PubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.

BACKGROUND The development of venous thromboembolism (VTE) is associated with high mortality among gastric cancer (GC) patients. Neutrophil extracellular traps (NETs) have been reported to correlate with the prothrombotic state in some diseases, but are rarely reported in GC patients. AIM To investigate the effect of NETs on the development of cancer-associated thrombosis in GC patients. METHODS The levels of NETs in blood and tissue samples of patients were analyzed by ELISA, flow cytometry, and immunofluorescence staining. NET generation and hypercoagulation of platelets and endothelial cells (ECs) in vitro were observed by immunofluorescence staining. NET procoagulant activity (PCA) was determined by fibrin formation and thrombin–antithrombin complex (TAT) assays. Thrombosis in vivo was measured in a murine model induced by flow stenosis in the inferior vena cava (IVC). RESULTS NETs were likely to form in blood and tissue samples of GC patients compared with healthy individuals. In vitro studies showed that GC cells and their conditioned medium, but not gastric mucosal epithelial cells, stimulated NET release from neutrophils. In addition, NETs induced a hypercoagulable state of platelets by upregulating the expression of phosphatidylserine and P-selectin on the cells. Furthermore, NETs stimulated the adhesion of normal platelets on glass surfaces. Similarly, NETs triggered the conversion of ECs to hypercoagulable phenotypes by downregulating the expression of their intercellular tight junctions but upregulating that of tissue factor. Treatment of normal platelets or ECs with NETs augmented the level of plasma fibrin formation and the TAT complex. In the models of IVC stenosis, tumor-bearing mice showed a stronger ability to form thrombi, and NETs abundantly accumulated in the thrombi of tumor-bearing mice compared with control mice. Notably, the combination of deoxyribonuclease I, activated protein C, and sivelestat markedly abolished the PCA of NETs. CONCLUSION GC-induced NETs strongly increased the risk of VTE development both in vitro and in vivo . NETs are potential therapeutic targets in the prevention and treatment of VTE in GC patients.

Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar and different. We then evaluate the SOTA VQA and VQA-Grounding models and demonstrate that current SOTA algorithms often fail to identify the correct visual evidence where the answer is located. These models regularly struggle when the visual evidence occupies a small fraction of the image, for images that are higher quality, as well as for visual questions that require skills in text recognition. The dataset, evaluation server, and leaderboard all can be found at the following link: https://vizwiz.org/tasksand-datasets/answer-grounding-for-vqa/.

Published data on the association between microRNA-499 (miR-499) rs3746444 T>C polymorphism and cancer susceptibility are inconclusive. To derive a more precise estimation of this relationship, a comprehensive meta-analysis was performed on nine published studies, with a total sample of 4,794 cases and 5,971 controls. Overall, no significant association was found between miR-499 polymorphism and cancer risk after all studies were pooled into the meta-analysis. However, in the subgroup analysis by ethnicity, significant association with an increased risk was found in Asian (CC vs. TT: OR = 1.439, 95 % CI = 1.118-1.852, P = 0.005, p-heterogeneity = 0.116). Moreover, in the the subgroup analysis by cancer type, this SNP was associated with an increased risk of breast cancer in the recessive model (OR = 1.077, 95 % CI = 1.008-1.151, P = 0.028, p-heterogeneity = 0.125). Our findings support the view that miR-499 rs3746444 T>C polymorphism is associated with breast cancer and the C allele can increase cancer susceptibility in Asian.

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG’s usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.

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COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.