Stud Health Technol Inform. 2021 Oct 27;285:82-87. doi: 10.3233/SHTI210577.

ABSTRACT

The global pandemic over the past two years has reset societal agendas by identifying both strengths and weaknesses across all sectors. Focusing in particular on global health delivery, the ability of health care facilities to scale requirements and to meet service demands has detected the need for some national services and organisations to modernise their organisational processes and infrastructures. Core to requirements for modernisation is infrastructure to share information, specifically structural standardised approaches for both operational procedures and terminology services. Problems of data sharing (aka interoperability) is a main obstacle when patients are moving across healthcare facilities or travelling across border countries in cases where emergency treatment is needed. Experts in healthcare service delivery suggest that the best possible way to manage individual care is at home, using remote patient monitoring which ultimately reduces cost burden both for the citizen and service provider. Core to this practice will be advancing digitalisation of health care underpinned with safe integration and access to relevant and timely information. To tackle the data interoperability issue and provide a quality driven continuous flow of information from different health care information systems semantic terminology needs to be provided intact. In this paper we propose and present ContSonto a formal ontology for continuity of care based on ISO 13940:2015 ContSy and W3C Semantic Web Standards Language OWL (Web Ontology Language). ContSonto has several benefits including semantic interoperability, data harmonization and data linking. It can be use as a base model for data integration for different healthcare information models to generate knowledge graph to support shared care and decision making.

PMID:34734855 | DOI:10.3233/SHTI210577

BMC Public Health. 2021 Oct 31;21(1):1968. doi: 10.1186/s12889-021-11953-9.

ABSTRACT

BACKGROUND: Online food delivery services facilitate 'online' access to food outlets that typically sell lenergy-dense nutrient-poor food. Greater online food outlet access might be related to the use of this purchasing format and living with excess bodyweight, however, this is not known. We aimed to investigate the association between aspects of online food outlet access and online food delivery service use, and differences according to customer sociodemographic characteristics, as well as the association between the number of food outlets accessible online and bodyweight.

METHODS: In 2019, we used an automated data collection method to collect data on all food outlets in the UK registered with the leading online food delivery service Just Eat (n = 33,204). We linked this with contemporaneous data on food purchasing, bodyweight, and sociodemographic information collected through the International Food Policy Study (analytic sample n = 3067). We used adjusted binomial logistic, linear, and multinomial logistic regression models to examine associations.

RESULTS: Adults in the UK had online access to a median of 85 food outlets (IQR: 34-181) and 85 unique types of cuisine (IQR: 64-108), and 15.1% reported online food delivery service use in the previous week. Those with the greatest number of accessible food outlets (quarter four, 182-879) had 71% greater odds of online food delivery service use (OR: 1.71; 95% CI: 1.09, 2.68) compared to those with the least (quarter one, 0-34). This pattern was evident amongst adults with a university degree (OR: 2.11; 95% CI: 1.15, 3.85), adults aged between 18 and 29 years (OR: 3.27, 95% CI: 1.59, 6.72), those living with children (OR: 1.94; 95% CI: 1.01; 3.75), and females at each level of increased exposure. We found no association between the number of unique types of cuisine accessible online and online food delivery service use, or between the number of food outlets accessible online and bodyweight.

CONCLUSIONS: The number of food outlets accessible online is positively associated with online food delivery service use. Adults with the highest education, younger adults, those living with children, and females, were particularly susceptible to the greatest online food outlet access. Further research is required to investigate the possible health implications of online food delivery service use.

PMID:34719382 | PMC:PMC8557109 | DOI:10.1186/s12889-021-11953-9

Nucleic Acids Res. 2021 Oct 29:gkab991. doi: 10.1093/nar/gkab991. Online ahead of print.

ABSTRACT

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the 'Support' link.

PMID:34718729 | DOI:10.1093/nar/gkab991

Front Digit Health. 2021 Jul 21;3:683161. doi: 10.3389/fdgth.2021.683161. eCollection 2021.

