Emerging Technologies for the Electrochemical Detection of Bacteria
Bibliographic record
Abstract
Infections are a huge economic liability to the health care system, although real-time detection can allow early treatment protocols to avoid some of this cost and patient morbidity and mortality. Pseudomonas aeruginosa (PA) is a drug-resistant gram-negative bacterium found ubiquitously in clinical settings, accounting for up to 27% of hospital acquired infections. PA secretes a vast array of molecules, ranging from secondary metabolites to quorum sensing molecules, of which many can be exploited to monitor bacterial presence. In addition to electrochemical immunoassays to sense bacteria via antigen-antibody interactions, PA pertains a distinct redox-active virulence factor called pyocyanin (PYO), allowing a direct electrochemical detection of the bacteria. There has been a surge of publications relating to the electrochemical tracing of PA via a myriad of novel biosensing techniques, materials, and methodologies. In addition to indirect methods, research approaches where PYO has been sensitively detected using surface modified electrodes are reviewed and compared with conventional PA-sensing methodologies. This review aims at presenting indirect and direct electrochemical methods currently developed using various surface modified electrodes, materials, and electrochemical configurations on their electrocatalytic effects on sensing of PA and in particular PYO.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".