Rapid Detection of Wound Pathogen Proteus mirabilis Using Disposable Electrochemical Sensors
Bibliographic record
Abstract
The detection of infection in clinical practice is a time consuming and laborious process.The ability to monitor infection status in real time, for example in wounds, would enable earlier intervention and improved prognosis.This study describes the real time electrochemical detection of the clinically important pathogen Proteus mirabilis.Using impedance spectroscopy in conjunction with a normalisation approach, the growth of P. mirabilis in LB medium was detected 1 hour after sample inoculation at a cell concentration of 7.4 x10 6 CFU/mL.Furthermore, a significant decrease in charge transfer resistance arose over the 24 hour growth period (p = 0.009), modelled using a simple equivalent circuit.Additional experiments performed in 0.9% w/v NaCl (where growth was inhibited) indicated that processes facilitated by the organism's metabolism and growth dominated the impedance response in LB medium.A simulated wound fluid was used to explore a more complex environment, and similar changes to normalised impedance were observed.The ability of these low cost sensors to rapidly detect P. mirabilis highlights their potential for adoption into point-of-care infection monitoring devices.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".