ABSTRACT

Human papillomavirus (HPV) causes the most prevalent sexually transmitted infection (STI) in the United States. Sexually active young adults are susceptible to HPV, accounting for approximately 50% of new STIs. Oncogenic HPV subtypes 16 and 18 are associated with squamous intraepithelial lesions and cancers and are mostly preventable through prophylactic HPV vaccination. Accordingly, this study's objectives are to (1) summarize SDoH barriers and implication for low HPV vaccination rates among young adults (18-26 years), (2) propose a digital health solution that utilizes the PHL to collect, integrate, and manage personalized sexual and health information, and (3) describe the features of the PHL-based app. Through the application of novel techniques from artificial intelligence, specifically knowledge representation, semantic web, and natural language processing, this proposed PHL-based application will compile clinical, biomedical, and SDoH data from multi-dimensional sources. Therefore, this application will provide digital health interventions that are customized to individuals' specific needs and capacities. The PHL-based application could promote management and usage of personalized digital health information to facilitate precision health promotion thereby, informing health decision-making regarding HPV vaccinations, routine HPV/STI testing, cancer screenings, vaccine safety/efficacy/side effects, and safe sexual practices. In addition to detecting vaccine hesitancy, disparities and perceived barriers, this application could address participants' specific needs/challenges with navigating health literacy, technical skills, peer influence, education, language, cultural and spiritual beliefs. Precision health promotion focused on improving knowledge acquisition and information-seeking behaviors, promoting safe sexual practices, increasing HPV vaccinations, and facilitating cancer screenings could be effective in preventing HPV-associated cancers.

PMID:34713154 | PMC:PMC8521976 | DOI:10.3389/fdgth.2021.683161

Sci Data. 2021 Oct 15;8(1):269. doi: 10.1038/s41597-021-01047-x.

ABSTRACT

Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard resource for this task. However, given the significant computational resources needed for their generation, very few resources exist that make diachronic word embeddings available to the scientific community. In this paper we present DUKweb, a set of large-scale resources designed for the diachronic analysis of contemporary English. DUKweb was created from the JISC UK Web Domain Dataset (1996-2013), a very large archive which collects resources from the Internet Archive that were hosted on domains ending in '.uk'. DUKweb consists of a series word co-occurrence matrices and two types of word embeddings for each year in the JISC UK Web Domain dataset. We show the reuse potential of DUKweb and its quality standards via a case study on word meaning change detection.

PMID:34654827 | DOI:10.1038/s41597-021-01047-x

Nucleic Acids Res. 2021 Oct 14:gkab918. doi: 10.1093/nar/gkab918. Online ahead of print.

ABSTRACT

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API.

PMID:34648031 | DOI:10.1093/nar/gkab918

Neural Comput Appl. 2021 Oct 8:1-26. doi: 10.1007/s00521-021-06517-2. Online ahead of print.

ABSTRACT

The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.

PMID:34642549 | PMC:PMC8500271 | DOI:10.1007/s00521-021-06517-2

Procedia Comput Sci. 2021;192:487-496. doi: 10.1016/j.procs.2021.08.050. Epub 2021 Oct 1.

ABSTRACT

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.

PMID:34630741 | PMC:PMC8486259 | DOI:10.1016/j.procs.2021.08.050

Bioinformatics. 2021 Oct 6:btab694. doi: 10.1093/bioinformatics/btab694. Online ahead of print.

ABSTRACT

SUMMARY: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis, and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator, and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download.

AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/.

PMID:34613368 | DOI:10.1093/bioinformatics/btab694

IEEE Trans Neural Netw Learn Syst. 2021 Oct 5;PP. doi: 10.1109/TNNLS.2021.3113026. Online ahead of print.

ABSTRACT

Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.

PMID:34609944 | DOI:10.1109/TNNLS.2021.3113026

Adv Databases Inf Syst. 2021 Aug;12843:260-274. doi: 10.1007/978-3-030-82472-3_19. Epub 2021 Aug 16.

ABSTRACT

Schema matching aims to identify the correspondences among attributes of database schemas. It is frequently considered as the most challenging and decisive stage existing in many contemporary web semantics and database systems. Low-quality algorithmic matchers fail to provide improvement while manually annotation consumes extensive human efforts. Further complications arise from data privacy in certain domains such as healthcare, where only schema-level matching should be used to prevent data leakage. For this problem, we propose SMAT, a new deep learning model based on state-of-the-art natural language processing techniques to obtain semantic mappings between source and target schemas using only the attribute name and description. SMAT avoids directly encoding domain knowledge about the source and target systems, which allows it to be more easily deployed across different sites. We also introduce a new benchmark dataset, OMAP, based on real-world schema-level mappings from the healthcare domain. Our extensive evaluation of various benchmark datasets demonstrates the potential of SMAT to help automate schema-level matching tasks.

PMID:34608464 | PMC:PMC8487677 | DOI:10.1007/978-3-030-82472-3_19

Evid Based Complement Alternat Med. 2021 Sep 23;2021:6637810. doi: 10.1155/2021/6637810. eCollection 2021.

