Detecting mental and physical disorders using multi-task learning equipped with knowledge graph attention network.

Mental and physical disorders (MPD) are inextricably linked in many medical cases; psychosomatic diseases can be induced by mental concerns and psychological discomfort can ensue from physiological diseases. However, existing medical informatics studies focus on identifying mental or physical disorders from a unilateral perspective. Consequently, no existing domain knowledge base, corpus, or detection modeling approach considers mental as well as physical aspects concurrently. This paper proposes a joint modeling approach to detect MPD. First, we crawl through online medical consultation records of patients from websites and build an MPD knowledge ontology by extracting the core conceptual features of the text. Based on the ontology, an MPD knowledge graph containing 12,673 nodes and 82,195 relations is obtained using term matching with a domain thesaurus of each concept. Subsequently, an MPD corpus with fine-grained severities (None, Mild, Moderate, Severe, Dangerous) and 8909 records is constructed by formulating MPD classification criteria and a data annotation process under the guidance of domain experts. Taking the knowledge graph and corpus as the dataset, we design a multi-task learning model to detect the MPD severity, in which a knowledge graph attention network (KGAT) is embedded to better extract knowledge features. Experiments are performed to demonstrate the effectiveness of our model. Furthermore, we employ ontology-based and centrality-based methods to discover additional potential inferred knowledge, which can be captured by KGAT so as to improve the prediction performance and interpretability of our model. Our dataset has been made publicly available, so it can be further used as a medical informatics reference in the fields of psychosomatic medicine, psychiatrics, physical co-morbidity, and so on.

Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine.

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.

Scientific knowledge graph and trend analysis of Tuina: A bibliometric analysis.

OBJECTIVES: Tuina is an effective complementary and alternative therapy. However, no bibliometric analysis has explored the global research status and emerging trends of tuina. Therefore, our study aimed to provide a perspective on the current state and frontier trends in the field. DESIGN: Bibliometric analysis SETTING: Tuina-related publications between January 1, 2003, and December 31, 2022, were obtained from the Web of Science Core Collection database. MAIN OUTCOME MEASURES: The knowledge graph software CiteSpace and VOSViewer were used to quantitatively analyse annual trends in annual publication volume, journals, countries, institutions, authors, cited references, and keywords. RESULTS: Overall, 1877 articles were obtained. Consequently, the number of annual publications in tuina gradually increased. China published the most articles (1402 articles, 58.01%), followed by the Chinese Academy of Sciences (110 articles, 2.57%). Original and review articles were the two main types of publications. Photonics Research ranked first (101 articles, 5.38%) as the most influential affiliate and productive journal. These articles come from 8423 authors, among whom Min Fang published the most publications, and Ernst E was co-cited most often. According to the keyword co-occurrence analysis, the new research frontiers were meta-analyses. CONCLUSION: This comprehensive bibliometric study analysed the publications on tuina and presented them visually, revealing new research trends, pivotal points, research hotspots, and frontiers. Prospective strategies and potential directions for further studies were also provided.

Current situation and trend of translational research of acupuncture-moxibustion in treatment of aphasia based on knowledge graph analysis. / åŸºäºŽçŸ¥è¯†å›¾è°±æŠ€æœ¯åˆ†æžé’ˆç¸æ²»ç–—å¤±è¯­ç—‡è½¬åŒ–ç ”ç©¶çš„çŽ°çŠ¶ä¸Žè¶‹åŠ¿.

