Evidence map›Paper›PMID 38629357›Full record

ArticleCurrent pharmaceutical design2024

Lipase and Protease Production Ability of Multi-drug Resistant Bacteria Worsens the Outcomes of Wound Infections.

Attaur Rahman, Saiqa Sardar, Zeeshan Niaz, Asif Khan, Sheheryar Sheheryar, Abdulwahed Fahad Alrefaei, Muhammad Hamayun, Sajid Ali

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Article in Current pharmaceutical design, 2024. 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
0.5field-weighted citation impact, top 33% of its field
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, 1 citations in OpenAlex.

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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 at 7 institutions in 4 countries.

Attaur RahmanLaboratório de Hanseníase, Department of Parasitology, Institute Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brazil.
Saiqa SardarMalaria Research Laboratory, Departament of Parasitology, Institute Oswaldo Cruz-Fiocruz, Rio de Janeiro, RJ, Brazil.
Zeeshan NiazDepartment of Microbiology, Hazara University, Mansehra, Pakistan.
Asif KhanLaboratory of Phytochemistry, Department of Botany, University of São Paulo, São Paulo, Brazil.
Sheheryar SheheryarDepartment of Biochemistry and Molecular Biology, Federal University of Ceara, Fortaleza, Brazil.
Abdulwahed Fahad AlrefaeiDepartment of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia.
Muhammad HamayunDepartment of Botany, Abdul Wali Khan University Mardan, Mardan, Pakistan.
Sajid AliDepartment of Horticulture and Life Science, Yeungnam University, Gyeongsan, South Korea.
Fundação Oswaldo Cruz · BRAbdul Wali Khan University Mardan · PKHazara University · PKKing Saud University · SAUniversidade de São Paulo · BRUniversidade Federal do Ceará · BRYeungnam University · KR

Funding

Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia RSP2024R218
6 · The paper itself

Abstract

backgroundSurgical site infections are one of the major clinical problems in surgical departments that cost hundreds of millions of dollars to healthcare systems around the world.

aimThe study aimed to address the pressing issue of surgical site infections, which pose significant clinical and financial burdens on healthcare systems globally. Recognizing the substantial costs incurred due to these infections, the research has focused on understanding the role of lipase and protease production by multi-drug resistant bacteria isolated from surgical wounds in the development of post-surgical wound infections.

methodsFor these purposes, 153 pus specimens were collected from patients with severe post-surgical wound infections having prolonged hospital stays. The specimens were inoculated on appropriate culture media. Gram staining and biochemical tests were used for the identification of bacterial growth on suitable culture media after 24 hours of incubation. The isolated pathogens were then applied for lipase and protease, key enzymes that could contribute to wound development, on tributyrin and skimmed milk agar, respectively. Following the CSLI guidelines, the Kirby-Bauer disc diffusion method was used to assess antibiotic susceptibility patterns. The results revealed that a significant proportion of the samples (127 out of 153) showed bacterial growth of Gram-negative (n = 66) and Gram-positive (n = 61) bacteria. In total, isolated 37 subjects were declared MDR due to their resistance to three or more than three antimicrobial agents. The most prevalent bacteria were

resultsThe antimicrobial resistance was evaluated among enzyme producers and non-producers and it was found that the lipase and protease-producing bacteria revealed higher resistance to selected antibiotics than non-producers. Notably, fosfomycin and carbapenem were identified as effective antibiotics against the isolated bacterial strains. However, gram-positive bacteria displayed high resistance to lincomycin and clindamycin, while gram-negative bacteria were more resistant to cefuroxime and gentamicin.

conclusionIn conclusion, the findings suggest that lipases and proteases produced by bacteria could contribute to drug resistance and act as virulence factors in the development of surgical site infections. Understanding the role of these enzymes may inform strategies for preventing and managing post-surgical wound infections more effectively.

Indexed as

Anti-Bacterial AgentsDrug Resistance, Multiple, BacterialLipaseMicrobial Sensitivity TestsPeptide HydrolasesAdultFemaleGram-Negative BacteriaGram-Positive BacteriaHumansMaleMiddle AgedSurgical Wound InfectionWound InfectionAnti-Bacterial AgentsLipasePeptide Hydrolasesantibioticbacteria.drug resistanceLipaseproteasesurgical site infections

Identifiers

PMID38629357
OpenAlexW4394873812

What Socratic holds

Textmetadata
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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 Socratic graph.