ABSTRACT

Traditional Chinese drugs (TCDs) have been widely used in clinical practice in China and many other regions for thousands of years. Nowadays TCD's bioactive ingredients and mechanisms of action are being identified. However, the lack of standardized terminologies or ontologies for the description of TCDs has hindered the interoperability and deep analysis of TCD knowledge and data. By aligning with the Basic Formal Ontology (BFO), an ISO-approved top-level ontology, we constructed a community-driven TCD ontology (TCDO) with the aim of supporting standardized TCD representation and integrated analysis. TCDO provides logical and textual definitions of TCDs, TCD categories, and the properties of TCDs (i.e., nature, flavor, toxicity, and channel tropism). More than 400 popular TCD decoction pieces (TCD-DPs) and Chinese medicinal materials (CMMs) are systematically represented. The logical TCD representation in TCDO supports computer-assisted reasoning and queries using tools such as Description Logic (DL) and SPARQL queries. Our statistical analysis of the knowledge represented in TCDO revealed scientific insights about TCDs. A total of 36 TCDs with medium or high toxicity are most densely distributed, primarily in Aconitum genus, Lamiids clade, and Fabids clade. TCD toxicity is mostly associated with the hot nature and pungent or bitter flavors and has liver, kidney, and spleen channel tropism. The three pairs of TCD flavor-nature associations (i.e., bitter-cold, pungent-warm, and sweet-neutral) were identified. The significance of these findings is discussed. TCDO has also been used to support the development of a web-based traditional Chinese medicine semantic annotation system that provides comprehensive annotation for individual TCDs. As a novel formal TCD ontology, TCDO lays out a strong foundation for more advanced TCD studies in the future.

PMID:34603473 | PMC:PMC8483929 | DOI:10.1155/2021/6637810

Appl Clin Inform. 2021 Aug;12(4):910-923. doi: 10.1055/s-0041-1735180. Epub 2021 Sep 22.

ABSTRACT

OBJECTIVE: Verbal autopsy is a technique used to collect information about a decedent from his/her family members using questionnaires, conducting interviews, making observations, and sampling. In substantial parts of the world, particularly in Africa and Asia, many deaths are unrecorded. In 2017, globally pregnant women were dying daily around 810 and 295,000 in a year because of pregnancy-related problems, pointed out by World Health Organization. Identifying the cause of a death is a complex process which requires in-depth medical knowledge and practical experience. Generally, medical practitioners possess different knowledge levels, set of abilities, and problem-solving skills. Additionally, the medical negligence plays a significant part in further worsening the situation. Accurate identification of the cause of death can help a government to take strategic measures to focus on, particularly increasing the death rate in a specific region.

METHODS: This research provides a solution by introducing a semantic-based verbal autopsy framework for maternal death (SVAF-MD) to identify the cause of death. The proposed framework consists of four main components as follows: (1) clinical practice guidelines, (2) knowledge collection, (3) knowledge modeling, and (4) knowledge codification. Maternal ontology for the framework is developed using Protégé knowledge editor. Resource description framework application programming interface (API) for PHP (RAP) is used as a Semantic Web toolkit along with Simple Protocol and RDF Query Language (SPARQL) is used for querying with ontology to retrieve data.

RESULTS: The results show that 92% of maternal causes of deaths assigned using SVAF-MD correctly matched manual reports already prepared by gynecologists.

CONCLUSION: SVAF-MD, a semantic-based framework for the verbal autopsy of maternal deaths, assigns the cause of death with minimum involvement of medical practitioners. This research helps the government to ease down the verbal autopsy process, overcome the delays in reporting, and facilitate in terms of accurate results to devise the policies to reduce the maternal mortality.

PMID:34553359 | DOI:10.1055/s-0041-1735180

JMIR Pediatr Parent. 2021 Sep 19. doi: 10.2196/30169. Online ahead of print.

ABSTRACT

BACKGROUND: Parents commonly experience anxiety, worry and psychological distress in caring for newborn infants, particularly those born preterm. Web-based therapist services may offer greater accessibility and timely psychological support for parents, but are nevertheless labor-intensive due to their interactive nature. Chatbots that simulate human-like conversations show promise for such interactive applications.

OBJECTIVE: To explore the usability and feasibility of chatbot technology for gathering real-life conversation data on stress, sleep and infant feeding from parents with newborn infants and to investigate differences between experiences of parents with preterm and term infants.

METHODS: Parents aged ≥21 years with infants aged ≤6 months were enrolled from November 2018 to March 2019. Three chatbot scripts (stress, sleep, feeding) were developed to capture conversations with parents via their mobile devices. Parents completed a chatbot usability questionnaire upon study completion. Responses to closed-ended questions and manually-coded open-ended responses were summarized descriptively. Open-ended responses were analyzed using the Latent Dirichlet Allocation (LDA) method to uncover semantic topics.

