Evidence map›Paper›PMID 40849541›Full record

ArticleScientific reports2025

Potential application of Healitide-GP1, a novel antibacterial peptide, in wound healing: in vitro studies.

Hadi Zare-Zardini, Sima Sadat Seyedjavadi

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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. 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

2 authors.

Hadi Zare-ZardiniDepartment of Biomedical Engineering, Meybod University, Meybod, Iran. hzare@meybod.ac.ir.
Sima Sadat SeyedjavadiDepartment of Mycology, Pasteur Institute of Iran, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wound healing is a complex process that can be compromised by bacterial infections, leading to delayed healing and an increased risk of complications. The aim of this study was to design and develop a novel antibacterial peptide, Healitide-GP1, which could be used in the treatment of infected wounds. A machine learning-based approach was used to identify the key features of WHPs and a genetic algorithm was used to generate new peptide sequences. Healitide-GP1 was synthesized and functionally validated. It showed high cytocompatibility (> 200 µg/mL in human dermal fibroblasts (HDF) and human keratinocytes (HaCaT)), improved wound closure (48% and 52% after 24 h, respectively) and strong antibacterial activity against Staphylococcus aureus (MIC: 12.5 µg/mL) and Escherichia coli (MIC: 25 µg/mL). Bioinformatics analyses revealed the unique hydrophobic motif and distinct evolutionary positioning of Healitide-GP1, suggesting a novel mechanism of action. These results emphasize the potential of Healitide-GP1 as a promising therapeutic candidate for the treatment of infected wounds. Our study demonstrates the successful integration of machine learning, bioinformatics and experimental validation in the development of therapeutic peptides and provides a valuable framework for the discovery of novel treatments for wound infections.

Indexed as

Anti-Bacterial AgentsAntimicrobial PeptidesWound HealingEscherichia coliFibroblastsHumansKeratinocytesMachine LearningMicrobial Sensitivity TestsStaphylococcus aureusAnti-Bacterial AgentsAntimicrobial PeptidesAntibacterial activityCytocompatibilityGenetic algorithmMachine learningWound healing peptide

Identifiers

PMID40849541
PMCPMC12374991

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.