Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network.

Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages.

Establishment-level safety analytics: a scoping review.

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

A real­world pharmacovigilance study of FDA adverse event reporting system events for daratumumab.

BACKGROUND: Daratumumab, a first-in-class humanized IgG1κ monoclonal antibody that targets the CD38 epitope, has been approved for treatment of multiple myeloma by FDA. The current study was to evaluate daratumumab-related adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). RESEARCH DESIGN AND METHODS: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were employed to quantify the signals of daratumumab-associated AEs. RESULTS: Out of 10,378,816 reports collected from the FAERS database, 8727 reports of daratumumab-associated AEs were identified. A total of 183 significant disproportionality preferred terms (PTs) were retained. Unexpected significant AEs such as meningitis aseptic, leukoencephalopathy, tumor lysis syndrome, disseminated intravascular coagulation, hyperviscosity syndrome, sudden hearing loss, ileus and diverticular perforation were also detected. The median onset time of daratumumab-related AEs was 11 days (interquartile range [IQR] 0-76 days), and most of the cases occurred within 30 days. CONCLUSION: Our study found potential new and unexpected AEs signals for daratumumab, suggesting prospective clinical studies are needed to confirm these results and illustrate their relationship.

Lactate dehydrogenase to albumin ratio is associated with in-hospital mortality in patients with acute heart failure: Data from the MIMIC-III database.

The effect of the lactate dehydrogenase to albumin ratio (LAR) on the survival of patients with acute heart failure (AHF) is unclear. We aimed to analyze the impact of LAR on survival in patients with AHF. We retrieved eligible patients for our study from the Monitoring in Intensive Care Database III. For each patient in our study, we gathered clinical data and demographic information. We conducted multivariate logistic regression modeling and smooth curve fitting to assess whether the LAR score could be used as an independent indicator for predicting the prognosis of AHF patients. A total of 2,177 patients were extracted from the database. Survivors had an average age of 69.88, whereas nonsurvivors had an average age of 71.95. The survivor group had a mean LAR ratio of 13.44, and the nonsurvivor group had a value of 17.38. LAR and in-hospital mortality had a nearly linear correlation, according to smooth curve fitting (P < 0.001). According to multivariate logistic regression, the LAR may be an independent risk factor in predicting the prognosis of patients with AHF (odd ratio = 1.09; P < 0.001). The LAR ratio is an independent risk factor associated with increased in-hospital mortality rates in patients with AHF.

Exploration of the molecular mechanism guiding Xinfeng capsule regulatory mechanism for rheumatoid arthritis inflammation.

OBJECTIVES: Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joint synovium. The traditional Chinese medicine Xinfeng capsule (XFC) has a remarkable alleviating effect on inflammatory symptoms, such as joint pain and swelling, in patients with RA. However, the underlying mechanism of action remains to be elucidated. This study intended to conduct network pharmacology, animal experiments, data mining, and molecular docking to explore the molecular mechanism through which XFC can improve the inflammatory symptoms of RA. METHODS: The Apriori association rules and a random walk model were employed to evaluate the effect of XFC on the clinical inflammatory indexes of RA. The active ingredients and the potential target genes of XFC were obtained from public databases. Based on the search tool for recurring instances of neighboring genes (STRING) database, the Database for Annotation, Visualization and Integrated Discovery (DAVID) database, Cytoscape software, and molecular docking method, the molecular mechanism by which XFC acts on RA was also analyzed. Finally, an adjuvant arthritis rat model was established to verify the effects of XFC on inflammation-related signaling pathways and inflammatory factors. RESULTS: XFC significantly reduced the level of C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and the erythrocyte sedimentation rate (ESR). The docking space structures of the active ingredients in XFC, namely triptolide and quercetin, and the key targets were stable. Inflammation-related biological processes were identified as the key factors involved in the development of RA, and the regulation of the toll-like receptor (TLR) signaling pathway may be the key link for XFC toward improving the inflammatory state of RA. The expression levels of toll-like receptor 4 (TLR4), myeloid differentiation primary response protein MyD88 (MyD88), interleukin-1 receptor-associated kinase 1 (IRAK1), TNF receptor-associated factor 6 (TRAF6), TGF-beta-activated kinase 1 (TAK1), phospho-Inhibitor of NF-κB kinaseß (p-IKKß), phospho-Nuclear factor-k-gene binding (p-NF-κB), and interleukin-1ß (IL-1ß) can all be decreased by XFC. XFC improves joint inflammation symptoms by lowering pro-inflammatory factors tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interferon-γ (INF-γ) levels. CONCLUSIONS: XFC could effectively improve the clinical inflammatory indexes of RA. The active ingredients of XFC improved the inflammatory state of RA by regulating the TLR-signaling pathway.

