Robustness evaluations of pathway activity inference methods on gene expression data.

BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

Reimagining bioRxiv and preprint servers as platforms for academic learning.

A popular preprint server, bioRxiv, is important as a tool for increased visibility for life science research. If used properly, however, bioRxiv can also be an important tool for training, as it may expose trainees (degree-seeking students undertaking research or internships directly related to their field of study) to the peer review process. Here, we offer a comprehensive guide to using bioRxiv as a training tool, as well as offer suggestions for improvements in bioRxiv, including confusion that may be caused by bioRxiv articles appearing on PubMed.

Classification of recombinant factor VIII products and implications for clinical practice: A systematic literature review.

INTRODUCTION: Consensus over the definition of recombinant factor VIII (rFVIII) product classification in haemophilia A is lacking. rFVIII products are often classified as standard half-life (SHL) or extended half-life (EHL); despite this, no universally accepted definition currently exists. One proposed definition includes half-life, area under the curve, and technology designed to extend half-life; however, the International Society on Thrombosis and Haemostasis defines activity over time as the most intuitive information for building treatment regimens and the World Federation of Hemophilia describes rFVIII product classification in terms of infusion frequency. AIM: To summarise published data on the clinical and pharmacokinetic criteria used to define rFVIII product classification. METHODS: PubMed and EMBASE database searches of English-language articles (2002-2022) were conducted using search strings to identify the relevant population, intervention, and outcomes (e.g., clinical and pharmacokinetic parameters). Articles then underwent title/abstract and full-text screens. RESULTS: Among 1147 identified articles, 62 were included. Half-life was the most widely reported outcome with no clear trends or product groupings observed. No clear groupings emerged among other outcomes, including infusion frequency, consumption, and efficacy. As activity over time was reported in few articles, further investigation of its relevance to rFVIII product classification is warranted. CONCLUSION: The findings of this systematic literature review suggest that parameters other than half-life might be important for the development of a comprehensive and clinically relevant rFVIII product classification definition. There seems to be an opportunity to consider parameters that are clinically meaningful and useful for shared decision-making in haemophilia A treatment.

Small molecule protein kinase inhibitors approved by regulatory agencies outside of the United States.

Owing to genetic alterations and overexpression, the dysregulation of protein kinases plays a significant role in the pathogenesis of many autoimmune and neoplastic disorders and protein kinase antagonists have become an important drug target. Although the efficacy of imatinib in the treatment of chronic myelogenous leukemia in the United States in 2001 was the main driver of protein kinase inhibitor drug discovery, this was preceded by the approval of fasudil (a ROCK antagonist) in Japan in 1995 for the treatment of cerebral vasospasm. There are 21 small molecule protein kinase inhibitors that are approved in China, Japan, Europe, and South Korea that are not approved in the United Sates and 75 FDA-approved inhibitors in the United States. Of the 21 agents, eleven target receptor protein-tyrosine kinases, eight inhibit nonreceptor protein-tyrosine kinases, and two block protein-serine/threonine kinases. All 21 drugs are orally bioavailable or topically effective. Of the non-FDA approved drugs, sixteen are prescribed for the treatment of neoplastic diseases, three are directed toward inflammatory disorders, one is used for glaucoma, and fasudil is used in the management of vasospasm. The leading targets of kinase inhibitors approved by both international regulatory agencies and by the FDA are members of the EGFR family, the VEGFR family, and the JAK family. One-third of the 21 internationally approved drugs are not compliant with Lipinski's rule of five for orally bioavailable drugs. The rule of five relies on four parameters including molecular weight, number of hydrogen bond donors and acceptors, and the Log of the partition coefficient.

Rule of five violations among the FDA-approved small molecule protein kinase inhibitors.

