ReviewSLAS discovery : advancing life sciences R & D2020
Based on Principles and Insights of COVID-19 Epidemiology, Genome Sequencing, and Pathogenesis: Retrospective Analysis of Sinigrin and Prolixin
Review in SLAS discovery : advancing life sciences R & D, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quality assessment tools used in systematic reviews of in vitro studies: A systematic review.BMC medical research methodology · 2021Pooled it
- Molecular docking as a tool for the discovery of novel insight about the role of acid sphingomyelinase inhibitors in SARS- CoV-2 infectivity.BMC public health · 2024Review
- Drug repositioning by merging active subnetworks validated in cancer and COVID-19.Scientific reports · 2021Article
- Update on Functional Inhibitors of Acid Sphingomyelinase (FIASMAs) in SARS-CoV-2 Infection.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Population-Based COVID-19 Screening in Mexico: Assessment of Symptoms and Their Weighting in Predicting SARS-CoV-2 Infection.Medicina (Kaunas, Lithuania) · 2021Article
- Drug Discovery Targeting COVID-19.SLAS discovery : advancing life sciences R & D · 2020Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative pathogen of pandemic coronavirus disease 2019 (COVID-19). So far, no approved therapy has been developed to halt the spread of the pathogen, and unfortunately, the strategies for developing a new therapy will require a long time and very extensive resources. Therefore, drug repurposing has emerged as an ideal strategy toward a smart, versatile, quick way to confine the lethal disease. In this endeavor, natural products have been an untapped source for new drugs. This review represents the confederated experience of multidisciplinary researchers of 99 articles using several databases: Google Scholar, Science Direct, MEDLINE, Web of Science, Scopus, and PubMed. To establish the hypothesis, a Bayesian perspective of a systematic review was used to outline evidence synthesis. Our docking documentation of 69 compounds and future research agenda assumptions were directed toward finding an effective and economic anti-COVID-19 treatment from natural products. Glucosinolate, flavones, and sulfated nitrogenous compounds demonstrate direct anti-SARS-CoV-2 activity through inhibition protease enzymes and may be considered potential candidates against coronavirus. These findings could be a starting point to initiate an integrative study that may encompass interested scientists and research institutes to test the hypothesis in vitro, in vivo, and in clinics after satisfying all ethical requirements.
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.