(Invited) Electrochemical Detection of Pseudomonas Aeruginosa Quorum Sensing Molecules at Micro Liquid|Liquid Interface Via Facilitated Proton Transfer Mechanism
Bibliographic record
Abstract
The micro interface between two electrolyte solutions (micro-ITIES) has been exploited as a miniaturized electrochemical platform for detection of Pseudomonas aeruginosa. Electrochemical detection methods for point-of-care-devices in healthcare environments are ideal owing to their small equipment footprint and relative ease of use, while simultaneously, disposable electrode materials are easily fabricated. Meanwhile, P. aeruginosa is a pathogenic bacteria that poses a serious health risk to patients with compromised immune systems. Herein, a proof-of-concept electrochemical method is demonstrated that employs a micro-ITIES between water and oil (w/o) held at the tip of a glass capillary which targets two quorum sensing (QS) molecules produced throughout the bacteria’s life-cycle: 4‐hydroxy‐2‐heptylquinoline (HHQ) and 2‐heptyl‐3,4‐dihydroxyquinoline (PQS, Pseudomonas quinolone signal). QS molecules are used to signal colony growth in a pseudo-multicellular fashion; however, HHQ and PQS are also known virulence factors and are unique to this form of pathogenic bacteria. HHQ and PQS show excellent solubility in chlorinated organic solvent with limited partitioning to the aqueous phase. Moreover, as observed electrochemically, they facilitate proton transfer across the w/o interface which can be monitored/quantified and is exploited here as a means for early bacterial detection. This work demonstrates that the micro-ITIES can serve as a viable platform for P. aeruginosa identification and quantification. Density functional theory calculations were also 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. Figure 1
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".