Antimicrobial and Anti-Biofilm Effects of Essential Oils on Hypermucoviscous Klebsiella pneumoniae
Bibliographic record
Abstract
The multidrug-resistant bacterium Klebsiella pneumoniae is known to cause critical infections.Its hypermucoviscous phenotype enhances virulence.This study investigates the antimicrobial and anti-biofilm potential of essential oils against these strains.A total of 57 Klebsiella pneumoniae strains were collected from diverse clinical specimens obtained from hospitals in Baghdad.These isolates underwent identification and assessment for hypermucoviscosity characteristics through the string test.The antimicrobial properties of essential oils extracted from thyme (Thymus vulgaris), peppermint (Mentha piperita), and rosemary (Rosmarinus officinalis) were analyzed utilizing both agar diffusion and broth dilution techniques.Biofilm formation and hypermucoviscosity were assessed using the microtiter plate and silicon catheter methods.Among the 57 K. pneumoniae isolates, 9% exhibited the hypermucoviscous phenotype.Resistance was highest to ceftazidime and cefotaxime (94.42%), followed by ampicillin and cefepime (91.42%).Biofilm was formed in 65.46% of the isolates.Essential oils of thyme, peppermint, and rosemary exhibited antimicrobial activity.Notably, thyme and peppermint oils were effective in inhibiting both hypermucoviscosity and biofilm formation.Thyme oil exhibited the highest biofilm inhibition (46.64%), followed by peppermint oil (39.68%).Both oils reduced bacterial adhesion on catheters.The findings of the present study suggest essential oils (Eos), especially those obtained from thyme, peppermint, and rosemary, as potential alternatives for combating hypermucoviscous K. pneumoniae infections, antibiotic resistance, and biofilm-related issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".