OBJECTIVES: To investigate the hot topics in acupuncture-moxibustion research for treatment of aphasia and explore the current situation and trend of technology transformation in this field through analyzing the relevant Chinese literatures in recent 30 years by means of knowledge graph technology. METHODS: CiteSpace 6.1.R 2 and VOSviewer V1.6.16 software were used to collate the data, draw knowledge graphs and conduct visual analysis of the literatures related to acupuncture-moxibustion treatment of aphasia, searched from CNKI, WanFang and VIP databases.The time line view and strongest bursts of keywords were formed in the field of acupuncture-moxibustion treatment for aphasia. The treatment-based keyword networks were visualized. RESULTS: A total of 773 Chinese articles were included. Through visual analysis of the co-occurrence networks, the top 10 high-frequency overall keywords and the top 10 clusters of overall keywords were listed. The top 5 high-frequency aphasia categories were Broca aphasia, hysterical aphasia, transcortical motor aphasia, nominal aphasia and sensory aphasia. Regarding the keywords of the techniques of acupuncture-moxibustion, the occurrence frequencies of scalp acupuncture, tongue acupuncture, body acupuncture and electroacupuncture were ≥ 10 times.The occurrence frequencies of 16 acupoints were ≥25 times. After collation and cluster analysis of acupoints and techniques of acupuncture-moxibustion, 7 keyword clusters of "acupuncture techniques-acupoints" were obtained. The time line view showed that the strongest burst of keywords were transcranial magnatic stimulation, language rehabilitation training, acupuncture-medicine therapy and stroke, etc. in the recent 5 years. CONCLUSIONS: Acupuncture-moxibustion displays its unique advantage in treatment of aphasia. With the deepening of modern research, the hot topics for aphasia treated with acupuncture-moxibustion are present and the achievements enriched. In future, these therapeutic methods should be further investigated to explore a model of translational medicine for aphasia in line with the characteristics of acupuncture-moxibustion.

[Current status and trend of acupuncture-moxibustion for myofascial pain syndrome: a visual analysis of knowledge graph based on CiteSpace and VOSviewer].

Bibliometric and scientific knowledge graph methods were used to analyze the research status and hot spots of acupuncture-moxibustion in treatment of myofascial pain syndrome (MPS) and explore its development trend. The articles of both Chinese and English versions relevant to MPS treated by acupuncture-moxibustion were searched in CNKI, VIP, Wanfang, SinoMed and WOS from the database inception to March 20, 2023. Using Excel2016, CiteSpace6.2.R2 and VOSviewer1.6.18, the visual analysis was conducted by means of the cooperative network, keyword co-occurrence, keyword timeline, keyword emergence, etc. From Chinese databases and WOS database, 910 Chinese articles and 300 English articles were included, respectively. The annual publication volume showed an overall rising trend. Literature output of English articles was concentrated in Spain, China, and the United States, of which, there was less cross-regional cooperation. In the keyword analysis, regarding acupuncture-moxibustion therapy, Chinese articles focused on "acupuncture", "electroacupuncture" and "acupotomy"; while, "dry needling" and "injection" were dominated for English one. Clinical study was the current hot spot in Chinese databases, in comparison, the randomized controlled double-blind clinical trial was predominant in WOS. Both Chinese and English articles were limited in the report of mechanism research. The cooperation among research teams should be strengthened to conduct comparative research, dose-effect research and effect mechanism research with different methods of acupuncture-moxibustion involved so that the evidences can be provided for deeper exploration.

Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume.

In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effective technique for information sharing. Our approach is evaluated on two datasets, including the public Nursing Activities and our self-built Tai Chi Action dataset. Results from linear evaluation protocol and fine-tuned evaluation demonstrate that our pre-trained model effectively captures common motion features among human actions and achieves steady and precise accuracy across all training settings, while mitigating network overfitting. Notably, our model outperforms state-of-the-art models in recognition accuracy when fusing joint and limb modality features along the channel dimension.

TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and their related relationships when searching for papers. To address these issues, a database on tea risk substances and their research relationships was constructed. These data were correlated by knowledge mapping techniques, and a Neo4j graph database centered on tea risk substance research was constructed, containing 4189 nodes and 9400 correlations (e.g. research category-PMID, risk substance category-PMID, and risk substance-PMID). This is the first knowledge-based graph database that is specifically designed for integrating and analyzing risk substances in tea and related research, containing nine main types of tea risk substances (including a comprehensive discussion of inclusion pollutants, heavy metals, pesticides, environmental pollutants, mycotoxins, microorganisms, radioactive isotopes, plant growth regulators, and others) and six types of tea research papers (including reviews, safety evaluations/risk assessments, prevention and control measures, detection methods, residual/pollution situations, and data analysis/data measurement). It is an essential reference for exploring the causes of the formation of risk substances in tea and the safety standards of tea in the future. Database URL http://trsrd.wpengxs.cn.

[Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng].

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.

