Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

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

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 continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT 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.

Bibliometric analysis of research on gene expression in spinal cord injury

Background

Spinal cord injury (SCI) is a severe disease with motor and sensory function being destroyed, which leads to a poor prognosis and a serious financial burden. It is urgent to figure out the molecular and pathological mechanisms of SCI to develop feasible therapeutic strategies. This article aims to review documents focused on gene expression in SCI and summarize research hotspots and the development process in this field.

Methods

Publications of SCI-related studies from 2000 to 2022 were retrieved from the Web of Science Core Collection database. Biblioshiny was used to evaluate the research performance, core authors, journals and contributed countries, together with trend topics, hotspots in the field, and keyword co-occurrence analysis. Visualized images were obtained to help comprehension.

Results

Among 351 documents, it was found that the number of annual publications increased in general. The most productive country was China, followed by the United States with the highest influence and the most international cooperation. Plos One was the journal of the maximum publications, while Journal of Neuroscience was the most influential one. According to keyword co-occurrence and trend topics analysis, these articles mainly focused on molecular and pathological mechanisms as well as novel therapies for SCI. Neuropathic pain, axonal regeneration and messenger RNA are significant and promising research areas.

Conclusion

As the first bibliometric study focused on gene expression in SCI, we demonstrated the evolution of the field and provided future research directions like mechanisms and treatments of SCI with great innovativeness and clinical value. Further studies are recommended to develop more viable therapeutic methods for SCI.

Immunopathogenesis and immunogenetic variants in COVID-19

Abstract:

Coronavirus disease 2019 (COVID-19) continues to spread globally despite the discovery of vaccines. Many people die due to COVID-19 as a result of catastrophic consequences, such as acute respiratory distress syndrome, pulmonary embolism, and disseminated intravascular coagulation caused by a cytokine storm. Immunopathology and immunogenetic research may assist in diagnosing, predicting, and treating severe COVID-19 and the cytokine storm associated with COVID-19. This paper reviews the immunopathogenesis and immunogenetic variants that play a role in COVID-19. Although various immune-related genetic variants have been investigated in relation to severe COVID-19, the NOD-like receptor protein 3 (NLRP3) and interleukin 18 (IL-18) have not been assessed for their potential significance in the clinical outcome. Here, we a) summarize the current understanding of the immunogenetic etiology and pathophysiology of COVID-19 and the associated cytokine storm; and b) construct and analyze protein-protein interaction (PPI) networks (using enrichment and annotation analysis) based on the NLRP3 and IL18 variants and all genes, which were established in severe COVID-19. Our PPI network and enrichment analyses predict a) useful drug targets to prevent the onset of severe COVID-19 including key antiviral pathways such as Toll-Like-Receptor cascades, NOD-like receptor signaling, RIG-induction of interferon (IFN) α/β, and interleukin (IL)-1, IL-6, IL-12, IL-18, and tumor necrosis factor signaling; and b) SARS-CoV-2 innate immune evasion and the participation of MYD88 and MAVS in the pathophysiology of severe COVID-19. The PPI network genetic variants may be used to predict more severe COVID-19 outcomes, thereby opening the door for targeted preventive treatments.

Docosahexaenoic acid ester of phloridzin reduces inflammation and insulin resistance via AMPK

BACKGROUND

Docosahexaenoic acid-acylated phloridzin (PZ-DHA), a novel polyphenol fatty acid ester derivative, is synthesized through an acylation reaction of phloridzin (PZ) and docosahexaenoic acid (DHA). PZ-DHA is more stable than DHA and exhibits higher cellular uptake and bioavailability than PZ.

OBJECTIVE

To investigate the effects of PZ-DHA on insulin resistance in the skeletal muscle and the related mechanisms, we used palmitic acid (PA)-treated C2C12 myotubes as an insulin resistance model.

RESULTS

We found that PZ-DHA increased the activity of AMP-activated protein kinase (AMPK) and improved glucose uptake and mitochondrial function in an AMPK-dependent manner in untreated C2C12 myotubes. PZ-DHA treatment of the myotubes reversed PA-induced insulin resistance; this was indicated by increases in glucose uptake and the expression of membrane glucose transporter 4 (Glut4) and phosphorylated Akt. Moreover, PZ-DHA treatment reversed PA-induced inflammation and oxidative stress. These effects of PZ-DHA were mediated by AMPK. Furthermore, the increase in AMPK activity, improvement in insulin resistance, and decrease in inflammatory and oxidative responses after PZ-DHA treatment diminished upon co-treatment with a liver kinase B1 (LKB1) inhibitor, suggesting that PZ-DHA improved AMPK activity by regulating its upstream kinase, LKB1.

