Evidence map›Paper›PMID 35799590›Full record

ArticleFrontiers in nutrition2022

Hyper-Immune Bovine Milk as an Immunological and Nutritional Supplement for COVID-19.

Hassan Nili, Majid Bouzari, Hamid Reza Attaran, Nader Ghalegolab, Mohammad Rabani, Ahmad Mahmoudian

Open access · goldAbstract read
In one paragraph

Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
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

6 authors at 3 institutions in 1 country.

Hassan NiliVirology Research Center, Faculty of Biological Sciences and Technology, University of Isfahan, Isfahan, Iran.
Majid BouzariVirology Research Center, Faculty of Biological Sciences and Technology, University of Isfahan, Isfahan, Iran.
Hamid Reza AttaranVirology Research Center, Faculty of Biological Sciences and Technology, University of Isfahan, Isfahan, Iran.
Nader GhalegolabRazi Serum and Vaccine Research Institute, Agricultural Research, Education and Extension (AREEO), Shiraz, Iran.
Mohammad RabaniDepartment of Cellular and Molecular Biology and Microbiology, Faculty of Biological Sciences and Technology, University of Isfahan, Isfahan, Iran.
Ahmad MahmoudianDepartment of Community Medicine, School of Medicine, Isfahan University of Medical Science, Isfahan, Iran.
University of Isfahan · IRAgricultural Research & Education Organization · IRIsfahan Fertility and Infertility Center · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many different strategies have been used to fight against the Coronavirus disease (COVID-19) pandemic as a therapeutics or prophylaxis approaches. However, not enough attention has been paid to general and specific immune factors and nutritional components found in hyper-immunized dairy products. Hyper-immune bovine colostrum (HBC) has been used against many different respiratory and gastrointestinal tracts infections during past decades. An isolated dairy farm was established, and nine mixed Holstein X Simmental dairy cattle in their 6-7 months of gestation period were chosen for hyper-immunization with inactivated Severe acute respiratory syndrome corona virus-2 (SARS-CoV-2). For this, six cows were inoculated with 2 ml of 10 Clinical Trial Registration: [https://www.irct.ir/trial/51259].

Indexed as

antibodiesbovine milkCOVID-19hyper-immuneneutralizingnutritionalsupplement

Identifiers

PMID35799590
PMCPMC9254720
OpenAlexW4283203958

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.