RESULTS: Of 45 enrolled participants (20 preterm; 25 term), 26 completed the study. Parents rated the chatbot as "easy" to use (mean ± SD: 4.08±0.74; 1 [Very difficult] - 5 [Very easy]) and were "satisfied" (mean ± SD: 3.81±0.90; 1 [Very dissatisfied] - 5 [Very satisfied]). Of 45 enrolled parents, those with preterm infants reported emotional stress more frequently than parents of term infants (33 vs. 24 occasions). Parents generally reported satisfactory sleep quality. The preterm group reported feeding problems more frequently than the term group (8 vs. 2 occasions). In stress domain conversations, topics linked to "discomfort" and "tiredness" were more prevalent in preterm group conversations, whereas the topic of "positive feelings" occurred more frequently in term group conversations. Interestingly, feeding-related topics dominated the content of sleep domain conversations, suggesting that frequent or irregular feeding may affect parents' ability to get adequate sleep or rest.

CONCLUSIONS: The chatbot was successfully utilized to collect real-time conversation data on stress, sleep and infant feeding from a group of 45 parents. In their chatbot conversations, term group parents frequently expressed positive emotions, whereas preterm group parents frequently expressed physical discomfort and tiredness, as well as emotional stress. Overall, parents who completed the study gave positive feedback on their user experience with the chatbot as a tool to express their thoughts and concerns.

CLINICALTRIAL: ClinicalTrials.gov NCT03630679.

PMID:34544679 | DOI:10.2196/30169

Proc Int World Wide Web Conf. 2021 Apr;2021:2672-2683. doi: 10.1145/3442381.3450128. Epub 2021 Apr 19.

ABSTRACT

With 214 source vocabularies, the construction and maintenance process of the UMLS (Unified Medical Language System) Metathesaurus terminology integration system is costly, time-consuming, and error-prone as it primarily relies on (1) lexical and semantic processing for suggesting groupings of synonymous terms, and (2) the expertise of UMLS editors for curating these synonymy predictions. This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. We evaluate this deep learning (DL) approach against a rule-based approach (RBA) that approximates the current UMLS Metathesaurus construction process. The key to the generalizability of our approach is the use of various degrees of lexical similarity in negative pairs during the training process. Our initial experiments demonstrate the strong performance across multiple datasets of our DL approach in terms of recall (91-92%), precision (88-99%), and F1 score (89-95%). Our DL approach largely outperforms the RBA method in recall (+23%), precision (+2.4%), and F1 score (+14.1%). This novel approach has great potential for improving the UMLS Metathesaurus construction process by providing better synonymy suggestions to the UMLS editors.

PMID:34514472 | PMC:PMC8434895 | DOI:10.1145/3442381.3450128

Asian J Psychiatr. 2021 Aug 28;65:102820. doi: 10.1016/j.ajp.2021.102820. Online ahead of print.

ABSTRACT

BACKGROUND: Auditory emotion recognition (AER) deficits refer to the abnormal identification and interpretation of tonal or prosodic features that transmit emotional information in sounds or speech. Evidence suggests that AER deficits are related to the pathology of schizophrenia. However, the effect size of the deficit in specific emotional category recognition in schizophrenia and its association with psychotic symptoms have never been evaluated through a meta-analysis.

METHODS: A systematic search for literature published in English or Chinese until November 30, 2020 was conducted in PubMed, Embase, Web of Science, PsychINFO, and China National Knowledge Infrastructure (CNKI), WanFang and Weip Databases. AER differences between patients and healthy controls (HCs) were assessed by the standardized mean differences (SMDs). Subgroup analyses were conducted for the type of emotional stimuli and the diagnosis of schizophrenia or schizoaffective disorders (Sch/SchA). Meta-regression analyses were performed to assess the influence of patients' age, sex, illness duration, antipsychotic dose, positive and negative symptoms on the study SMDs.

RESULTS: Eighteen studies containing 615 psychosis (Sch/SchA) and 488 HCs were included in the meta-analysis. Patients exhibited moderate deficits in recognizing the neutral, happy, sad, angry, fear, disgust, and surprising emotion. Neither the semantic information in the auditory stimuli nor the diagnosis subtype affected AER deficits in schizophrenia. Sadness, anger, and disgust AER deficits were each positively associated with negative symptoms in schizophrenia.

CONCLUSIONS: Patients with schizophrenia have moderate AER deficits, which were associated with negative symptoms. Rehabilitation focusing on improving AER abilities may help improve negative symptoms and the long-term prognosis of schizophrenia.