Because genetic alterations including mutations, overexpression, translocations, and dysregulation of protein kinases are involved in the pathogenesis of many illnesses, this enzyme family is the target of many drug discovery programs in the pharmaceutical industry. Overall, the US FDA has approved 74 small molecule protein kinase inhibitors, nearly all of which are orally effective. Of the 74 approved drugs, thirty-nine block receptor protein-tyrosine kinases, nineteen target nonreceptor protein-tyrosine kinases, twelve are directed against protein-serine/threonine protein kinases, and four target dual specificity protein kinases. The data indicate that 65 of these medicinals are approved for the management of neoplasms (51 against solid tumors such as breast, colon, and lung cancers, eight against nonsolid tumors such as leukemia, and six against both types of tumors). Nine of the FDA-approved kinase inhibitors form covalent bonds with their target enzymes and they are accordingly classified as TCIs (targeted covalent inhibitors). Medicinal chemists have examined the physicochemical properties of drugs that are orally effective. Lipinski's rule of five (Ro5) is a computational procedure that is used to estimate solubility, membrane permeability, and pharmacological effectiveness in the drug-discovery setting. It relies on four parameters including molecular weight, number of hydrogen bond donors and acceptors, and the Log of the partition coefficient. Other important descriptors include the lipophilic efficiency, the polar surface area, and the number of rotatable bonds and aromatic rings. We tabulated these and other properties of the FDA-approved kinase inhibitors. Of the 74 approved drugs, 30 fail to comply with the rule of five.

A multi-omics investigation of tacrolimus off-target effects on a proximal tubule cell-line.

INTRODUCTION: Tacrolimus, an immunosuppressive drug prescribed to a majority of organ transplant recipients is nephrotoxic, through still unclear mechanisms. This study on a lineage of proximal tubular cells using a multi-omics approach aims to detect off-target pathways modulated by tacrolimus that can explain its nephrotoxicity. METHODS: LLC-PK1 cells were exposed to 5 µM of tacrolimus for 24 h in order to saturate its therapeutic target FKBP12 and other high-affine FKBPs and favour its binding to less affine targets. Intracellular proteins and metabolites, and extracellular metabolites were extracted and analysed by LC-MS/MS. The transcriptional expression of the dysregulated proteins PCK-1, as well as of the other gluconeogenesis-limiting enzymes FBP1 and FBP2, was measured using RT-qPCR. Cell viability with this concentration of tacrolimus was further checked until 72 h. RESULTS: In our cell model of acute exposure to a high concentration of tacrolimus, different metabolic pathways were impacted including those of arginine (e.g., citrulline, ornithine) (p < 0.0001), amino acids (e.g., valine, isoleucine, aspartic acid) (p < 0.0001) and pyrimidine (p < 0.01). In addition, it induced oxidative stress (p < 0.01) as shown by a decrease in total cell glutathione quantity. It impacted cell energy through an increase in Krebs cycle intermediates (e.g., citrate, aconitate, fumarate) (p < 0.01) and down-regulation of PCK-1 (p < 0.05) and FPB1 (p < 0.01), which are key enzymes in gluconeogenesis and acid-base balance control. DISCUSSION: The variations found using a multi-omics pharmacological approach clearly point towards a dysregulation of energy production and decreased gluconeogenesis, a hallmark of chronic kidney disease which may also be an important toxicity pathway of tacrolimus.

Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus.

