposure matrix to replace working years. Important factors affecting lung function and CC16 were selected by establishing random forest models. Subsequently, the potential of CC16 to reflect lung injury was explored from multiple perspectives. First, restricted cubic spline (RCS) models were used to compare the trends of changes in lung function indicators and plasma CC16 levels after dust exposure. Then mediating analysis was performed to investigate the role of CC16 in the association between dust exposure and lung function decline. Finally, the association between baseline CC16 levels and follow-up lung function was explored. RESULTS: The median CDE were 35.13 mg/m³-years. RCS models revealed a rapid decline in forced vital capacity (FVC), forced expiratory volume in the first second (FEV₁), and their percentages of predicted values when CDE exceeded 25 mg/m³-years. The dust exposure level (<5 mg/m³-years) causing significant changes in CC16 was much lower than the level (25 mg/m³-years) that caused changes in lung function indicators. CC16 mediated 11.1% to 26.0% of dust-related lung function decline. Additionally, workers with low baseline CC16 levels experienced greater reductions in lung function in the future. CONCLUSIONS CC16 levels are more sensitive than lung indicators in reflecting early lung function injury and plays mediating role in lung function decline induced by dust exposure. Low baseline CC16 levels predict poor future lung function.
Uteroglobin/blood* ; Humans ; Dust/analysis* ; Occupational Exposure/analysis* ; Male ; Middle Aged ; Adult ; Retrospective Studies ; Lung Injury/chemically induced* ; Coal Mining ; Biomarkers/blood* ; China/epidemiology* ; Air Pollutants, Occupational ; Female

The application of artificial intelligence (AI) technology in the field of radiation protection is gradually becoming a key force driving industrial development. This paper focuses on the applications of AI technology in radiation monitoring, shielding design, and nuclear science data mining, and prospects its future development. Through real-time monitoring and predictive analysis, AI technology has significantly improved the accuracy and efficiency of radiation monitoring, optimized the selection and configuration of shielding materials, and effectively reduced radiation exposure. Additionally, AI's data mining capabilities provide powerful tools for nuclear reactor design and optimization, promoting innovation in nuclear and radiological sciences. Despite technical challenges and ethical issues such as accuracy, data processing, and algorithmic transparency, the application prospects of AI in radiation protection remain broad. This paper emphasizes the critical role of AI technology in enhancing medical safety and efficiency, and foresees more future innovations and applications in the field of radiation protection.
Radiation Protection/methods* ; Artificial Intelligence ; Radiation Monitoring ; Humans ; Data Mining ; Algorithms

The rules of acupoint selection and treatment were identified and discovered from the collected ancient acupuncture-moxibustion prescriptions recorded the earliest for epigastric pain. The database of ancient acupuncture-moxibustion prescriptions for epigastric pain was set up using Excel2016 software. After the disease term, etiology, pathogenesis, symptoms and acupoints were normalized, the underlying multi-dimensional correlation among the elements of acupuncture-moxibustion was explored, using the frequency statistics and the association rule of Apriori algorithm. In the ancient time, in treatment with acupuncture-moxibustion therapy for epigastric pain, the acupoints of the high use frequency were sequenced as Zhongwan (CV12), Shangwan (CV13), Zusanli (ST36), Neiguan (PC6), Gongsun (SP4), Pishu (BL20) and Weishu (BL21). The common combinations of acupoints included the pairs of back-shu points, the combination of back-shu points and front-mu points, the combination of front-mu points and yuan-source points and the combination of back-shu points and the lower he-sea points. The highly involved acupoints were those from the conception vessel, pericardium meridian, spleen meridian, stomach meridian and bladder meridian; and they were commonly distributed on the abdomen, the yin parts of the foot and the arm, the yang part of the leg and on the back. Regarding the etiologies such as parasites, food retention, masses, qi stagnation and stomach cold, Zhongwan (CV12) and Shangwan (CV13) were coordinated; and Sanyinjiao (SP6) and Daling (PC7) were highly associated with masses. Besides cold injury, parasites and masses, for the epigastric pain caused by other factors of etiology (qi stagnation, stomach cold and food retention), moxibustion therapy was greatly applicable. For epigastric pain combined with qi reversion in the lower abdominal region, Qichong (ST30), Sanyinjiao (SP6), Tianshu (ST25) and Zusanli (ST36) must be selected. Dadu (SP2) and Taibai (SP3) must be used if the distention in the chest and abdomen accompanied; and Zhongzhu (TE3) be used if back pain involved. Zusanli (ST36) was commonly selected for hiccups. For the other accompanied symptoms, Zhongwan (CV12) was used, which is consistent with the acupoint selection of main symptoms. On the trunk, moxibustion was generally used at Weishu (BL21), Pishu (BL20), Geshu (BL17), Zhongwan (CV12), Juque (CV14) and Qihai (CV6), except Shangwan (CV13). Among the acupoints below the elbows and knees, moxibustion was commonly applicable at Zusanli (ST36), and acupuncture was often used at Gongsun (SP4) and Daling (PC7).
Acupuncture Points ; Humans ; Moxibustion/history* ; History, Ancient ; Acupuncture Therapy/history* ; Data Mining ; Abdominal Pain/history*