Post-marketing safety concerns with palbociclib: a disproportionality analysis of the FDA adverse event reporting system.

OBJECTIVES: To explore the association between palbociclib and related adverse events (AEs) in the real world through U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: The signal strength of palbociclib-related AEs was done by disproportionality analysis. Clinical priority of palbociclib-related AEs was scored and ranked by assessing five different features. Outcome analysis, time to onset analysis, dose-report /AEs number analysis, and stratification analysis were all performed. RESULTS: There were 61,821 'primary suspected (PS)' reports of palbociclib and 195,616 AEs associated with palbociclib. The four algorithms simultaneously detected 18 positive signals at the SOC level, and 65 positive signals at the PT level. Bone marrow failure, neuropathy, peripheral, pleural effusion, myelosuppression, pulmonary edema, and pulmonary thrombosis were also found to have positive signals. Gender (female vs male, χ2 = 5.287, p = 0.022) and age showed significant differences in serious and non-serious reports. Palbociclib-related AEs had a median onset time of 79 days (interquartile range [IQR] 20-264 days). CONCLUSIONS: The study identified potential Palbociclib-related AEs and offered warnings for special AEs, providing further data for palbociclib safety studies in breast cancer patients. Nonetheless, prospective clinical trials are needed to validate these results and explain their relationship.

Early Bird or Night Owl: Insights into Dutch Students' Study Patterns using the Medical Faculty's E-learning Registrations.

Phenomenon: Educational activities for students are typically arranged without consideration of their preferences or peak performance hours. Students might prefer to study at different times based on their chronotype, aiming to optimize their performance. While face-to-face activities during the academic schedule do not offer flexibility and cannot reflect students' natural learning rhythm, asynchronous e-learning facilitates studying at one's preferred time. Given their ubiquitous accessibility, students can use e-learning resources according to their individual needs and preferences. E-learning usage data hence serves as a valuable proxy for certain study behaviors, presenting research opportunities to explore students' study patterns. This retrospective study aims to investigate when and for how long undergraduate students used medical e-learning modules. Approach: We performed a cross-sectional analysis of e-learning usage at one medical faculty in the Netherlands. We used data from 562 undergraduate multimedia e-learning modules for pre-clinical students, covering various medical topics over a span of two academic years (2018/19 and 2019/20). We employed educational data mining approaches to process the data and subsequently identified patterns in access times and durations. Findings: We obtained data from 70,805 e-learning sessions with 116,569 module visits and 1,495,342 page views. On average, students used e-learning for 16.8 min daily and stopped using a module after 10.2 min, but access patterns varied widely. E-learning was used seven days a week with an hourly access pattern during business hours on weekdays. Across all other times, there was a smooth increase or decrease in e-learning usage. During the week, more students started e-learning sessions in the morning (34.5% vs. 19.1%) while fewer students started in the afternoon (42.6% vs. 50.8%) and the evening (19.4% vs. 27.0%). We identified 'early bird' and 'night owl' user groups that show distinct study patterns. Insights: This retrospective educational data mining study reveals new insights into the study patterns of a complete student cohort during and outside lecture hours. These findings underline the value of 24/7 accessible study material. In addition, our findings may serve as a guide for researchers and educationalists seeking to develop more individualized educational programs.

Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges.

