Literature-related discovery: Potential treatments and preventatives for SARS

Graph embedding-based link prediction for literature-based discovery in Alzheimer's Disease

OBJECTIVE

We explore the framing of literature-based discovery (LBD) as link prediction and graph embedding learning, with Alzheimer's Disease (AD) as our focus disease context. The key link prediction setting of prediction window length is specifically examined in the context of a time-sliced evaluation methodology.

METHODS

We propose a four-stage approach to explore literature-based discovery for Alzheimer's Disease, creating and analyzing a knowledge graph tailored to the AD context, and predicting and evaluating new knowledge based on time-sliced link prediction. The first stage is to collect an AD-specific corpus. The second stage involves constructing an AD knowledge graph with identified AD-specific concepts and relations from the corpus. In the third stage, 20 pairs of training and testing datasets are constructed with the time-slicing methodology. Finally, we infer new knowledge with graph embedding-based link prediction methods. We compare different link prediction methods in this context. The impact of limiting prediction evaluation of LBD models in the context of short-term and longer-term knowledge evolution for Alzheimer's Disease is assessed.

RESULTS

We constructed an AD corpus of over 16 k papers published in 1977-2021, and automatically annotated it with concepts and relations covering 11 AD-specific semantic entity types. The knowledge graph of Alzheimer's Disease derived from this resource consisted of ∼11 k nodes and ∼394 k edges, among which 34% were genotype-phenotype relationships, 57% were genotype-genotype relationships, and 9% were phenotype-phenotype relationships. A Structural Deep Network Embedding (SDNE) model consistently showed the best performance in terms of returning the most confident set of link predictions as time progresses over 20 years. A huge improvement in model performance was observed when changing the link prediction evaluation setting to consider a more distant future, reflecting the time required for knowledge accumulation.

CONCLUSION

Neural network graph-embedding link prediction methods show promise for the literature-based discovery context, although the prediction setting is extremely challenging, with graph densities of less than 1%. Varying prediction window length on the time-sliced evaluation methodology leads to hugely different results and interpretations of LBD studies. Our approach can be generalized to enable knowledge discovery for other diseases.

AVAILABILITY

Code, AD ontology, and data are available at https://github.com/READ-BioMed/readbiomed-lbd.

Efficiency and herding analysis in gold-backed cryptocurrencies

This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.

A Framework Based on Blockchain, Artificial Intelligence, and Big Data Analytics to Leverage Supply Chain Resilience considering the COVID-19

In the global supply chains era, firms are more connected, integrated, and interdependent, bringing along a set of benefits and a number of risks. It is clear that the singular COVID-19 epidemic outbreak has led to unparalleled disruptions and considerable challenges for supply chains (SCs). For example, the sluggish economic environment provoked by the COVID-19 has negatively impacted the flow of goods, generating shortages and interruptions through the SCs. At the global level, many markets are enduring the effects of these disruptions. In this challenging context, the firms and their SCs must apply useful and efficient strategies to minimize and adapt their operations during and after these disruptions. In this view, this study aims to propose a novel framework based on Artificial Intelligence, Blockchain, and Big Data Analytics, to bring useful ideas and contribute to overcoming such disruptions. Besides, we propose novel categorizations that can support new insights for scholars and practitioners about the use of cutting-edge technologies during and after severe disruptions.

WASH to control COVID-19: A rapid review

Background

Preventive public health has been suggested as methods for reducing the transmission of COVID-19. Safety and efficacy of one such public health measure: WASH intervention for COVID-19 has not been systematically reviewed. We undertook a rapid review to assess the effect of WASH intervention in reducing the incidence of COVID-19.

Methods

We conducted searches in PubMed, MEDLINE, and EMBASE. We undertook screening of studies in two stages and extracted data and assessed the quality of evidence for the primary outcome using GRADE recommendations.

Main results

We included a total of 13 studies with three studies on COVID-19 and 10 on SARS. The study found that hand washing, sterilization of hands, gargling, cleaning/shower after attending patients of COVID-19, or SARS was protective. Evidence also found that frequent washes can prevent SARS transmission among HCWs. However; one study reported that due to enhanced infection-prevention measures, front-line HCWs are more prone to hand-skin damage. The certainty of the evidence for our primary outcome as per GRADE was very low. We did not find any studies that assessed the effect of WASH on hospitalizations, and mortality due to COVID-19. Also; we did not find any study that compared WASH interventions with any other public health measures.