CONCLUSION

The effects of PZ-DHA on insulin resistance in C2C12 myotubes may be mediated by the LKB1-AMPK signaling pathway. Hence, PZ-DHA is a promising therapeutic agent for insulin resistance in type 2 diabetes.

A Web Application for Biomedical Text Mining of Scientific Literature Associated with Coronavirus-Related Syndromes: Coronavirus Finder

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses' infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application's primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.

Targeting Specific Checkpoints in the Management of SARS-CoV-2 Induced Cytokine Storm

COVID-19-infected patients require an intact immune system to suppress viral replication and prevent complications. However, the complications of SARS-CoV-2 infection that led to death were linked to the overproduction of proinflammatory cytokines known as cytokine storm syndrome. This article reported the various checkpoints targeted to manage the SARS-CoV-2-induced cytokine storm. The literature search was carried out using PubMed, Embase, MEDLINE, and China National Knowledge Infrastructure (CNKI) databases. Journal articles that discussed SARS-CoV-2 infection and cytokine storm were retrieved and appraised. Specific checkpoints identified in managing SARS-CoV-2 induced cytokine storm include a decrease in the level of Nod-Like Receptor 3 (NLRP3) inflammasome where drugs such as quercetin and anakinra were effective. Janus kinase-2 and signal transducer and activator of transcription-1 (JAK2/STAT1) signaling pathways were blocked by medicines such as tocilizumab, baricitinib, and quercetin. In addition, inhibition of interleukin (IL)-6 with dexamethasone, tocilizumab, and sarilumab effectively treats cytokine storm and significantly reduces mortality caused by COVID-19. Blockade of IL-1 with drugs such as canakinumab and anakinra, and inhibition of Bruton tyrosine kinase (BTK) with zanubrutinib and ibrutinib was also beneficial. These agents' overall mechanisms of action involve a decrease in circulating proinflammatory chemokines and cytokines and or blockade of their receptors. Consequently, the actions of these drugs significantly improve respiration and raise lymphocyte count and PaO₂/FiO₂ ratio. Targeting cytokine storms' pathogenesis genetic and molecular apparatus will substantially enhance lung function and reduce mortality due to the COVID-19 pandemic.

A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning

As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes also increases. Peripartum cardiomyopathy is a rare and poorly-characterised condition that presents as heart failure in the last trimester prior to delivery or within 5-6 months postpartum. The lack of a definitive understanding of the molecular causes and clinical progress of this condition suggests that bibliometrics will be well-suited to creating new insights into this serious clinical problem. We examine similarities and differences between peripartum and its closely related familial dilated cardiomyopathy and idiopathic dilated cardiomyopathy. Using PubMed as the source of bibliometric data, we apply artificial intelligence-supported natural language processing to compare extracted data and genes association with these cardiomyopathies. Gene data were enhanced with additional metadata from third-party datasets and then analysed for their impact and specificity for peripartum cardiomyopathy. Artificial intelligence identified 14 genes that distinguished peripartum from both dilated and familial dilated cardiomyopathy. They are as follows: CTSD, RLN2, MMP23B*, SLC17A5, ST2*, PTHLH, CFH*, CFI, GPT, MR1, Rln1, SRI, STAT5A* and THBD. We then used the Human Protein Atlas website that uses affinity-purified rabbit polyclonal antibodies to identify genes that are expressed at the protein level (bold), or as RNA transcripts (*) in healthy human left ventricles. Additional analysis focussed on the full set of peripartum genes on linkage and specificity to cardiomyopathy yielded a different set of thirteen genes (bold font indicates those expressed in cardiomyocytes: PRL, RLN2, PLN, ST2, CTSD, F2, ACE, STAT3, TTN, SPP1, LGALS3, miR-146a, GNB3, SRI). This type of analysis can highlight new avenues for research, aimed at improving genomics-driven peripartum cardiomyopathy diagnosis as well as potential pathological and clinical sub-classification. We expect that this will allow for future improvements in identification, treatment and management of this condition. The first step in the application of these bibliometric-based artificial intelligence methods is to understand the current knowledge, and it is the aim of this paper to show how this might be achieved.