OBJECTIVES: The present study was conducted to evaluate the reproducibility of Lekholm and Zarb classification system (L&Z) for bone quality assessment of edentulous alveolar ridges and to investigate the potential of a data-driven approach for bone quality classification. MATERIALS AND METHODS: Twenty-six expert clinicians were asked to classify 110 CBCT cross-sections according to L&Z classification (T0). The same evaluation was repeated after one month with the images put in a different order (T1). Intra- and inter-examiner agreement analyses were performed using Cohen's kappa coefficient (CK) and Fleiss' kappa coefficient (FK), respectively. Additionally, radiomic features extraction was performed from 3D edentulous ridge blocks derived from the same 110 CBCTs, and unsupervised clustering using 3 different clustering methods was used to identify patterns in the obtained data. RESULTS: Intra-examiner agreement between T0 and T1 was weak (CK 0.515). Inter-examiner agreement at both time points was minimal (FK at T0: 0.273; FK at T1: 0.243). The three different unsupervised clustering methods based on radiomic features aggregated the 110 CBCTs in three groups in the same way. CONCLUSIONS: The results showed low agreement among clinicians when using L&Z classification, indicating that the system may not be as reliable as previously thought. The present study suggests the possible application of a reproducible data-driven approach based on radiomics for the classification of edentulous alveolar ridges, with potential implications for improving clinical outcomes. Further research is needed to determine the clinical significance of these findings and to develop more standardized and accurate methods for assessing bone quality of edentulous alveolar ridges.

Mining Bodily Cues to Deception.

A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

Unveiling the Negative Customer Experience in Diagnostic Centers: A Data Mining Approach.

Introduction: This study aims to identify the negative customer experiences reflected in complaints against diagnostic centers using data mining tools. Methods: Analyzing customer complaints from a consumer complaints website, the Apriori algorithm was employed to uncover frequent patterns and identify key areas of concern. The frequency and distribution of terms used in complaints were also analyzed, and word clouds were generated to visualize the findings. Results: The study revealed that major areas of unfavorable customer experience included delayed test reports, erroneous test results, difficulties scheduling appointments, staff incivility, subpar service, and medical negligence. Discussion: These findings and the proposed model can guide diagnostic centers in incorporating data mining tools for customer experience analysis, enabling managers to proactively address issues and view complaints as opportunities for service improvement rather than legal liabilities.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace.

Backgrounds: With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers. Aim: To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward. Methods: Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords. Results: According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement. Conclusion: Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings.

Wind environment is important in architectural sustainable design, as existing studies show that it can be considerably influenced by building morphologies. This study aimed to develop a data-mining framework to quantitatively evaluate and compare influences on Low-Wind-Velocity Area (LWVA) of common cuboid-form buildings with typical morphological parameters. The data-mining framework was originally developed by integrating multiple computational methods for rapid in-depth iterative analyses, including the generation of building models using parametric modelling, the big data generation based on hybrid model, and the statistical metric analysis method. The hybrid model was created by combining the CFD model and machine learning model. The accuracy and efficiency of the framework were fully demonstrated through the comprehensive validation and analyses of different models. The data of more than fifty thousand building cases with different morphological parameters and relevant wind conditions were generated and analyzed. Influences on LWVA of morphological parameters of cuboid-form building was comprehensively evaluated, including the visualization of multiple parameters, calculation and comparison of several correlation coefficients. It suggested that the reduction of height and width on the windward side would significantly decrease the LWVA and promote the outdoor ventilation. The change of depth would have relatively limited influence on the LWVA. Multivariate regression model-fit and variance analyses were further implemented, and it was found that there was a relatively significant linear correlation between the LWVA and morphological parameters. The equation of multivariate regression model was provided for extra rapid prediction. The study outcome could contribute to efficient evaluation of LWVA and provide useful information for sustainable design.

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime.

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques.

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays. The proposed method is tested against other pneumothorax segmentation techniques using a dataset of 'chest X-rays' with 'pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

A disproportionality analysis of FDA adverse event reporting system (FAERS) events for ticagrelor.

