Multimedia-based Medicinal Plants Sustainability Management System

Medicinal plants are increasingly recognized worldwide as an alternative source of efficacious and inexpensive medications to synthetic chemo-therapeutic compound. Rapid declining wild stocks of medicinal plants accompanied by adulteration and species substitutions reduce their efficacy, quality and safety. Consequently, the low accessibility to and non-affordability of orthodox medicine costs by rural dwellers to be healthy and economically productive further threaten their life expectancy. Finding comprehensive information on medicinal plants of conservation concern at a global level has been difficult. This has created a gap between computing technologies’ promises and expectations in the healing process under complementary and alternative medicine. This paper presents the design and implementation of a Multimedia-based Medicinal Plants Sustainability Management System addressing these concerns. Medicinal plants’ details for designing the system were collected through semi-structured interviews and databases. Unified Modelling Language, Microsoft-Visual-Studio.Net, C#3.0, Microsoft-Jet-Engine4.0, MySQL, Loquendo Multilingual Text-to-Speech Software, YouTube, and VLC Media Player were used. Keywords: Complementary and Alternative Medicine, conservation, extinction, medicinal plant, multimedia, phytoconstituents, rural dweller

Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.Perinteinen kiinalainen lääketiede (TCM) on ilmeinen tehokkuus taudin hoidoissa ja on arvokas lähde uuden lääkkeen löytämiseen. TCM: n farmakologisten vaikutusten taustalla oleva mekanismi pysyy kuitenkin tuntemattomassa, koska TCM on monimutkainen järjestelmä, jossa on useita yrttejä ja ainesosia, jotka tulevat yhteen reseptilääkkeeksi. Siksi on kiireellistä soveltaa Laskennallisia työkaluja TCM: lle ymmärtämään TCM-teorioiden taustalla oleva mekanismi molekyylitasolla ja käyttävät kehittyneitä verkkoalgoritmeja tutkimaan mahdollisia tehokkaita ainesosia ja havainnollistavat TCM: n periaatteita järjestelmän biologisissa näkökohdissa. Tässä opinnäytetyössä pyrimme ymmärtämään monimutkaisten TCM-järjestelmien toimintamekanismia molekyylitasolla bioinformaattilla ja laskennallisilla työkaluilla. Tutkimuksessa kehitettiin koneen oppimiskehystä yrttien ja ainesosien meridialaisista. Lopuksi saavutimme korkean tarkkuuden meridiaaneista yrtteistä ja ainesosista, mikä viittaa meridiaaneihin ja ainesosien ja yrtteihin liittyvien molekyylipiirin välillä, erityisesti koneen oppimismalleihin tärkeimmät ominaisuudet. Toiseksi ehdoimme uuden verkon lähestymistavan TCM-kaavojen tutkimiseksi kvantitoimisella vuorovaikutteisten yrttiparien vuorovaikutuksen tutkimuksessa ⅱ käyttämällä viisi verkkoetäisyyttä, mukaan lukien lähin, lyhyt, keskus, ydin sekä erottaminen. Osoitimme, että ylä-yrttiparien etäisyys on lyhyempi kuin satunnaisten yrttiparien, mikä viittaa voimakkaaseen vuorovaikutukseen ihmisellä vuorovaikutteisesti. Lisäksi Center-menetelmät ainesosan tasolla ylittivät muut menetelmät. Se vihjeitä meille, että keskeiset ainesosat ovat tärkeässä asemassa yrtteissä. Kolmanneksi tutkimme yrttien tai ainesosien välisiä yhdistyksiä ja niiden tärkeitä biologisia ominaisuuksia tutkimuksessa III, kuten ominaisuudet, meridiaanit, rakenteet tai tavoitteet klustereiden kautta moniparite-verkoston yhteisön analyysistä. Löysimme, että kasviperäiset lääkkeet samoilla klusterien keskuudessa ovat yleensä samankaltaisia ominaisuuksissa, meridiaaneissa. Samoin saman klusterin ainesosat ovat samankaltaisempia rakenteissa ja proteiinin tavoitteessa. Yhteenvetona tämä opinnäytetyö aikoo rakentaa silta TCM-järjestelmän ja nykyaikaisten lääkevalmisteiden välillä laskentatyökaluilla, mukaan lukien Meridian-teorian koneen oppimismalli, TCM-kaavojen verkkomallinnus sekä kasviperäiset lääkkeet ja niiden ainesosat Osoitimme, että uusien laskennallisten lähestymistapojen soveltaminen integroidulle korkean suorituskyvyttömiehille tarjosivat TCM: n näkemyksiä ja nopeuttaisivat romaanin huumeiden löytöä sekä toistuvat TCM: stä

