Tracing open data in emergencies: The case of the COVID-19 pandemic

Nutrition in Pediatric Patients and Vulnerable Populations: Updates and Advances

Nutrition is a modifiable factor of paramount importance for the prevention and attainment of health and the development of youngsters [...].

Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data

OBJECTIVES

To synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and what factors influence availability.

DESIGN

Systematic review with meta-analysis of individual participant data.

DATA SOURCES

Ovid Medline, Ovid Embase, and the preprint servers medRxiv, bioRxiv, and MetaArXiv were searched from inception to 1 July 2021. Forward citation searches were also performed on 30 August 2022.

REVIEW METHODS

Meta-research studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research were identified. Two authors screened records, assessed the risk of bias, and extracted summary data from study reports when individual participant data could not be retrieved. Key outcomes of interest were the prevalence of statements that declared that data or code were publicly or privately available (declared availability) and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (eg, journal policy, type of data, trial design, and human participants) were also examined. A two stage approach to meta-analysis of individual participant data was performed, with proportions and risk ratios pooled with the Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis.

RESULTS

The review included 105 meta-research studies examining 2 121 580 articles across 31 specialties. Eligible studies examined a median of 195 primary articles (interquartile range 113-475), with a median publication year of 2015 (interquartile range 2012-2018). Only eight studies (8%) were classified as having a low risk of bias. Meta-analyses showed a prevalence of declared and actual public data availability of 8% (95% confidence interval 5% to 11%) and 2% (1% to 3%), respectively, between 2016 and 2021. For public code sharing, both the prevalence of declared and actual availability were estimated to be <0.5% since 2016. Meta-regressions indicated that only declared public data sharing prevalence estimates have increased over time. Compliance with mandatory data sharing policies ranged from 0% to 100% across journals and varied by type of data. In contrast, success in privately obtaining data and code from authors historically ranged between 0% and 37% and 0% and 23%, respectively.

CONCLUSIONS

The review found that public code sharing was persistently low across medical research. Declarations of data sharing were also low, increasing over time, but did not always correspond to actual sharing of data. The effectiveness of mandatory data sharing policies varied substantially by journal and type of data, a finding that might be informative for policy makers when designing policies and allocating resources to audit compliance.

SYSTEMATIC REVIEW REGISTRATION

Open Science Framework doi:10.17605/OSF.IO/7SX8U.

Analysis of shared research data in Spanish scientific papers about COVID-19: A first approach

During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.

Public View of Public Health Emergencies Based on Artificial Intelligence Data

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people's health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people's hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO's optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.

Development of a recombinant nucleocapsid protein-based ELISA for the detection of IgM and IgG antibodies to SARS-CoV-2

Coronavirus 2019 (COVID-19) is a global concern for public health. Thus, early and accurate diagnosis is a critical step in management of this infectious disease. Currently, RT-PCR is routine diagnosis test for COVID-19, but it has some limitations and false negative results. enzyme-linked immunosorbent assay (ELISA) against SARS-CoV-2 antigens seems to be an appropriate approach for serodiagnosis of COVID-19. In the current study, an ELISA system, using a recombinant nucleocapsid (N) protein, was developed for the detection of IgM and IgG antibodies to SARS-CoV-2. The related protein was expressed, purified, and used in an ELISA system. Sera samples (67) for COVID-19 patients, as well as sera samples from healthy volunteers (112), along with sera samples from non-COVID-19 patients were examined by the ELISA system. The expression and purity of the recombinant N protein were approved by SDS-PAGE and Western blotting. The sensitivity of ELISA system was 91.04 and 92.53% for the detection of IgG and IgM antibodies, respectively. Moreover, the specificity of the developed ELISA system for IgG and IgM were 98.21 and 97.32%, respectively. Our developed ELISA system showed satisfactory sensitivity and specificity for the detection of antiSARS-CoV-2 IgM and IgG antibodies and could be used as a complementary approach for proper diagnosis of COVID-19.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.