BACKGROUND: Drug-induced suicide has been debated as a crucial issue in both clinical and public health research. Published research articles contain valuable data on the drugs associated with suicidal adverse events. An automated process that extracts such information and rapidly detects drugs related to suicide risk is essential but has not been well established. Moreover, few data sets are available for training and validating classification models on drug-induced suicide. OBJECTIVE: This study aimed to build a corpus of drug-suicide relations containing annotated entities for drugs, suicidal adverse events, and their relations. To confirm the effectiveness of the drug-suicide relation corpus, we evaluated the performance of a relation classification model using the corpus in conjunction with various embeddings. METHODS: We collected the abstracts and titles of research articles associated with drugs and suicide from PubMed and manually annotated them along with their relations at the sentence level (adverse drug events, treatment, suicide means, or miscellaneous). To reduce the manual annotation effort, we preliminarily selected sentences with a pretrained zero-shot classifier or sentences containing only drug and suicide keywords. We trained a relation classification model using various Bidirectional Encoder Representations from Transformer embeddings with the proposed corpus. We then compared the performances of the model with different Bidirectional Encoder Representations from Transformer-based embeddings and selected the most suitable embedding for our corpus. RESULTS: Our corpus comprised 11,894 sentences extracted from the titles and abstracts of the PubMed research articles. Each sentence was annotated with drug and suicide entities and the relationship between these 2 entities (adverse drug events, treatment, means, and miscellaneous). All of the tested relation classification models that were fine-tuned on the corpus accurately detected sentences of suicidal adverse events regardless of their pretrained type and data set properties. CONCLUSIONS: To our knowledge, this is the first and most extensive corpus of drug-suicide relations.

Further assessment of the clinical relevance of anatomical structures through PubMed and other measures.

A previous paper has demonstrated a statistically significant moderate correlation between the number of citations obtained from PubMed and a Delphi study for 251 anatomical structures of the Head and Neck region, suggesting that clinical significance is a major driver of research involving anatomical structures. This raises the possibility that these ranks could be an objective measure of clinical relevance of individual anatomical structures. In the present study, we revisited the rankings of the PubMed results from the previous paper and compared it with a Delphi study for 450 musculoskeletal structures. PubMed ranks were derived using different search parameters; a PubMed search with quotations yielded a moderate, statistically significant correlation coefficient of 0.639 with the musculoskeletal dataset. Additionally, we developed a Python tool, PDF Term Search, to calculate the frequency of anatomical terms in four authoritative textbooks, and these frequencies exhibited moderate significant correlations ranging (0.549-0.646) with our PubMed-derived ranks. We further explored strategies to improve the accuracy of our PubMed results by addressing limitations identified in the previous paper. We refined the syntax of search queries for 500 anatomical structures, resulting in marked improvement in the correlation coefficients with the musculoskeletal dataset, demonstrating clear avenues for future iterations of PubMed-derived ranks. We also created a spreadsheet of 2181 anatomical structures ranked using PubMed, published Delphi studies, and authoritative texts, providing a resource for anatomical educators who are adjusting their curricula to better train future healthcare practitioners.

Automated indexing using NLM's Medical Text Indexer (MTI) compared to human indexing in Medline: a pilot study.

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches.

PubMed's core clinical journals filter: redesigned for contemporary clinical impact and utility.

Objective: The Core Clinical Journals (CCJ) list, produced by the U.S. National Library of Medicine (NLM), has been used by clinicians and librarians for half a century for two main purposes: narrowing a literature search to clinically useful journals and identifying high priority titles for library collections. After documentation of low usage of the existing CCJ, a review was undertaken to assess current validity, followed by an update to current clinical needs. Methods: As the subject coverage of the 50-year-old list had never been evaluated, the CCJ committee began its innovative step-wise approach by analyzing the existing subject scope. To determine whether clinical subjects had changed over the last half-century, the committee collected data on journal usage in hospitals and medical facilities, adding journal usage from Morning Report blogs recording the journal article citations used by physicians and residents in response to clinical questions. Patient-driven high-frequency diagnoses and subjects added contextual data by depicting the clinical environment. Results: The analysis identified a total of 80 subjects and selected 241 journals for the updated Clinical Journals filter, based on actual clinical utility of each journal. Discussion: These data-driven methods created a different framework for evaluating the structure and content of this filter. It is the real-world evidence needed to highlight CCJ clinical impact and push clinically useful journals to first page results. Since the new process resulted in a new product, the name warrants a change from Core Clinical Journals (CCJ) to Clinically Useful Journals (CUJ). Therefore, the redesigned NLM Core Clinical Journals/AIM set from this point forward will be referred to as Clinically Useful Journals (CUJ). The evidence-based process used to reframe evaluation of the clinical impact and utility of biomedical journals is documented in this article.

