Evidence map›Paper›PMID 42577753›Full record

ArticleVeterinary medicine international2026

The Role of a Biological Membrane in Healing Full-Thickness Cutaneous Wounds in Rats.

Ali Ghazi Atiyah, Thamer Jaddoa Shihab, Ismael I Hasan

Abstract read
In one paragraph

Article in Veterinary medicine international, 2026. 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

3 authors.

Ali Ghazi AtiyahDepartment of Internal Medicine, Surgery and Obstetrics, College of Veterinary Medicine, Tikrit University, Tikrit, Iraq, tu.edu.iq.ORCID https://orcid.org/0000-0001-7493-0057
Thamer Jaddoa ShihabDepartment of Pathology and Poultry Diseases, College of Veterinary Medicine, Tikrit University, Tikrit, Iraq, tu.edu.iq.ORCID https://orcid.org/0000-0002-7285-0157
Ismael I HasanDepartment of Pathology and Poultry Diseases, College of Veterinary Medicine, Tikrit University, Tikrit, Iraq, tu.edu.iq.ORCID https://orcid.org/0000-0001-9584-6444

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Full-thickness wounds pose significant healing challenges due to their reduced regenerative capacity and prolonged inflammation. Objectives: This study aimed to evaluate the healing potential of decellularized amniotic membrane (AM) as a biological dressing for the treatment of full-thickness cutaneous wounds. Methods: In this study, 18 healthy rats were evenly distributed between the control and treatment groups. A 3 × 3 cm full-thickness wound was made on the dorsal side of the back. The treated group received decellularized ovine amniotic membrane (OAM). This membrane was then applied to the affected area. The control group, however, did not get any treatment at all. The animals were euthanized at 7, 14, and 21 days after the operation so the histological analysis could be conducted. Result: Scanning electron microscopy images showed that the decellularized AM had a uniformly smooth surface, whereas the undecellularized AM appeared to contain organic and cellular material, resulting in a rougher surface. Histological and macroscopic examinations revealed that the treated group healed wounds more quickly than the control group. Conclusions: The biological membrane can induce tissue re-epithelialization, stimulate collagen synthesis, reduce inflammatory reactions, and prevent immunological rejection, making it an attractive, biocompatible scaffold and a promising option in regenerative medicine.

Indexed as

amniotic membranebiological membranecutaneous wounddecellularizedscaffold

Identifiers

PMID42577753
PMCPMC13455527

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Registered trials

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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.