Evidence map›Paper›PMID 39688521›Full record

ArticleVeterinary medicine and science2025

Antibiotic Susceptibility of Zoonotic Bacteria Isolated From Oral Cavities of Indigenous Dogs From Semi-Urban Areas in Uganda.

Paul Mukasa, Patrick Engeu Ogwang, Richard Oriko Owor, Julius B Lejju, Hannington Gumisiriza, Ibrahim Ntulume, Christopher Adaku

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 1 paper.

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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

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2 · The registry

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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

7 authors.

Paul MukasaDepartment of Chemistry, Mbarara University of Science and Technology, Mbarara, Uganda.ORCID 0000-0002-4423-5667
Patrick Engeu OgwangDepartment of Pharmacy, Mbarara University of Science and Technology, Mbarara, Uganda.
Richard Oriko OworDepartment of Chemistry, Busitema University, Tororo, Uganda.
Julius B LejjuDepartment of Biology, Mbarara University of Science and Technology, Mbarara, Uganda.
Hannington GumisirizaDepartment of Chemistry, Mbarara University of Science and Technology, Mbarara, Uganda.
Ibrahim NtulumeSchool of Biosecurity, Biotechnology and Laboratory Sciences, Makerere University, Kampala, Uganda.
Christopher AdakuDepartment of Chemistry, Mbarara University of Science and Technology, Mbarara, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dog bites are a source of zoonotic infections to humans, such as pasteurellosis and meningitis. Zoonotic bacterial identification and their antibiotic susceptibility assessment are key towards the successful management of such infections. This study isolated and identified zoonotic bacterial species from the oral cavities of indigenous dogs and also determined their antibiotic susceptibility profile. Oral swab samples collected from 54 indigenous dogs (domestic [36] and stray [18]) were cultured on agar media, and then on selective-differential media. The colony morphology and conventional biochemical tests were used to identify the bacterial isolates. Antibiotic susceptibility was evaluated using the Kirby-Bauer disk diffusion method. A total of 232 bacterial isolates were obtained, from which 29 bacteria species (18 Gram-negative and 11 Gram-positive) belonging to 19 genera and 13 families were identified. Aerobic and anaerobic bacteria species constituted 69% and 31%, respectively. The most prevalent bacteria species was Staphylococcus aureus (10.8%) followed by Escherichia coli (9.5%), while Yersinia enterocolitica (0.4%) was the least common. Most bacteria species belonged to family Enterobacteriaceae (11) followed by Vibrionaceae (04). All the sampled dogs had a number of multi-drug-resistant superbugs in their oral cavities. However, all the tested bacterial isolates were only susceptible to imipenem and chloramphenicol. All the identified bacteria species were resistant to metronidazole, amoxicillin-clavulanic acid and ceftazidime. Therefore, antibiotics such as imipenem and chloramphenicol could be of better choice for managing dog bite infections compared to the antibiotics recommended by Uganda Clinical Guidelines, such as metronidazole and amoxicillin-clavulanic acid.

Indexed as

Anti-Bacterial AgentsMouthAnimalsBacteriaDog DiseasesDogsDrug Resistance, BacterialMicrobial Sensitivity TestsUgandaZoonosesAnti-Bacterial Agentsdog bitesoral cavitiessusceptibilityzoonotic bacteria

Identifiers

PMID39688521
PMCPMC11651091

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LicenceCC BY-NC-ND
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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.