Sub-Saharan Africa's biomedical journal coverage in scholarly databases: a comparison of Web of Science, Scopus, EMBASE, MEDLINE, African Index Medicus, and African Journals Online.

Objective: This study aims to find out the coverage of biomedical journals published in Sub-Saharan Africa in four authoritative international databases-Web of Science, Scopus, MEDLINE and EMBASE and two Africa-focused scholarly databases-Africa Journals Online (AJOL) and African Index Medicus (AIM). Methods: Lists of active journals that are published in the 46 Sub-Saharan African countries were retrieved from the Ulrich periodical directory to create master journal lists. Unique journals from other databases that were not found in Ulrich were added to the master journal list. The six databases included in this study were searched for journals on the master lists. Results: Only 23 of the 46 Sub-Saharan African countries had at least one biomedical journal. Only about one-quarter (152) of the 560 biomedical journals from Sub-Saharan Africa were found in at least one of the biomedical databases. South African journals accounted for more than 50% of all the Sub-Saharan journals in the international scholarly databases. AJOL contains the highest number of biomedical journals from Sub-Saharan Africa, followed by Scopus and EMBASE. AJOL asserts its importance by covering the highest number of unique journals and having a representative number of journals in all biomedical sub-disciplines. Conclusion: The majority of studies from Sub-Saharan Africa are left out when biomedical evidence-based researchers only retrieve studies from authoritative international databases. Searching Google Scholar and the African research databases of AJOL and AIM would increase the number of studies from the region.

Evidence surveillance for a living clinical guideline: Case study of the Australian stroke guidelines.

BACKGROUND: Continual evidence surveillance is an integral feature of living guidelines. The Australian Stroke Guidelines include recommendations on 100 clinical topics and have been 'living' since 2018. OBJECTIVES: To describe the approach for establishing and evaluating an evidence surveillance system for the living Australian Stroke Guidelines. METHODS: We developed a pragmatic surveillance system based on an analysis of the searches for the 2017 Stroke Guidelines and evaluated its reliability by assessing the potential impact on guideline recommendations. Search retrieval and screening workload are monitored monthly, together with the frequency of changes to the guideline recommendations. RESULTS: Evidence surveillance was guided by practical considerations of efficiency and sustainability. A single PubMed search covering all guideline topics, limited to systematic reviews and randomised trials, is run monthly. The search retrieves about 400 records a month of which a sixth are triaged to the guideline panels for further consideration. Evaluations with Epistemonikos and the Cochrane Stroke Trials Register demonstrated the robustness of adopting this more restrictive approach. Collaborating with the guideline team in designing, implementing and evaluating the surveillance is essential for optimising the approach. CONCLUSION: Monthly evidence surveillance for a large living guideline is feasible and sustainable when applying a pragmatic approach.

Glycolysis inhibition ameliorates brain injury after ischemic stroke by promoting the function of myeloid-derived suppressor cells.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells which are immunosuppressive and glycolytically inactive in inflammatory diseases. However, it is unknown whether MDSCs contribute to ischemic stroke and how glycolysis regulates MDSC function in such a context. Here, we showed that MDSCs arise in the blood of patients at early phase of stroke. Similar results were observed in temporary middle cerebral artery occlusion-induced cerebral ischemic mice. Pharmaceutical exhaustion of MDSCs aggravated, while adoptive transfer of MDSCs rescued the ischemic brain injury. However, the differentiation of MDSCs into immunopotent myeloid cells which coincides with increased glycolysis was observed in the context of ischemic stroke. Mechanistically, the glycolytic product lactate autonomously induces MDSC differentiation through activation of mTORC1, and paracrinely activates Th1 and Th17 cells. Moreover, gene knockout or inhibition of the glycolytic enzyme PFKFB3 increased endogenous MDSCs by blocking their differentiation, and improved ischemic brain injury. Collectively, these results revealed that glycolytic switch decreases the immunosuppressive and neuroprotective role of MDSCs in ischemic stroke and pharmacological targeting MDSCs via glycolysis inhibition constitutes a promising therapeutic strategy for ischemic stroke.

