Evidence map›Paper›PMID 40329022›Full record

ArticleVeterinary medicine and science2025

Antibacterial Activity of Traditional Medicinal Plants: Combating Antibiotics Resistance in Animal Wound Infections.

Dan Jia, Safia Arbab, Hanif Ullah, Khalid J Alzahrani, Fuad M Alzahrani, Khalaf F Alsharif, Jiyu Zhang, Ka Li

Abstract read
In one paragraph

Article in Veterinary medicine and science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Dan JiaOutpatient Department, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Safia ArbabLanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.ORCID 0000-0002-9940-9708
Hanif UllahMedicine and Engineering Interdisciplinary Research Laboratory of Nursing and Materials/Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, China.ORCID 0000-0003-1052-8522
Khalid J AlzahraniDepartment of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.
Fuad M AlzahraniDepartment of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.
Khalaf F AlsharifDepartment of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.
Jiyu ZhangLanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou, China.
Ka LiMedicine and Engineering Interdisciplinary Research Laboratory of Nursing and Materials/Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, China.

Funding

National Natural Science Foundation of ChinaSichuan Province Science popularization Training Project 2025JDKP0057
6 · The paper itself

Abstract

The rise of antibiotic resistance poses a significant challenge to veterinary medicine, particularly in the treatment of wound infections in animals. This study aimed to evaluate the antibacterial properties of traditional medicinal plants against four bacterial strains isolated from septic animal wound infections and to compare their effectiveness with broad-spectrum antibiotics. A total of 150 bacterial isolates were collected using sterile cotton swabs, cultured on nutrient and mannitol salt agar for growth and identified through catalase and coagulase tests. The study assessed antibiotic resistance and tested the bacterial isolates' susceptibility to medicinal plants using the disc diffusion method. Among the isolates, Staphylococcus aureus was the most common pathogen, accounting for 26.6% (40 isolates), followed by Escherichia coli at 26% (39 isolates). Streptococcus spp. and Pseudomonas spp. were found in 20% (30 isolates) and 16% (25 isolates), respectively. Antibiogram analysis revealed notable antibiotic resistance, with S. aureus showing the highest resistance to Ciprofloxacin (60.5%) and Rifampicin (57.5%). E. coli exhibited 61.5% resistance to Ciprofloxacin and 56.4% to streptomycin. Streptococcus spp. had the highest resistance to Ciprofloxacin (53.3%), whereas Pseudomonas spp. showed the greatest resistance to Chloramphenicol (52%). Ethanol extracts of the medicinal plants, prepared using various solvents, were utilized for testing. Ethanol extracts of Loranthus acaciae and Cymbopogon proximus at concentrations of 60-90 µL demonstrated the largest inhibition zones, ranging from 55.5 ± 3.85 to 57.5 ± 2.5 mm against E. coli and S. aureus. Inhibition zones for Streptococcus spp. and Pseudomonas spp. ranged from 50 ± 2 to 48.3 ± 1.7 mm. In comparison, the standard antibiotics exhibited inhibition zones of 58.95 ± 2.55-60 ± 2.5 mm against S. aureus and E. coli, with slightly lower zones (51.65 ± 1.6-49 ± 1 mm) observed for Streptococcus spp. and Pseudomonas spp. These findings underscore the significant antibacterial activity of medicinal plants against multidrug-resistant pathogens in animal wound infections, highlighting their potential to accelerate healing, reduce infection severity and provide a cost-effective alternative to combat antibiotic resistance.

Indexed as

Anti-Bacterial AgentsBacteriaDrug Resistance, BacterialPlant ExtractsPlants, MedicinalWound InfectionAnimalsMicrobial Sensitivity TestsAnti-Bacterial AgentsPlant Extractsanimal woundantibioticsantimicrobialherbal medicinal plants

Identifiers

PMID40329022
PMCPMC12055452

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LicenceCC BY
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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 OpenQuestion graph.