Conclusions

Current evidence of WASH interventions for COVID-19 is limited as it is largely based on indirect evidence from SARS. Findings from the included studies consistently show that WASH is important in reducing the number of cases during a pandemic. Timely implementation of WASH along with other public health interventions can be vital to ensure the desired success. Further good-quality studies providing direct evidence of the efficacy of WASH on COVID-19 are needed.

A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

Purpose

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.

Design/methodology/approach

The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.

Findings

The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.

Originality/value

First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Using data mining techniques to fight and control epidemics: A scoping review

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Human coronaviruses and therapeutic drug discovery

Background

Coronaviruses (CoVs) are distributed worldwide and have various susceptible hosts; CoVs infecting humans are called human coronaviruses (HCoVs). Although HCoV-specific drugs are still lacking, many potent targets for drug discovery are being explored, and many vigorously designed clinical trials are being carried out in an orderly manner. The aim of this review was to gain a comprehensive understanding of the current status of drug development against HCoVs, particularly severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Main text

A scoping review was conducted by electronically searching research studies, reviews, and clinical trials in PubMed and the CNKI. Studies on HCoVs and therapeutic drug discovery published between January 2000 and October 2020 and in English or Chinese were included, and the information was summarized. Of the 3248 studies identified, 159 publication were finally included. Advances in drug development against HCoV, especially SARS-CoV-2, are summarized under three categories: antiviral drugs aimed at inhibiting the HCoV proliferation process, drugs acting on the host's immune system, and drugs derived from plants with potent activity. Furthermore, clinical trials of drugs targeting SARS-CoV-2 are summarized.

Conclusions

During the spread of COVID-19 outbreak, great efforts have been made in therapeutic drug discovery against the virus, although the pharmacological effects and adverse reactions of some drugs under study are still unclear. However, well-designed high-quality studies are needed to further study the effectiveness and safety of these potential drugs so as to provide valid recommendations for better control of the COVID-19 pandemic.

Impact of COVID-19 on the travel and tourism industry

Our paper is among the first to measure the potential effects of the COVID-19 pandemic on the tourism industry. Using panel structural vector auto-regression (PSVAR) (Pedroni, 2013) on data from 1995 to 2019 in 185 countries and system dynamic modeling (real-time data parameters connected to COVID-19), we estimate the impact of the pandemic crisis on the tourism industry worldwide. Past pandemic crises operated mostly through idiosyncratic shocks' channels, exposing domestic tourism sectors to large adverse shocks. Once domestic shocks perished (zero infection cases), inbound arrivals revived immediately. The COVID-19 pandemic, however, is different; and recovery of the tourism industry worldwide will take more time than the average expected recovery period of 10 months. Private and public policy support must be coordinated to assure capacity building and operational sustainability of the travel tourism sector during 2020-2021. COVID-19 proves that pandemic outbreaks have a much larger destructive impact on the travel and tourism industry than previous studies indicate. Tourism managers must carefully assess the effects of epidemics on business and develop new risk management methods to deal with the crisis. Furthermore, during 2020-2021, private and public policy support must be coordinated to sustain pre-COVID-19 operational levels of the tourism and travel sector.

Impact of COVID-19 on the travel and tourism industry

Our paper is among the first to measure the potential effects of the COVID-19 pandemic on the tourism industry. Using panel structural vector auto-regression (PSVAR) (Pedroni, 2013) on data from 1995 to 2019 in 185 countries and system dynamic modeling (real-time data parameters connected to COVID-19), we estimate the impact of the pandemic crisis on the tourism industry worldwide. Past pandemic crises operated mostly through idiosyncratic shocks' channels, exposing domestic tourism sectors to large adverse shocks. Once domestic shocks perished (zero infection cases), inbound arrivals revived immediately. The COVID-19 pandemic, however, is different; and recovery of the tourism industry worldwide will take more time than the average expected recovery period of 10 months. Private and public policy support must be coordinated to assure capacity building and operational sustainability of the travel tourism sector during 2020-2021. COVID-19 proves that pandemic outbreaks have a much larger destructive impact on the travel and tourism industry than previous studies indicate. Tourism managers must carefully assess the effects of epidemics on business and develop new risk management methods to deal with the crisis. Furthermore, during 2020-2021, private and public policy support must be coordinated to sustain pre-COVID-19 operational levels of the tourism and travel sector.