Network analysis for estimating standardization trends in genomics using MEDLINE.

BACKGROUND: Biotechnology in genomics, such as sequencing devices and gene quantification software, has proliferated and been applied to clinical settings. However, the lack of standards applicable to it poses practical problems in interoperability and reusability of the technology across various application domains. This study aims to visualize and identify the standard trends in clinical genomics and to suggest areas on which standardization efforts must focus. METHODS: Of 16,538 articles retrieved from PubMed, published from 1975 to 2020, using search keywords "genomics and standard" and "clinical genomic sequence and standard", terms were extracted from the abstracts and titles of 15,855 articles. Our analysis includes (1) network analysis of full phases (2) period analysis with five phases; (3) statistical analysis; (4) content analysis. RESULTS: Our research trend showed an increasing trend from 2003, years marked by the completion of the human genome project (2003). The content analysis showed that keywords related to such concepts as gene types for analysis, and analysis techniques were increased in phase 3 when US-FDA first approved the next-generation sequencer. During 2017-2019, oncology-relevant terms were clustered and contributed to the increasing trend in phase 4 of the content analysis. In the statistical analysis, all the categories showed high regression values (R2 > 0.586) throughout the whole analysis period and phase-based statistical analysis showed significance only in the Genetics terminology category (P = .039*) at phase 4. CONCLUSIONS: Through comprehensive trend analysis from our study, we provided the trend shifts and high-demand items in standardization for clinical genetics.

Neuroleptic intolerance in the context of anti-N-methyl-D-aspartate receptor encephalitis: A systematic review and synthesis of global case reports.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis presents commonly with psychiatric symptoms. One cohort of these patients reported that antipsychotic administration led to neuroleptic intolerance (NI) in 19% of them, which was preventable by a prompt encephalitis diagnosis. To date, there is no clear description of the "neuroleptic intolerance" spectrum in general or during anti-NMDAR encephalitis. We aimed to synthesize epidemiological and clinical characteristics of patients with NI and confirmed anti-NMDAR encephalitis, the time to the encephalitis diagnosis, the disease course, outcomes at discharge, and associated factors. We systematically searched three databases, to include clinical cases, case series, and observational studies. Additionally, we reported one clinical case. Results were summarized using narrative synthesis and the quality of the included studies was assessed. We included 22 records representing 40 patients (28 females; mean age, 24.6). Overall, the evidence quality was low. Initially, most cases were admitted in psychiatric wards (70%) with purely psychiatric symptoms (37.5%). However, most of them developed subtle concomitant neurological symptoms. The mean time to anti-NMDAR encephalitis diagnosis was 26.7 days, which was triggered by the NI in six patients. We found no association between clinical variables as delayed diagnosis, admission to psychiatric wards or the presence of malignancy with outcome variables as unfavorable outcomes at discharge, ICU, or mechanical ventilation requirement. A thorough neurological examination in young patients with new-onset psychiatric symptoms could help emergency physicians, neurologists, and psychiatrists suspect anti-NMDAR encephalitis earlier. Awareness of NI as a potential side effect during suspected or confirmed anti-NMDAR encephalitis is encouraged.

Towards a unified search: Improving PubMed retrieval with full text.

