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1. Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

   https://doi.org/10.3390/biology12091269  

   Kartchner, David ; McCoy, Kevin ; Dubey, Janhvi ; Zhang, Dongyu ; Zheng, Kevin ; Umrani, Rushda ; Kim, James J. ; Mitchell, Cassie S. ( September 2023 , Biology)

   Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin–angiotensin–aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.

    

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2. COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

   https://doi.org/10.1007/s11192-023-04747-w  

   Wu, Mengjia ; Zhang, Yi ; Markley, Mark ; Cassidy, Caitlin ; Newman, Nils ; Porter, Alan ( May 2023 , Scientometrics)

   Covid-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood has surged, researchers have been disadvantaged by not having access to a platform that can quickly synthesize rapidly emerging information and link the expertise it contains to established knowledge foundations. Aiming to fill this gap, in this paper we propose a research framework that can assist scientists in identifying, retrieving, and understanding Covid-19 knowledge from the ocean of scholarly articles. Incorporating Principal Component Decomposition (PDC), a knowledge model based on text analytics, and hierarchical topic tree analysis, the proposed framework profiles the research landscape, retrieves topic-specific knowledge and visualizes knowledge structures. Addressing 127,971 Covid-19 research papers from PubMed, our PCD topic analysis identifies 35 research hotspots, along with their correlations and trends. The hierarchical topic tree analysis further segments the knowledge landscape of the whole dataset into clinical and public health branches at a macro level. To supplement this analysis, we also built a knowledge model from research papers on vaccinations and fetched 92,286 pre-Covid publications as the established knowledge foundation for reference. The hierarchical topic tree analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization. 

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3. The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms

   https://doi.org/10.13012/B2IDB-4614455_V3  

   Clarke, Caitlin ; Lischwe Mueller, Natalie ; Joshi, Manasi Ballal ; Fu, Yuanxi ; Schneider, Jodi ( January 2023 , University of Illinois at Urbana-Champaign)

   The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file UPDATES IN THIS VERSION COMPARED TO V2 (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2022): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V2) - We updated file article_list.csv to fill in a missing value: row 389, article_id 440. We filled in the "date" column as 2016-08, which was missing before. 

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4. Statistical Analysis of Imbalanced Classification with Training Size Variation and Subsampling on Datasets of Research Papers in Biomedical Literature

   Dixon, J ; Rahman, M. ( December 2023 , Machine Learning and Knowledge Extraction)

   The overall purpose of this paper is to demonstrate how data preprocessing, training size variation, and subsampling can dynamically change the performance metrics of imbalanced text classification. The methodology encompasses using two different supervised learning classification approaches of feature engineering and data preprocessing with the use of five machine learning classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes, statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided into five labels from the World Health Organization Coronavirus Research Downloadable Articles of COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification that affects the performance metrics of precision, recall, receiver operating characteristic area under the curve, and accuracy. One approach that involves labeling rows of sentences based on regular expressions significantly improved the performance of imbalanced sampling techniques verified by performing statistical analysis using a t-test documenting performance metrics of iterations versus another approach that automatically labels the sentences based on how the documents are organized into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and sampling techniques in text classification datasets, with different performance levels and class imbalance issues observed in manual and automatic methods of data processing. 

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5. Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare

   https://doi.org/10.1371/journal. pone.0155001  

   Larry Carbone, Jamie Austin ( May 2016 , PloS one)

   Scientists who perform major survival surgery on laboratory animals face a dual welfare and methodological challenge: how to choose surgical anesthetics and post-operative analgesics that will best control animal suffering, knowing that both pain and the drugs that manage pain can all affect research outcomes. Scientists who publish full descriptions of animal procedures allow critical and systematic reviews of data, demonstrate their adherence to animal welfare norms, and guide other scientists on how to conduct their own studies in the field. We investigated what information on animal pain management a reasonably diligent scientist might find in planning for a successful experiment. To explore how scientists in a range of fields describe their management of this ethical and methodological concern, we scored 400 scientific articles that included major animal survival surgeries as part of their experimental methods, for the completeness of information on anesthesia and analgesia. The 400 articles (250 accepted for publication pre-2011, and 150 in 2014–15, along with 174 articles they reference) included thoracotomies, craniotomies, gonadectomies, organ transplants, peripheral nerve injuries, spinal laminectomies and orthopedic procedures in dogs, primates, swine, mice, rats and other rodents. We scored articles for Publication Completeness (PC), which was any mention of use of anesthetics or analgesics; Analgesia Use (AU) which was any use of post-surgical analgesics, and Analgesia Completeness (a composite score comprising intra-operative analgesia, extended post-surgical analgesia, and use of multimodal analgesia). 338 of 400 articles were PC. 98 of these 338 were AU, with some mention of analgesia, while 240 of 338 mentioned anesthesia only but not postsurgical analgesia. Journals’ caliber, as measured by their 2013 Impact Factor, had no effect on PC or AU. We found no effect of whether a journal instructs authors to consult the ARRIVE publishing guidelines published in 2010 on PC or AC for the 150 mouse and rat articles in our 2014–15 dataset. None of the 302 articles that were silent about analgesic use included an explicit statement that analgesics were withheld, or a discussion of how pain management or untreated pain might affect results. We conclude that current scientific literature cannot be trusted to present full detail on use of animal anesthetics and analgesics. We report that publication guidelines focus more on other potential sources of bias in experimental results, under-appreciate the potential for pain and pain drugs to skew data, PLOS ONE | DOI:10.1371/journal.pone.0155001 May 12, 2016 1 / 24 a11111 OPEN ACCESS Citation: Carbone L, Austin J (2016) Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare. PLoS ONE 11(5): e0155001. doi:10.1371/journal. pone.0155001 Editor: Chang-Qing Gao, Central South University, CHINA Received: December 29, 2015 Accepted: April 22, 2016 Published: May 12, 2016 Copyright: © 2016 Carbone, Austin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Authors may be contacted for further information. Funding: This study was funded by the United States National Science Foundation Division of Social and Economic Sciences. Award #1455838. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. and thus mostly treat pain management as solely an animal welfare concern, in the jurisdiction of animal care and use committees. At the same time, animal welfare regulations do not include guidance on publishing animal data, even though publication is an integral part of the cycle of research and can affect the welfare of animals in studies building on published work, leaving it to journals and authors to voluntarily decide what details of animal use to publish. We suggest that journals, scientists and animal welfare regulators should revise current guidelines and regulations, on treatment of pain and on transparent reporting of treatment of pain, to improve this dual welfare and data-quality deficiency. 

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