Tracking and Mining the COVID-19 Research Literature

The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.

A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure

Corona pandemic has affected the whole world, and it is a highly researched area in biological sciences. As the current pandemic has affected countries socially and economically, the purpose of this bibliometric analysis is to provide a holistic review of the corona pandemic in the field of social sciences. This study aims to highlight significant, influential aspects, research streams, and themes. We have reviewed 395 journal articles related to coronavirus in the field of social sciences from 2003 to 2020. We have deployed 'biblioshiny' a web-interface of the 'bibliometrix 3.0' package of R-studio to conduct bibliometric analysis and visualization. In the field of social sciences, we have reported influential aspects of coronavirus literature. We have found that the 'Morbidity and Mortality Weekly Report' is the top journal. The core article of coronavirus literature is 'Guidelines for preventing health-care-associated pneumonia'. The most commonly used word, in titles, abstracts, author's keywords, and keywords plus, is 'SARS'. Top affiliation is 'The University of Hong Kong'. Hong Kong is a leading country based on citations, and the USA is on top based on total publications. We have used a conceptual framework to identify potential research streams and themes in coronavirus literature. Four research streams are found by deploying a co-occurrence network. These research streams are 'Social and economic effects of epidemic disease', 'Infectious disease calamities and control', 'Outbreak of COVID 19,' and 'Infectious diseases and the role of international organizations'. Finally, a thematic map is used to provide a holistic understanding by dividing significant themes into basic or transversal, emerging or declining, motor, highly developed, but isolated themes. These themes and subthemes have proposed future directions and critical areas of research.

Potential use of polyphenols in the battle against COVID-19

The coronavirus disease 2019 (COVID-19) is a public health emergency of international concern. The rising number of cases of this highly transmissible infection has stressed the urgent need to find a potent drug. Although repurposing of known drugs currently provides an accelerated route to approval, there is no satisfactory treatment. Polyphenols, a major class of bioactive compounds in nature, are known for their antiviral activity and pleiotropic effects. The aim of this review is to assess the effects of polyphenols on COVID-19 drug targets as well as to provide a perspective on the possibility to use polyphenols in the development of natural approaches against this viral disease.

A systematic review on literature-based discovery workflow

As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to the input component, we discuss the data types and data sources used in the literature. The process component presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, the output component focuses on the visualisation techniques used in LBD discipline. As for the evaluation component, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions.

Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation

Science foresight comprises a range of methods to analyze past, present and expected research trends, and uses this information to predict the future status of different fields of science and technology. With the ability to identify high-potential development directions, science foresight can be a useful tool to support the management and planning of future research activities. Science foresight analysts can choose from a rather large variety of approaches. There is, however, relatively little information about how the various approaches can be applied in an effective way. This paper describes a three-step methodological framework for science foresight on the basis of published research papers, consisting of (i) life-cycle analysis, (ii) text mining and (iii) knowledge gap identification by means of automated clustering. The three steps are connected using the research methodology of the research papers, as identified by text mining. The potential of combining these three steps in one framework is illustrated by analyzing scientific literature on wind catchers; a natural ventilation concept which has received considerable attention from academia, but with quite low application in practice. The knowledge gaps that are identified show that the automated foresight analysis is indeed able to find uncharted research areas. Results from a sensitivity analysis further show the importance of using full-texts for text mining instead of only title, keywords and abstract. The paper concludes with a reflection on the methodological framework, and gives directions for its intended use in future studies.