Background: Ticagrelor is a commonly used antiplatelet agent, but due to the stringent criteria for trial population inclusion and the limited sample size, its safety profile has not been fully elucidated. Method: We utilized OpenVigil 2.1 to query the FDA Adverse Event Reporting System database and retrieved reports by the generic name "ticagrelor" published between 1 October 2010 and 31 March 2023. Adverse drug events (ADEs) were classified and described according to the preferred terms and system organ classes in the Medical Dictionary of Regulatory Activity. Proportional reporting ratio (PRR), reporting odds ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) were used to detect signals. Results: The number of ADE reports with ticagrelor as the primary suspect drug was 12,909. The top three ADEs were dyspnea [1824 reports, ROR 7.34, PRR 6.45, information component (IC) 2.68], chest pain (458 reports, ROR 5.43, PRR 5.27, IC 2.39), and vascular stent thrombosis (406 reports, ROR 409.53, PRR 396.68, IC 8.02). The highest ROR, 630.24, was found for "vascular stent occlusion". Cardiac arrest (137 reports, ROR 3.41, PRR 3.39, IC 1.75), atrial fibrillation (99 reports, ROR 2.05, PRR 2.04, IC 1.03), asphyxia (101 reports, ROR 23.60, PRR 23.43, IC 4.51), and rhabdomyolysis (57 reports, ROR 2.75, PRR 2.75, IC 1.45) were suspected new adverse events of ticagrelor. Conclusion: The FAERS database produced potential signals associated with ticagrelor that have not been recorded in the package inserts, such as cardiac arrest, atrial fibrillation, asphyxia, and rhabdomyolysis. Further clinical surveillance is needed to quantify and validate potential hazards associated with ticagrelor-related adverse events.

An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.

In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.

Insights into the nutritional prevention of macular degeneration based on a comparative topic modeling approach.

Topic modeling and text mining are subsets of natural language processing (NLP) with relevance for conducting meta-analysis (MA) and systematic review (SR). For evidence synthesis, the above NLP methods are conventionally used for topic-specific literature searches or extracting values from reports to automate essential phases of SR and MA. Instead, this work proposes a comparative topic modeling approach to analyze reports of contradictory results on the same general research question. Specifically, the objective is to identify topics exhibiting distinct associations with significant results for an outcome of interest by ranking them according to their proportional occurrence in (and consistency of distribution across) reports of significant effects. Macular degeneration (MD) is a disease that affects millions of people annually, causing vision loss. Augmenting evidence synthesis to provide insight into MD prevention is therefore of central interest in this article. The proposed method was tested on broad-scope studies addressing whether supplemental nutritional compounds significantly benefit macular degeneration. Six compounds were identified as having a particular association with reports of significant results for benefiting MD. Four of these were further supported in terms of effectiveness upon conducting a follow-up literature search for validation (omega-3 fatty acids, copper, zeaxanthin, and nitrates). The two not supported by the follow-up literature search (niacin and molybdenum) also had scores in the lowest range under the proposed scoring system. Results therefore suggest that the proposed method's score for a given topic may be a viable proxy for its degree of association with the outcome of interest, and can be helpful in the systematic search for potentially causal relationships. Further, the compounds identified by the proposed method were not simultaneously captured as salient topics by state-of-the-art topic models that leverage document and word embeddings (Top2Vec) and transformer models (BERTopic). These results underpin the proposed method's potential to add specificity in understanding effects from broad-scope reports, elucidate topics of interest for future research, and guide evidence synthesis in a scalable way. All of this is accomplished while yielding valuable and actionable insights into the prevention of MD.

Analysis of mechanism of core points in acupuncture and moxibustion treatment for epilepsy based on data mining. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŽ¢è®¨é’ˆç¸æ²»ç–—ç™«ç—«æ ¸å¿ƒç©´ä½çš„ä½œç”¨æœºåˆ¶.