PMID:34482183 | DOI:10.1016/j.ajp.2021.102820

J Ethnopharmacol. 2021 Sep 1:114588. doi: 10.1016/j.jep.2021.114588. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Liver disease is a major cause of illness and death worldwide which accounts for approximately 2 million deaths per year worldwide, 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. That's why it is seeking the researchers' attention to find out the effective treatment strategies. Phytochemicals from natural resources are the main leads for the development of noble hepatoprotective drugs. The majority of the natural sources whose active compounds are currently employed actually have an ethnomedical use. Ethnopharmacological research is essential for the development of these bioactive compounds. These studies not only provide scientific evidence on medicinal plants utilized for particular therapeutic purposes, but they also ensure cultural heritage preservation. Plenty of experimental studies have been well-documented that the ethnomedicinal plants are of therapeutics' interest for the advanced pharmacological intervention in terms of hepatic disorders.

AIM OF THE STUD: This study summarizes the processes of hepatotoxicity induced by various toxins and explores identified hepatoprotective plants and their phytoconstituents, which can guide the extraction of novel phytochemical constituents from plants to treat liver injury. This review aimed to summarize the hepatoprotective activity of Bangladeshi medicinal plants where the bioactive compounds may be leads for the drug discovery in future.

MATERIALS AND METHODS: Literature searches in electronic databases, such as Web of Science, Science Direct, SpringerLink, PubMed, Google Scholar, Semantic Scholar, Scopus, BanglaJOL, and so on, were performed using the keywords 'Bangladesh', 'ethnomedicinal plants', 'Hepatoprotective agents' as for primary searches, and secondary search terms were used as follows, either alone or in combination: traditional medicine, medicinal plants, folk medicine, liver, hepatitis, therapeutic uses, and anti-inflammatory. Besides, several books, including the book entitled "Medicinal plants of Bangladesh: chemical constituents and uses" authored by Abdul Ghani was carefully considered, which contained pharmacological properties and phytoconstituents of 449 medicinal plants growing and traditionally available in Bangladesh. Among them, the most promising plant species with their latest therapeutic effects against hepatic disorders were deeply considered in this review.

RESULTS: The results of this study revealed that in most cases, therapy using plant extracts stabilized altered hepatic biochemical markers induced by hepatotoxins. Initially, we investigated 32 plant species for hepatoprotective activity, however after extensive literature searching; we observed that 20 plants offer good pharmacological evidence of hepatoprotective function. Consequently, most bioactive compounds derived from the herbs including berberine, thymoquinone, andrographolide, ursolic acid, luteolin, naringenin, genistein, quercetin, troxerutin, morin, epigallocatechin-3-gallate, chlorogenic acid, emodin, curcumin, resveratrol, capsaicin, ellagic acid, etc. are appeared to be effective against hepatic disorders.

CONCLUSIONS: Flavonoids, phenolic acids, monoterpenoids, diterpenoids, triterpenoids, alkaloids, chromenes, capsaicinoids, curcuminoids, and anthraquinones are among the phytoconstituents were appraised to have hepatoprotective activities. All the actions displayed by these ethnomedicinal plants could make them serve as leads in the formulation of drugs with higher efficacy to treat hepatic disorders.

PMID:34480997 | DOI:10.1016/j.jep.2021.114588

Bioinformatics. 2021 Sep 3:btab627. doi: 10.1093/bioinformatics/btab627. Online ahead of print.

ABSTRACT

MOTIVATION: Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories.

RESULTS: The use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries.

AVAILABILITY: A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34478489 | DOI:10.1093/bioinformatics/btab627

J Imaging. 2021 Jul 22;7(8):121. doi: 10.3390/jimaging7080121.

ABSTRACT

Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

PMID:34460757 | DOI:10.3390/jimaging7080121

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2020 Dec;2020:1714-1719. doi: 10.1109/bibm49941.2020.9313375. Epub 2021 Jan 13.

ABSTRACT

A variety of severe health issues can be attributed to poor nutrition and poor eating behaviors. Research has explored the impact of nutritional knowledge on an individual's inclination to purchase and consume certain foods. This paper introduces the Ontology of Fast Food Facts, a knowledge base that models consumer nutritional data from major fast food establishments. This artifact serves as an aggregate knowledge base to centralize nutritional information for consumers. As a semantically-linked data source, the Ontology of Fast Food Facts could engender methods and tools to further the research and impact the health consumers' diet and behavior, which is a factor in many severe health outcomes. We describe the initial development of this ontology and future directions we plan with this knowledge base.

PMID:34457376 | PMC:PMC8389188 | DOI:10.1109/bibm49941.2020.9313375