Growth inhibition of adherent <i>Pseudomonas aeruginosa</i> by an <i>N</i>-butanoyl-<scp>l</scp>-homoserine lactone analog
Bibliographic record
Abstract
The discovery of quorum sensing (QS) communication systems regulating bacterial virulence has afforded a novel opportunity for controlling infectious bacteria by interfering with QS. Pseudomonas aeruginosa is an example of an opportunistic human pathogen for which N-acyl homoserine lactone (AHL)-related compounds have been described as potent inhibitors of biofilm formation and virulence factors, given their similarity to the natural QS autoinducers (AHLs). Our purpose was to design potent analogs of N-butanoyl-L-homoserine lactone (C4-HSL) and to screen them for biological activity. Eleven original compounds characterized by the modification of the lactone moiety were screened for their ability to impair biofilm formation. Among them, compound 11 was able to modify the growth kinetics and to restrict the number of adherent cells when added from the early stages of biofilm formation (i.e., adhesion and microcolony formation) in a dose-dependent manner. To demonstrate antagonism with C4-HSL, we showed that the inhibition of biofilm formation by compound 11 was impaired when C4-HSL was added. Structure-activity relationships are discussed with respect to the results obtained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".