OBJECTIVES: To explore the mechanism of core points in acupuncture and moxibustion treatment for epilepsy by using data mining technique, so as to provide a reference for clinical practice and experimental research. METHODS: The data comes from relevant documents collected from CNKI, Wanfang, SinoMed, VIP, PubMed, Embase, Cochrane Library, EBSCO, Web of Science databases. The selected acupoints were analyzed in descriptive statistics, high-frequency acupoints group and core acupoint prescription. Further, potential target mining, "core acupoint prescription-target-epilepsy" network construction, protein-protein interactions (PPI) network establishment and core target extraction, gene ontology (GO) and KEGG gene enrichment analysis of the core acupoint prescription were carried out to predict its anti-epileptic potential mechanism. RESULTS: A total of 122 acupoint prescriptions were included. The core acupoint prescriptions were Baihui (GV20), Hegu (LI4), Neiguan (PC6), Shuigou (GV26) and Taichong (LR3). 277 potential targets were identified, among which 134 were shared with epilepsy. The core targets were extracted by PPI network topology analysis, including signal transducer and activator of transcription 3, tumor necrosis factor (TNF), interleukin (IL)-6, protein kinase B1, c-Jun N-terminal kinase, brain-derived neurotrophic factor, tumor protein 53, vascular endothelial growth factor A, Caspase-3, epidermal growth factor receptor, etc. The main anti-epileptic pathways of the core acupoints were predicted by KEGG enrichment, including lipid and atherosclerosis, neurodegeneration, phosphatidylinositol-3-kinase/protein B kinase signaling pathway, mitogen-activated protein kinase signaling pathway, cyclic adenosine monophosphate signaling pathway, TNF signaling pathway, IL-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, apoptosis, etc., involving neuronal death, synaptic plasticity, oxidative stress, inflammation and other related biological process. CONCLUSIONS: The core acupoint prescription of acupuncture and moxibustion intervention for epilepsy can act on multiple targets and multiple pathways to exert anti-epileptic effects, which can provide a theoretical basis for further clinical application and mechanism research.

Analysis on acupoint selection rules in the treatment of metabolism-associated fatty liver disease based on data mining. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŠ€æœ¯åˆ†æžé’ˆç¸æ²»ç–—ä»£è°¢ç›¸å ³è„‚è‚ªæ€§è‚ç— çš„é€‰ç©´è§„å¾‹.

OBJECTIVES: To explore the rules of acupoint selection in the treatment of metabolic-associated fatty liver disease (MAFLD) with acupuncture and moxibustion by using data mining technology. METHODS: The clinical research literature on acupuncture treatment of MAFLD was collected from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, VIP Database and China Biology Medicine from their inception to November 20, 2022. According to our inclusion and exclusion criteria, the literature was independently screened and re-screened by two research members, and the screened results were checked, followed by establishing an acupoint prescription database using Excel 2019. Descriptive statistics of acupoints applied frequency, involved meridians, locations and specific acupoints were perpormed. Then, SPSS Modeler18.0 software was used to conduct analysis about association rules, and the SPSS Statistics 26.0 software was used to perform cluster analysis on high-frequency acupoints, exploring the characteristics and rules of acupoint selection and combination in the treatment of MAFLD. RESULTS: Totally, 178 papers were collected, containing 130 acupoints, with a total application frequency of 1 305. The top five acupoints are Zusanli (ST36), Fenglong (ST40), Ganshu (BL18), Taichong (LR3) and Sanyinjiao (SP6). The commonly involved meridians are the Stomach Meridian of Foot Yangming, Bladder Meridian of Foot Taiyang, and Spleen Meridian of Foot Taiyin. The employed acupoints are mostly located in the lower limbs and abdomen, and the five Shu acupoints and crossing acupoints are in the majority. The association rule analysis of high frequency acupoints indicated that of the 16 qualified acupoint groups, the top 5 with close correlation degrees are ST36 and ST40, ST36 and LR3, ST36 and SP6, ST40 and LR3 and ST36, ST36 and SP6 and ST40. Further, 3 effective clusters were obtained by cluster analysis. CONCLUSIONS: Acupuncture and moxibustion treatment of MAFLD follows the therapeutic principles of soothing the liver, invigorating the spleen, tonifying the kidney, and resolving phlegm and removing dampness. The core acupoint group is ST36, ST40 and LR3, and the combination of acupoints is based on syndrome differentiation. These results may provide a useful reference for clinical practice.