OBJECTIVE: A significant number of recent articles in PubMed have full text available in PubMed Central®, and the availability of full texts has been consistently growing. However, it is not currently possible for a user to simultaneously query the contents of both databases and receive a single integrated search result. In this study, we investigate how to score full text articles given a multitoken query and how to combine those full text article scores with scores originating from abstracts and achieve an overall improved retrieval performance. MATERIALS AND METHODS: For scoring full text articles, we propose a method to combine information coming from different sections by converting the traditionally used BM25 scores into log odds ratio scores which can be treated uniformly. We further propose a method that successfully combines scores from two heterogenous retrieval sources - full text articles and abstract only articles - by balancing the contributions of their respective scores through a probabilistic transformation. We use PubMed click data that consists of queries sampled from PubMed user logs along with a subset of retrieved and clicked documents to train the probabilistic functions and to evaluate retrieval effectiveness. RESULTS AND CONCLUSIONS: Random ranking achieves 0.579 MAP score on our PubMed click data. BM25 ranking on PubMed abstracts improves the MAP by 10.6%. For full text documents, experiments confirm that BM25 section scores are of different value depending on the section type and are not directly comparable. Naïvely using the body text of articles along with abstract text degrades the overall quality of the search. The proposed log odds ratio scores normalize and combine the contributions of occurrences of query tokens in different sections. By including full text where available, we gain another 0.67%, or 7% relative improvement over abstract alone. We find an advantage in the more accurate estimate of the value of BM25 scores depending on the section from which they were produced. Taking the sum of top three section scores performs the best.

Antihypertensive medicine use differs between Ghana and Nigeria.

BACKGROUND: Non-communicable diseases are a growing burden in many African countries; cardiovascular disease is the main disease. Antihypertensive medicines (AHM) are a common treatment option but we know little about community use in most low- and medium-income countries (LMIC). We aimed to describe the use of antihypertensive medicines (AHM) in Ghana and Nigeria using a novel data source. METHODS: We used data from mPharma-a health and pharmaceutical company which distributes pharmaceuticals to hospital and retail pharmacies. We extracted data using the anatomical therapeutic chemical (ATC) classification codes and calculated use in defined daily doses and explored patterns by class, medicines, dose, and originator or generic product. RESULTS: AHM use differed between Ghana and Nigeria. The most used classes in Ghana were angiotensin receptor blockers (ARB) followed by calcium channel blockers (CCB) and angiotensin-converting-enzyme inhibitors (ACEi). The five most used products were 16 mg candesartan, 30 mg nifedipine, 10 mg lisinopril, 5 mg amlodipine and 50 mg losartan. In Nigeria ARB, CCB and diuretics were widely used; the top five products were 50 mg losartan, 10 mg lisinopril, 30 mg nifedipine, 40 mg furosemide, and 5 mg amlodipine. More originator products were used in Ghana than Nigeria. CONCLUSION: The differences between Ghana and Nigeria may result from a combination of medical, contextual and policy evidence and reflect factors related to clinical guidance (e.g. standard treatment guidelines), accessibility to prescribers and the role of community pharmacies, and structure of the health system and universal health coverage including funding for medicines. We show the feasibility of using novel data sources to gain insights on medicines use in the community.

Deciphering the Diversity of Mental Models in Neurodevelopmental Disorders: Knowledge Graph Representation of Public Data Using Natural Language Processing.