Mining a stroke knowledge graph from literature

From Springer Nature via Jisc Publications RouterHistory: collection 2021-05, received 2021-06-13, accepted 2021-07-06, registration 2021-07-09, pub-electronic 2021-07-29, online 2021-07-29Publication status: PublishedFunder: National High-level Personnel for Defense Technology Program; Grant(s): (2017-JCJQ-ZQ-013), and NSF 61902405Funder: the national key r&d project by ministry of science and technology of china; Grant(s): 2018YFB1003203Funder: the open fund from the State Key Laboratory of High Performance Computing; Grant(s): No. 201901-11Funder: National Science Foundation of China; Grant(s): U1811462Abstract: 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/Strok

Database development and machine learning classification of medicinal chemicals and biomolecules

Ph.DDOCTOR OF PHILOSOPH

Английский язык. "Reader"

УЧЕБНО-МЕТОДИЧЕСКИЕ ПОСОБИЯАНГЛИЙСКИЙ ЯЗЫКЧТЕНИЕПОСОБИЯПособие содержит текстовой материал по формированию различных навыков чтения (просмотрового, поискового, ознакомительного, изучающего). Пособие может быть использовано как для аудиторной, так и для самостоятельной работы студентов и магистрантов

Database development and mechanistic study of traditional Chinese medicine by computer

Master'sMASTER OF SCIENC

Plant extracts and natural products - Predictive structural and biodiversity-based analyses of uses, bioactivity, and 'research and development' potential

The process of drug discovery and development over the last 30 years has been increasingly shaped by formulaic approaches and natural products – integral to the drug discovery process and widely recognized as the most successful class of drug leads – have significantly been deprioritized by a struggling worldwide pharmaceutical industry. Alkaloids - historically the most important superclass of medically important secondary metabolites - have been used worldwide as a source of remedies to treat a wide variety of illnesses yet, there exists a wide discrepancy between their historical and modern significances. To understand these trends from an insider’s perspective, 52 senior-stakeholders in industry and academia were engaged to provide insights on a series of qualitative and quantitative aspects related to developments in the process of drug discovery from natural products. Stakeholders highlighted the dissonance between the perceived high potential of natural products as drug leads and overall industry and company level strategies. Many industry contacts were highly critical to prevalent company and industry-wide drug discovery strategies indicating a high level of dissatisfaction within the industry. One promising strategy which respondents highlighted was virtual screening which, to a large extent has not been explored in natural products research strategies. Furthermore, the physicochemical features of 27,783 alkaloids from the Dictionary of Natural Products were cross-referenced to pharmacologically significant and other metrics from various databases including the European Bioinformatics Institute’s ChEMBL and Global Biodiversity Information Facility’s GBIF biodiversity data. The combined dataset revealed that a compound's likelihood of medicinal use can be linked to its host species’ abundance and was input into target-independent machine learning algorithms to predict likelihood of pharmaceutical use. The neural network model demonstrated an accuracy of >57% for all pharmaceutical alkaloids and 98% of all alkaloids. This study is the first to incorporate the biodiversity of host organisms in a machine learning scheme characterizing druglikeness and thus demonstrates the link between host species’ abundance and druglikeness. These findings yield new insights into cost-effective, real-world indicators of drug development potential across the diverse field of natural products

Applications and Advances in Electronic-Nose Technologies

Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Recent applications of electronic nose technologies have come through advances in sensor design, material improvements, software innovations and progress in microcircuitry design and systems integration. The invention of many new e-nose sensor types and arrays, based on different detection principles and mechanisms, is closely correlated with the expansion of new applications. Electronic noses have provided a plethora of benefits to a variety of commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields. Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes. This paper is a review of the major electronic-nose technologies, developed since this specialized field was born and became prominent in the mid 1980s, and a summarization of some of the more important and useful applications that have been of greatest benefit to man

The Future of Medicine: Frontiers in Integrative Health and Medicine

Contemporary healthcare trends indicate that many chronic and communicable diseases are related to lifestyle, stress, personal choices and systemic factors. In response to the shortfalls of modern medicine regarding the prevention of these diseases and the promotion of whole-person health, providers and consumers worldwide are exploring integrative, natural and complementary approaches to prevention, treatment and health promotion. These trends harbor the future of medicine. The issues of clinician burnout, high rates of adverse effects, high cost, and lack of rigorous methods to promote individual and collective immunity are addressed by leading physicians and scientists from around the world. The original research and reviews in this volume investigate efficacy, molecular mechanisms and hypotheses that suggest that traditional systems of medicine and health, e.g., Ayurveda, yoga, traditional Chinese medicine, and mind–body–lifestyle medicine, may offer preventive and cost-effective solutions to contemporary health care challenges. Integrating innovative health approaches with conventional medicine offers a whole system of medicine that encompasses the individual, family, community and environment—from single person to planetary health