[Knowledge graph analysis of pyroptosis research in traditional Chinese medicine based on VOSviewer and CiteSpace].

To explore the research hotspots and frontier directions of pyroptosis in the field of traditional Chinese medicine(TCM), the authors searched CNKI and Web of Science for literature related to pyroptosis in TCM, screened literature according to the search strategy and inclusion criteria, and analyzed the publication trend of the included literature. VOSviewer was used to draw author cooperation and keyword co-occurrence network diagrams, and CiteSpace was employed for keyword clustering, emergence, and timeline view. Finally, 507 Chinese literature and 464 English literature were included, and it was found that the number of Chinese and English literature was increasing rapidly year by year. The co-occurrence of the authors showed that in terms of Chinese literature, there was a representative research team composed of DU Guan-hua, WANG Shou-bao and FANG Lian-hua, and for English literature, the representative research team was composed of XIAO Xiao-he, BAI Zhao-fang and XU Guang. The network visualization of Chinese and English keywords revealed that inflammation, apoptosis, oxidative stress, autophagy, organ damage, fibrosis, atherosclerosis, and ischemia-reperfusion injury were the primary research diseases and pathological processes in TCM; berberine, resveratrol, puerarin, na-ringenin, astragaloside Ⅳ, and baicalin were the representative active ingredients; NLRP3/caspase-1/GSDMD, TLR4/NF-κB/NLRP3, and p38/MAPK signaling pathways were the main research pathways. Keyword clustering, emergence, and timeline analysis indicated that the pyroptosis research in TCM focused on the mechanism of TCM monomers and compounds intervening in diseases and pathological processes. Pyroptosis is a research hotspot in the area of TCM, and the current discussion mainly focuses on the mechanism of the therapeutic effect of TCM.

An herbal drug combination identified by knowledge graph alleviates the clinical symptoms of plasma cell mastitis patients: A nonrandomized controlled trial.

Background: Plasma cell mastitis (PCM) is a nonbacterial breast inflammation with severe and intense clinical manifestation, yet treatment methods for PCM are still rather limited. Although the mechanism of PCM remains unclear, mounting evidence suggests that the dysregulation of immune system is closely associated with the pathogenesis of PCM. Drug combinations or combination therapy could exert improved efficacy and reduced toxicity by hitting multiple discrete cellular targets. Methods: We have developed a knowledge graph architecture toward immunotherapy and systematic immunity that consists of herbal drug-target interactions with a novel scoring system to select drug combinations based on target-hitting rates and phenotype relativeness. To this end, we employed this knowledge graph to identify an herbal drug combination for PCM and we subsequently evaluated the efficacy of the herbal drug combination in clinical trial. Results: Our clinical data suggests that the herbal drug combination could significantly reduce the serum level of various inflammatory cytokines, downregulate serum IgA and IgG level, reduce the recurrence rate, and reverse the clinical symptoms of PCM patients with improvements in general health status. Conclusions: In summary, we reported that an herbal drug combination identified by knowledge graph can alleviate the clinical symptoms of PCM patients. We demonstrated that the herbal drug combination holds great promise as an effective remedy for PCM, acting through the regulation of immunoinflammatory pathways and improvement of systematic immune level. In particular, the herbal drug combination could significantly reduce the recurrence rate of PCM, a major obstacle to PCM treatment. Our data suggests that the herbal drug combination is expected to feature prominently in future PCM treatment. Funding: C. Liu's lab was supported by grants from the Public Health Science and Technology Project of Shenyang (grant: 22-321-32-18); Y. Yang's laboratory was supported by the National Natural Science Foundation of China (grant: 81874301), the Fundamental Research Funds for Central University (grant: DUT22YG122), and the Key Research project of 'be Recruited and be in Command' in Liaoning Province (2021JH1/10400050). Clinical trial number: NCT05530226.

Fingerprint matching using the onion peeling approach and turning function.

Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae points are the ways that the fingerprint ridges can be discontinuous. Ridge ending and ridge bifurcation are two frequently used minutiae in most fingerprint matching algorithms. This article presents a new minutiae-based fingerprint matching using the onion peeling approach. In the proposed method, fingerprints are aligned to find the matched minutiae points. Then, the nested convex polygons of matched minutiae points are constructed and the comparison between peer-to-peer polygons is performed by the turning function distance. Simplicity, accuracy, and low time complexity of the onion peeling approach are three important factors that make it a standard method for fingerprint matching purposes. The performance of the proposed algorithm is evaluated on the database FVC2002. Since the fingerprints that the difference between the number of their layers is more than 2 and the a minutiae matching score lower than 0.15 are ignored, better results are obtained. KEYWORDS: Fingerprint Matching, Minutiae, Convex Layers, Turning Function, Computational Geometry.

Study on the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng / 中国针灸

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.

Knowledge graph analysis of pyroptosis research in traditional Chinese medicine based on VOSviewer and CiteSpace / 中国中药杂志

To explore the research hotspots and frontier directions of pyroptosis in the field of traditional Chinese medicine(TCM), the authors searched CNKI and Web of Science for literature related to pyroptosis in TCM, screened literature according to the search strategy and inclusion criteria, and analyzed the publication trend of the included literature. VOSviewer was used to draw author cooperation and keyword co-occurrence network diagrams, and CiteSpace was employed for keyword clustering, emergence, and timeline view. Finally, 507 Chinese literature and 464 English literature were included, and it was found that the number of Chinese and English literature was increasing rapidly year by year. The co-occurrence of the authors showed that in terms of Chinese literature, there was a representative research team composed of DU Guan-hua, WANG Shou-bao and FANG Lian-hua, and for English literature, the representative research team was composed of XIAO Xiao-he, BAI Zhao-fang and XU Guang. The network visualization of Chinese and English keywords revealed that inflammation, apoptosis, oxidative stress, autophagy, organ damage, fibrosis, atherosclerosis, and ischemia-reperfusion injury were the primary research diseases and pathological processes in TCM; berberine, resveratrol, puerarin, na-ringenin, astragaloside Ⅳ, and baicalin were the representative active ingredients; NLRP3/caspase-1/GSDMD, TLR4/NF-κB/NLRP3, and p38/MAPK signaling pathways were the main research pathways. Keyword clustering, emergence, and timeline analysis indicated that the pyroptosis research in TCM focused on the mechanism of TCM monomers and compounds intervening in diseases and pathological processes. Pyroptosis is a research hotspot in the area of TCM, and the current discussion mainly focuses on the mechanism of the therapeutic effect of TCM.

Mining on Alzheimer's diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing.

BACKGROUND: To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study the relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. RESULTS: Among three knowledge graph completion models, TransE outperformed the other two (MR = 10.53, Hits@1 = 0.28). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. CONCLUSION: This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

[Visual analysis on regulation of necroptosis with Chinese medicine based on VOSviewer and CiteSpace knowledge graphs].

The study was conducted by searching the literature related to the regulation of necroptosis with Chinese medicine from January 1, 2005 to December 31, 2021 in CNKI, VIP, Wanfang, Web of Science(WoS), and PubMed. The obtained literature were imported into NoteExpress for eliminating duplicates and screening, and the final included articles were imported into Excel to plot the publication trend. The core authors were identified according to Price's law, and VOSviewer 1.6.17 was used to draw a collaborative view of the core authors and sort the high-frequency keywords. Then CiteSpace 5.8.R3 was employed to analyze keywords clustering, burst, and timeline view. Finally, 98 Chinese articles and 72 English articles were included in the study. The number of publications on the regulation of necroptosis with Chinese medicine has been increasing year by year. China ranked among the top in the world in terms of the number of publications, and Chinese authors played a central role in this field. Specifically, LIU Hua published the most Chinese literature while CHEN X P had the most English publications. The collaborative view of the core authors showed more intra-team cooperation and less inter-team cooperation. The Chinese and English keywords formed ten clusters separately, indicating that the research hotspots of regulation of necroptosis with Chinese medicine mainly focused on disease, prescription, related factors, and mecha-nism. Further, the analysis of Chinese and English keywords revealed that regarding disease treatment, tumor, ischemia-reperfusion injury, neurodegenerative diseases, and inflammatory diseases were studied most. The Chinese medicines that received much attention in this field were curcumin, shikonin and tanshinone. The main protein factors involved were Ripk1, Ripk3, Mlkl, and TNF-α, and Ripk1/Ripk3/Mlkl and p53 signaling pathways were predominant. Moreover, single herbs and herbal monomers were the hotspots of the included articles. In the future, scholars need to expand the study of classical Chinese herbal compounds and explore their mechanism of action in the occurrence and development of various diseases, to provide new ideas and experimental basis for the treatment of clinical diseases with Chinese medicine.

