Evidence map›Paper›PMID 33884222›Full record

ArticleJournal of nutrition & food sciences2021

Chemo-Preventive Effect of Vegetables and Fruits Consumption on the COVID-19 Pandemic.

Clement G Yedjou, Richard A Alo, Jinwei Liu, Juliet Enow, Pierre Ngnepiepa, Richard Long, Lekan Latinwo, Paul B Tchounwou

Open access · greenAbstract read
In one paragraph

Article in Journal of nutrition & food sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Clement G YedjouDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.
Richard A AloDepartment of Computer Science, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.
Jinwei LiuDepartment of Computer Science, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.
Juliet EnowDepartment of Health Policy and Administration. School of Public Health, Jackson State University, 350 W. Woodrow Wilson Drive, Jackson, MS 39213, USA.
Pierre NgnepiepaDepartment of Mathematics, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.
Richard LongDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.
Lekan LatinwoDepartment of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, USA.
Paul B TchounwouDepartment of Biology, College of Science, Engineering and Technology, Jackson State University, 1400 Lynch Street, Box 18750, Jackson, MS 39217, USA.
Florida Agricultural and Mechanical University · USJackson State University · US

Funding

Strengthening the Biomedical Data Science Training Program at Jackson State UniversityU54MD015929 · NIMHD · JACKSON STATE UNIVERSITY · PI PAYTON, MARINELLE · 2020 to 2024
$12.1M
NIMHD NIH HHS U54 MD015929
6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) is a new disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a global pandemic that has claimed the death of 1,536,957 human beings worldwide including 287,842 deaths in the United States as of December 3, 2020. It has become a major threat to the medical community and the entire healthcare system in every part of the world. Recently, the Food and Drug Administration (FDA) has approved the emergency use of Pfizer and Moderna COVID-19 vaccine on December 12, 2020. However, there are concern about the new COVID-19 vaccine safety, efficacy, and immunity after the vaccination. In addition, both coronavirus and COVID-19 vaccine are new at this point and there is no scientific evidence to know whether people who are vaccinated can still carry the COVID 19 pathogens and pass them along to others. Therefore, many people all over the world have an increased interest in consuming more VF for the purpose of maintaining their health and boosting their immune system. Identifying novel antiviral agents for COVID-19 is of critical importance, and VF is an excellent source for drug discovery and therapeutic development. The objective of this study is to test the hypothesis that a high intake of vegetables and/or fruits prevents COVID-19 incidence and reduces the mortality rate. To achieve this objective, we collected the diet data of COVID-19 from Kaggle (https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset), and used a machine-learning algorithm to examine the effects of different food types on COVID-19 incidences and deaths. Specifically, we used the feature selection method to identify the factors (e.g., diet-related factors) that contribute to COVID-19 morbidity and mortality. Data generated from the study demonstrated that VF intake can help to combat the SARS-CoV-2. Taken together, VF may be potential chemopreventive agents for COVID-19 due to their antiviral properties and their ability to boost the human body immune system.

Indexed as

Chemo preventive agentsCOVID-19Death rateFruitsIncidence rateVegetables

Identifiers

PMID33884222
PMCPMC8057745
OpenAlexW3153464252

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.