ReviewJournal of pharmacy & bioallied sciences2021
COVID and Animal Trials: A Systematic Review.
Review in Journal of pharmacy & bioallied sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Knowledge of and Self-Expressed Stigma for COVID-19 Vaccination among Pre-University Students in a College in South India: A Cross-Sectional Study.Journal of pharmacy & bioallied sciences · 2024Article
- "Dentist as Smile Weavers" An Online Survey on Infection Surveillance, Emergency Treatment, Economic Setbacks, and Stress Endured by Periodontists of South India during COVID-19.Journal of pharmacy & bioallied sciences · 2022Article
- Debate on mandatory COVID-19 vaccination.Ethics, medicine, and public health · 2022Article
- The pigtail macaque (Macaca nemestrina) model of COVID-19 reproduces diverse clinical outcomes and reveals new and complex signatures of disease.PLoS pathogens · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extensive work is being done to form targeted drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection; however, it is imperative to have a safe and effective vaccine against the same to win the war against this pandemic. For creating an efficacious vaccine, a proper animal model needs to be selected which can have an acceptable similarity of response as well as effects when administered to humans. For the present research, extensive search was conducted in MEDLINE and bioRxiv and medRxiv servers which were published in the English language from January 1, 2020, to August 20, 2020. Search terms included animal models, SARS-CoV-2, COVID-19, immune response against coronavirus, nonhuman primates, mice, ferrets, and macaques. In our study, creating an adequate immune response mimicking the response as in humans, as the endpoint, was considered as inclusion criterion while assessment of any additional therapies like safety as well as minimal tolerable dose using animal models as well as formation of adequate sample size of these models against COVID-19 was not considered. In our search, 163 articles were shortlisted, of them only 20 articles were finally included in our study which addressed to our inclusion and exclusion criterion. Our research articles focused on nonhuman primates, mice, hamsters, ferrets, cats, and dogs, with the main goal to investigate the role of animal models in the pathogenesis of COVID-19. It was evident in our research that animal models only mimic limited signs and symptoms experienced in COVID infection as compared to infections in humans. However, they are still essential to understand the pathogenesis, transmissibility of viral particles, and vaccine testing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.