Differences between the European Union and United States of America in Drug Regulatory Affairs Affect Global Patient Safety Standards and Public Health Awareness: The Case of Deferasirox and Other Iron Chelating Drugs

Regulatory policies on drugs have a major impact on patient safety and survival. Some pharmaceutical companies employ all possible methods to achieve maximum sales in relation to the monopoly of their patented drugs, leading sometimes to irregularities and illegal activities. Misinformation on the orphan drug deferasirox has reached the stage of criminal investigations and fines exceeding USD 100 million. Additional lawsuits of USD 3.5 billion for damages and civil fines were also filed by the FBI of the USA involving deferasirox and mycophenolic acid, which were later settled with an additional fine of USD 390 million. Furthermore, a USD 345 million fine was also settled for bribes and other illegal overseas operations including an EU country. However, no similar fines for illegal practises or regulatory control violations have been issued in the EU. Misconceptions and a lack of clear guidelines for the use of deferasirox in comparison to deferiprone and deferoxamine appear to reduce the effective treatment prospects and to increase the toxicity risks for thalassaemia and other iron loaded patients. Similar issues have been raised for the activities of other pharmaceutical companies promoting the use of new patented versus generic drugs. Treatments for different categories of patients using new patented drugs are mostly market driven with no clear safeguards or guidelines for risk/benefit assessment indications or for individualised effective and safe optimum therapies. There is a need for the establishment of an international organisation, which can monitor and assess the risk/benefit assessment and marketing of drugs in the EU and globally for the benefit of patients. The pivotal role of the regulatory drug authorities and the prescribing physicians for identifying individualised optimum therapies is essential for improving the survival and safety of millions of patients worldwide.

The sharing of research data facing the COVID-19 pandemic

During the previous Ebola and Zika outbreaks, researchers shared their data, allowing many published epidemiological studies to be produced only from open research data, to speed up investigations and control of these infections. This study aims to evaluate the dissemination of the COVID-19 research data underlying scientific publications. Analysis of COVID-19 publications from December 1, 2019, to April 30, 2020, was conducted through the PubMed Central repository to evaluate the research data available through its publication as supplementary material or deposited in repositories. The PubMed Central search generated 5,905 records, of which 804 papers included complementary research data, especially as supplementary material (77.4%). The most productive journals were The New England Journal of Medicine, The Lancet and The Lancet Infectious Diseases, the most frequent keyword was pneumonia, and the most used repositories were GitHub and GenBank. An expected growth in the number of published articles following the course of the pandemics is confirmed in this work, while the underlying research data are only 13.6%. It can be deduced that data sharing is not a common practice, even in health emergencies, such as the present one. High-impact generalist journals have accounted for a large share of global publishing. The topics most often covered are related to epidemiological and public health concepts, genetics, virology and respiratory diseases, such as pneumonia. However, it is essential to interpret these data with caution following the evolution of publications and their funding in the coming months.

Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities

Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our 'vademecum' for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis

Objective

The purpose of this study is to guide researchers in the COVID-19 pandemic by evaluating the 100 most cited articles of COVID-19 in terms of bibliometric analysis, Altmetric scores, and dimension badges.

Methods

“COVID-19” was entered as the search term in Thomson Reuter’s Web of Science database. The 100 most cited articles (T100) were analyzed bibliometrically. Altmetric attention scores (AASs) and dimension badge scores of the articles were evaluated.

Results

T100 articles were published from January to September 2020. The average citation of the top 100 articles on COVID-19 was 320 ± 344.3 (143–2676). The language of all articles was English. The average Altmetric value of T100 is 3246 ± 3795 (85–16,548) and the mean dimension badge value was 670 ± 541.6 (176–4232). Epidemiological features (n = 22) and treatment (n = 21) were at the top of the main topics of T100 articles.

Conclusion

The more citations an article is made, the more it indicates the contribution of that article to science. However, the number of citations is not always the only indicator of article quality. The existence of methods that measure the impact of the article outside the academia to measure the value of the article arises more in an issue that affects the whole world, such as the COVID-19 pandemic.