Synergistic effect of methionine encephalin (MENK) combined with pidotimod(PTD) on the maturation of murine dendritic cells (DCs)

To gain new insight into the functional interaction between dendritic cells and methionine encephalin (MENK) combined with pidotimod (PTD), we have analyzed the effect of MENK plus PTD on the morphology, phenotype and functions of murine bone-marrow derived dendritic cells (BMDCs) in vitro. The maturation of BMDCs cultured in the presence of either MENK or PTD alone, or MENK in combination with PTD, was detected. The cell proliferation was measured by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxy-methoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt/phenazinemethosulphate (MTS/PMS). The changes of BMDCs morphology were confirmed with light microscopy, transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The BMDCs treated with MENK combined with PTD displayed a higher expression of typical maturation markers of CD40, CD80, CD83, CD86 and MHC-IIidentified by fluorescence activated cell sorting (FACS), and stronger ability to drive T cells. The decrease of the endocytic ability was assayed by DAB kit, FITC-dextran and cellular immunohistochemistry. Finally upregulation of cytokines production of IL-12 and TNF-α was determined by ELISA. These data indicate that MENK combined with PTD could exert synergistic action on BMDC maturation.

Literature-related discovery and innovation - update

Literature-Related Discovery and Innovation (LRDI - formerly LRD - literature-related discovery) integrates 1) discovery generation from disparate literatures with 2) the wealth of knowledge contained in prior art to 3) potentially reverse chronic and infectious diseases and/or 4) potentially solve technical problems that appear intractable. This article describes the evolution of LRDI by the author and the insights gained/lessons learned over the past decade. To illustrate the potential power of LRDI, the article emphasizes the relationship between the results of our 2008 LRDI multiple sclerosis (MS) study and a recent demonstration of MS reversal. Lessons learned from the six LRDI medical studies done so far include:⁎The main operational problem in the author's LRDI approach is selecting the most important concepts from extremely large volumes of potential discovery retrieval. This is contrary to most published LRDI research, where the discovery focus is searching for rare events.⁎It is important to have topical specialist(s) working closely with information technologist(s); the topical specialist(s) applies judgment in selecting the most important concepts.⁎A functional form of the information retrieval query with proximity searching capability provides highly selective filtering for discovery retrieval and core prevention/treatment retrieval; the functional form of the query with proximity searching capability allows the use of full-text for discovery and core prevention/treatment.⁎Bibliographic coupling (identifying papers that share common references) combined with text-based relationships strengthens selection for potential discovery further.⁎Having 'skin-in-the-game' (being affected personally) relative to the medical outcome is a strong incentive to do whatever is necessary to solve the research problem.⁎Hormesis is critical to healing; relatively modest doses of stimuli tend to be beneficial, whereas relatively large doses may be harmful. The synergy of hormetic treatment doses produces effects larger than combinations of individual doses and requires smaller doses when combined; the synergy of hormetic doses allows conversion of megadoses of nutrients typically reported in lab/clinical studies to physiological (food-level) doses and associated increased safety.⁎Co-promoters (combinations of toxic stimuli required to produce disease symptoms) are extremely important for explaining seemingly conflicting results; if true co-promotion is present, elimination of one of the co-promoters may be adequate for removing symptoms, even though the overall problem persists.⁎Prior art (potential treatments already published in the literature but not pursued by mainline medicine) may have much to contribute to potentially solve many serious medical problems; much of prior art is overlooked, especially low-tech prior art (e.g., foods, food extracts, herbs, etc.).⁎Systemic and focused treatments are both necessary components of healing, but neither will be fully, or many times even partially, effective until the cause(s) is identified and removed. Any medical approach that involves administering treatments for chronic and infectious diseases without addressing the cause(s) results in a broad range of outcomes mainly involving substitution of one set of symptoms for another.⁎Past results of LRDI medical studies showed much overlap among preventatives/systemic treatments for different diseases. Differences will arise mainly in focused treatments, especially those involving high technology.⁎The central parameters to healing in much medical research are never identified nor reported. Many treatments require a combination of skilled practitioners, cause removal, and immune/neural/endocrine/circulatory systems to be healthy for full effectiveness, yet practitioner skill, degree of cause removal, and immune system et al. health are never reported. A lack of this information does not allow efficacy of different treatments to be compared. Reviews and meta-analyses that compare and draw conclusions about the effectiveness of these different treatments without the above critical information being reported are of extremely limited value and credibility.⁎Finally, the most important deficiency for fully reversing chronic and infectious diseases, as well as rapidly accelerating healing of injuries and wounds, is the credibility and integrity of the medical literature itself, especially in areas that concern commercial and government/political sensitivities. In the evaluation of many concepts that deviated from the norm, it was difficult to ascertain whether the difference was based on solid high-quality research, poor research, or deliberately skewed research.