Effect of sub-inhibitory concentrations of cefepime on biofilm formation by <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
This study investigated the effect of cefepime at sub-minimum inhibitory concentrations (sub-MICs) on in vitro biofilm formation (BF) by clinical isolates of Pseudomonas aeruginosa. The effect of cefepime at sub-MIC levels (½– 1 / 256 MIC) on in vitro BF by six clinical isolates of P. aeruginosa was phenotypically assessed following 24 and 48 h of challenge using the tissue culture plate (TCP) assay. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to observe the change in expression of three biofilm-related genes, namely, a protease-encoding gene (lasA), fimbrial protein-encoding gene (cupA1), and alginate-encoding gene (algC), in a weak biofilm-producing strain of P. aeruginosa following 24 and 48 h of challenge with sub-MICs of cefepime. The BF morphology in response to cefepime was imaged using scanning electron microscopy (SEM). The TCP assay showed strain-, time-, and concentration-dependent changes in in vitro BF in P. aeruginosa following challenge with sub-MICs of cefepime, with a profound increase in strains with inherently no or weak biofilm-producing ability. RT-PCR revealed time-dependent upregulation in the expression of the investigated genes following challenge with ½ and ¼ MIC levels, as confirmed by SEM. Cefepime at sub-MICs could upregulate the expression of BF-related genes and enhance BF by P. aeruginosa clinical isolates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".