Detection of<i>Pseudomonas aeruginosa</i>quorum sensing molecules at an electrified liquid|liquid micro-interface through facilitated proton transfer
Bibliographic record
Abstract
Miniaturization of electrochemical detection methods for point-of-care-devices is ideal for their integration and use within healthcare environments. Simultaneously, the prolific pathogenic bacteria Pseudomonas aeruginosa poses a serious health risk to patients with compromised immune systems. Recognizing these two factors, a proof-of-concept electrochemical method employing a micro-interface between water and oil (w/o) held at the tip of a pulled borosilicate glass capillary is presented. This method targets small molecules produced by P. aeruginosa colonies as signalling factors that control colony growth in a pseudo-multicellular process known as quorum sensing (QS). The QS molecules of interest are 4-hydroxy-2-heptylquinoline (HHQ) and 2-heptyl-3,4-dihydroxyquinoline (PQS, Pseudomonas quinolone signal). Hydrophobic HHQ and PQS molecules, dissolved in the oil phase, were observed electrochemically to facilitate proton transfer across the w/o interface. This interfacial complexation can be exploited as a facile electrochemical detection method for P. aeruginosa and is advantageous as it does not depend on the redox activity of HHQ/PQS. Interestingly, the limit-of-linearity is reached as [H+] ≈ [ligand]. Density functional theory calculations were performed to determine the proton affinities and gas-phase basicities of HHQ/PQS, as well as elucidate the likely site of stepwise protonation within each molecule.
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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 | 0.001 | 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".