BACKGROUND: Understanding how individuals think about a topic, known as the mental model, can significantly improve communication, especially in the medical domain where emotions and implications are high. Neurodevelopmental disorders (NDDs) represent a group of diagnoses, affecting up to 18% of the global population, involving differences in the development of cognitive or social functions. In this study, we focus on 2 NDDs, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), which involve multiple symptoms and interventions requiring interactions between 2 important stakeholders: parents and health professionals. There is a gap in our understanding of differences between mental models for each stakeholder, making communication between stakeholders more difficult than it could be. OBJECTIVE: We aim to build knowledge graphs (KGs) from web-based information relevant to each stakeholder as proxies of mental models. These KGs will accelerate the identification of shared and divergent concerns between stakeholders. The developed KGs can help improve knowledge mobilization, communication, and care for individuals with ADHD and ASD. METHODS: We created 2 data sets by collecting the posts from web-based forums and PubMed abstracts related to ADHD and ASD. We utilized the Unified Medical Language System (UMLS) to detect biomedical concepts and applied Positive Pointwise Mutual Information followed by truncated Singular Value Decomposition to obtain corpus-based concept embeddings for each data set. Each data set is represented as a KG using a property graph model. Semantic relatedness between concepts is calculated to rank the relation strength of concepts and stored in the KG as relation weights. UMLS disorder-relevant semantic types are used to provide additional categorical information about each concept's domain. RESULTS: The developed KGs contain concepts from both data sets, with node sizes representing the co-occurrence frequency of concepts and edge sizes representing relevance between concepts. ADHD- and ASD-related concepts from different semantic types shows diverse areas of concerns and complex needs of the conditions. KG identifies converging and diverging concepts between health professionals literature (PubMed) and parental concerns (web-based forums), which may correspond to the differences between mental models for each stakeholder. CONCLUSIONS: We show for the first time that generating KGs from web-based data can capture the complex needs of families dealing with ADHD or ASD. Moreover, we showed points of convergence between families and health professionals' KGs. Natural language processing-based KG provides access to a large sample size, which is often a limiting factor for traditional in-person mental model mapping. Our work offers a high throughput access to mental model maps, which could be used for further in-person validation, knowledge mobilization projects, and basis for communication about potential blind spots from stakeholders in interactions about NDDs. Future research will be needed to identify how concepts could interact together differently for each stakeholder.

Biological databases and tools for neurological disorders.

Computational approach to study of neuronal impairment is rapidly evolving, as experiments and intuition alone could not explain the complexity of brain system. The increase in an overwhelming amount of new data from both theory and computational modeling necessitate the development of databases and tools for analysis, visualization, and interpretation of neuroscience data. To ensure the sustainability of this development, consistent update and training of young professionals are imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. Therefore, this article seeks to outline the biological databases and analytical tools along with their applications. It's envisaged that knowledge along this line would be a "training recipe" for young talents and guide for professionals and researchers in neuroscience.

Research hotspots and trends in nursing education from 2014 to 2020: A co-word analysis based on keywords.

AIMS: To investigate research hotspots and trends in nursing education from 2014 to 2020, and provide references for researchers to understand the research status and developing trends in this field. DESIGN: A co-word analysis based on keywords. METHODS: Data were obtained from nursing education-related academic research articles that were retrieved through a literature search using PubMed during the period of 2014-2020. Keywords included in the analysis of literature were considered as the research objects. Bibliographic Item Co-occurrence Matrix Builder (BICOMB) was employed to extract high-frequency keywords and generate co-occurrence matrix, graphical clustering toolkit (gCLUTO) was used to perform cluster analysis, and SPSS 25.0 was used to perform strategic diagram analysis. RESULTS: Overall, 7857 articles were retrieved, from which 2679 keywords were obtained and 64 high-frequency keywords extracted. The results revealed seven hotspots in nursing education during the period of 2014-2020, which included research on: (i) continuing education in nursing, (ii) application and influence of the internet in nursing education, (iii) postgraduate nursing education, (iv) undergraduate nursing education and clinical quality training, (v) current development status and tendency of nursing education, (vi) nursing teaching methods and (vii) organization and management in nursing education. CONCLUSION: The seven research hotspots could reflect the publication trends in nursing education. By providing a co-word analysis-including cluster and strategic diagram analysis-an overall command of the latest hotspots can be depicted, and researchers conducting research in nursing education can have some hints. IMPACT: This study allows the development of future research on nursing education. Future researchers should explore the application of new network technologies in the process of nursing teaching, quality of postgraduate nursing education, innovative teaching methods as well as teaching strategies of improving students' clinical ability, current situation of economics and leadership in nursing education and ability of organization and management in undergraduate nursing education.