OBJECTIVE: To analyze the rules of moxibustion for low back pain by ZHOU Meisheng by using data mining and knowledge graph technology. METHODS: Taking the medical cases of moxibustion for low back pain from ZHOU Meisheng's legacy manuscripts and existing works as the research objects, information on disease types, symptoms, tongue manifestations, pulse conditions, syndrome patterns, moxibustion methods and acupoints were collected. Frequency statistics and community analysis were conducted by the ancient and modern medical record cloud platform V 2.3.7, cluster analysis of high-frequency acupoints was performed by SPSS26.0, association rule analysis of high-frequency acupoints was performed by SPSS Modeler 18.0, and the generated linked data were imported into Cytoscape 3.9.1 for complex network analysis. Knowledge graph of moxibustion for low back pain by ZHOU Meisheng was constructed based on the results of data mining. The data storage and display of knowledge graph were realized through the Neo4j 3.5.25 graph database, and the Cypher query language was used for knowledge graph retrieval and discovery. RESULTS: A total of 219 medical cases were collected, involving 14 disease types, 85 related clinical symptoms, 5 related TCM syndrome types, and 6 moxibustion methods. The acupoints were mostly attributed to the governor vessel, the bladder meridian of foot-taiyang, non-meridian and non-acupoint areas. The core prescription of acupoints derived from complex network analysis included tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34), which were largely coincides with high-frequency acupoints. Cluster analysis obtained 4 cluster combinations. Community analysis yielded 6 communities, each corresponding to different acupoints.The constructed knowledge graph contained 187 nodes and 696 relationships, by retrieving clinical elements related to low back pain, the disease-moxibustion association graph, disease-acupoint association graph, accompanying symptom-acupoint association graph and syndrome type-matching point association graph were obtained. CONCLUSION When treating low back pain with moxibustion, ZHOU Meisheng adopts the principle of promoting circulation, distinguishing diseases to determine the treatment, selecting acupoints according to the diseases, and matching points according to the symptoms.And taking tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34) as core acupoints, combined with tenderness point selection, acupoint selection based on meridian and zangfu syndrome differentiation, "sunshine area" acupoint selection, yin-yang acupoint matching. Additionally, he skillfully employs special points, such as Zhongzhu (KI15) and ear tips, pays attention to the reform of moxibustion tools, and innovates the moxibustion techniques, using distinctive moxibustion tools and methods to treat low back pain.
Moxibustion/methods* ; Humans ; Data Mining ; Low Back Pain/history* ; Acupuncture Points ; History, Ancient ; Female ; China ; Male ; Adult ; Middle Aged

OBJECTIVE: To explore the rules of acupoint selection and compatibility of acupuncture and moxibustion in treatment of chronic cough using data mining. METHODS: The ancient and modern medical record cloud platform, and the databases, i.e. CNKI, Wanfang, VIP, EMbase, Web of Science and PubMed, were searched to screen the ancient and modern literature on acupuncture and moxibustion treatment of chronic cough. The prescription database was established for acupuncture and moxibustion treatment of chronic cough, and the analysis conducted on the frequency and use percentage in the aspects of intervention measures, acupoint selection, acupoint distribution, meridian tropism, special points and acupoint combination, as well as the association rules and clustering rules of acupoint selection. The subgroup analysis was performed in accordance with the etiology of chronic cough and intervention measures. RESULTS: A total of 106 articles were included and 158 prescriptions were extracted. The intervention measures were acupuncture, moxibustion, herbal medication and the combination of several measures. The high-frequency acupoints included Feishu (BL13), Zusanli (ST36), Dazhui (GV14), Pishu (BL20), Danzhong (CV17), Shenshu (BL23), Lieque (LU7), Dingchuan (EX-B1), Tiantu (CV22), and Fenglong (ST40). These acupoints are mainly distributed on the back, lumbar region, chest and abdomen. The involved meridians were bladder meridian of foot-taiyang, conception vessel, and lung meridian of hand-taiyin. The special points covered back-shu points, crossing points and five-shu point. Regarding the compatibility of acupoints, the combination of upper and lower points, and the combination of front and back points were predominant in treatment. The analysis of association rules found that the support of Feishu (BL13)→Zusanli (ST36) was the highest; the cluster analysis obtained 8 clusters of acupoints. The acupoint compatibility and overall rules were similar when cough variant asthma (CVA) or the mixed reasons were involved, and the local treatment approach was adopted if the etiology of disease was related to upper airway cough syndrome (UACS) and gastroesophageal reflux cough (GERC). The acupoint selection was similar among different intervention measures. When two kinds of measures were combined in treatment, Feishu (BL13), Pishu (BL20) and Zusanli (ST36) were the most common. CONCLUSION In treatment with acupuncture and moxibustion for chronic cough, the acupoints are selected on the affected local area, depending on syndrome differentiation, and focusing on back-shu points. The main acupoints are Feishu (BL13), Zusanli (ST36), Dazhui (GV14), Pishu (BL20), Danzhong (CV17) and Shenshu (BL23). The combined therapy is dominant with acupuncture, moxibustion and herbal medicine involved.
Acupuncture Points ; Moxibustion/history* ; Humans ; Cough/history* ; Acupuncture Therapy/history* ; Chronic Disease/therapy* ; Data Mining ; History, Ancient ; Meridians ; Chronic Cough