ATR-FTIR-MIR Spectrometry and Pattern Recognition of Bioactive Volatiles in Oily versus Microencapsulated Food Supplements: Authenticity, Quality, and Stability.

Fourier transform infrared spectroscopy on the middle infrared region (ATR-FTIR-MIR) proved to be a convenient and reliable technique to evaluate foods' quality and authenticity. Plants' essential oils are bioactive mixtures used as such or in different oily or microencapsulated formulations, beneficial to human health. Six essential oils (thyme, oregano, juniperus, tea tree, clove, and cinnamon) were introduced in three oily formulations (Biomicin, Biomicin Forte, and Biomicin urinary) and these formulations were microencapsulated on fructose and maltodextrin matrices. To study their stability, the microencapsulated powders were kept under light irradiation for 14 days at 25 °C or introduced in biopolymer capsules. All variants were analysed by ATR-FTIR-MIR, recording wavenumbers and peak intensities (3600-650 cm-1). The data were processed by Unscrambler and Metaboanalyst software, with specific algorithms (PCA, PLSDA, heatmaps, and random forest analysis). The results demonstrated that ATR-FTIR-MIR can be successfully applied for fingerprinting and finding essential oil biomarkers as well as to recognize this pattern in final microencapsulated food supplements. This study offers an improved ATR-FTIR-MIR procedure coupled with an adequate chemometric analysis and accurate data interpretation, to be applied for the evaluation of authenticity, quality, traceability, and stability during storage of essential oils incorporated in different matrices.

Mining a stroke knowledge graph from literature.

BACKGROUND: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the "Western" biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases. RESULTS: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine. CONCLUSIONS: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke.

[Knowledge graph characteristics of sepsis research based on scientometric study].

OBJECTIVE: To illustrate a relatively complete knowledge system (e.g., research outputs, current hotspots, and future trends) in the sepsis field and to help scholars grasp the scientific research direction or clinical focus of treatment. METHODS: The relevant literatures of sepsis during the time from 1985 to 2019 in Web of Science database were collected. Sepsis-related research contents were generated using softwares (CiteSpace 5.6.R2 and VOSviewer 1.6.13), which using data mining, information processing and knowledge map methods, to analyze the historical evolution and predict the development trend. RESULTS: A total of 8 189 papers on sepsis were published. The volume of publications were increasing yearly from 1985 to 2019, and reached the top list of 1 276 in 2019. For research contents of sepsis, it has formed the basic characteristics of sepsis which focusing on epidemiological studies and animal experiments. Through cluster analysis, the researches mainly focused on six aspects: septic rat, necrotizingenterocolitis, sepsis-associated encephalopathy, acute kidney injury (AKI), gut-derived sepsis, and inflammatory mediator. And it presented the literature characteristics that related to the injury or dysfunction of intestines, brain, liver, kidney or other organs, but the heart and lung researches were more marginal. Additionally, based on the top key words with the strongest citation bursts, it reflected that the development trend of the continuous attention hotspots with "endotoxin" or "endotoxin shock", the significant attention hotspots with "inflammation", "immunity" and "multiple organ dysfunction syndrome" (MODS), and the novel burst attention hotspots with sepsis management including "diagnosis" and "chemotherapy". CONCLUSIONS: Through the hotspots and trends visualization of sepsis, the current researches are prefer to animal experiments, epidemiology, or other basic scientific aspects. Meanwhile, the researches are mostly focusing on inflammatory reaction, immune function or organ dysfunctions. Integrating the knowledge maps of hotspots and trends, based on researches of epidemiology, diagnosis, risk factors, pathogenesis, or treatment, we predict that the future scientific topics will concentrating on childhood sepsis, organ injury mechanism or intervention relating to MODS, and integrated management of sepsis by combining traditional Chinese medicine and Western medicine.

A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface.

Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.