Evidence map›Paper›PMID 36950535›Full record

ReviewFrontiers in veterinary science2023

Cell membrane-coated nanoparticles: An emerging antibacterial platform for pathogens of food animals.

Sidra Altaf, Khalid Ali Alkheraije

Full text readReview
In one paragraph

Review in Frontiers in veterinary science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Sidra AltafDepartment of Pharmacy, University of Agriculture, Faisalabad, Pakistan.
Khalid Ali AlkheraijeDepartment of Veterinary Medicine, College of Agriculture and Veterinary Medicine, Qassim University, Buraidah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacterial pathogens of animals impact food production and human health globally. Food animals act as the major host reservoirs for pathogenic bacteria and thus are highly prone to suffer from several endemic infections such as pneumonia, sepsis, mastitis, and diarrhea, imposing a major health and economical loss. Moreover, the consumption of food products of infected animals is the main route by which human beings are exposed to zoonotic bacteria. Thus, there is excessive and undue administration of antibiotics to fight these virulent causative agents of food-borne illness, leading to emergence of resistant strains. Thus, highprevalence antibiotic-resistant resistant food-borne bacterial infections motivated the researchers to discover new alternative therapeutic strategies to eradicate resistant bacterial strains. One of the successful therapeutic approach for the treatment of animal infections, is the application of cell membrane-coated nanoparticles. Cell membranes of several different types of cells including platelets, red blood cells, neutrophils, cancer cells, and bacteria are being wrapped over the nanoparticles to prepare biocompatible nanoformulations. This diversity of cell membrane selection and together with the possibility of combining with an extensive range of nanoparticles, has opened a new opportunistic window for the development of more potentially effective, safe, and immune evading nanoformulations, as compared to conventionally used bare nanoparticle. This article will elaborately discuss the discovery and development of novel bioinspired cell membrane-coated nanoformulations against several pathogenic bacteria of food animals such as

Indexed as

animalsantibioticbacteriacellfoodinfectionnanoparticles

Identifiers

PMID36950535
PMCPMC10025400

What OpenQuestion holds

Textfull text, public
LicenceCC BY
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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.