Electrochemical Detection of Tobramycin Resistance in Pseudomonas Aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa are Gram-negative anaerobic bacteria and is listedby the World Health Organization as one of the critical antibiotic-resistant pathogens1. The treatment for P. aeruginosa infection can be challenging, since this pathogen often exhibits an enhanced resistance to antibiotics, including aminoglycosides, such as Tobramycin (TOB). These are called mucoid strains and are mainly found in the lungs of cystic fibrosis patients, where it is a common cause of death for these individuals2. The overall goal of the presented work is the development of an electrochemical method capable of detecting TOB uptake in P. aeruginosa. Drug susceptible and drug resistant strains of P. aeruginosa were exposed to a TOB solution for different time intervals (Figure 1). By removing organisms from the solution after incubation, the remaining TOB in solution can be determined by electrochemistry. Resistant bacteria strains showed significantly lower TOB uptake than susceptible organisms, because cellular drug resistant mechanisms enable them to inhibit TOB absorption. The presented drug uptake studies demonstrate the sensitivity of electrochemistry to detect drug resistance in living bacteria. References Singh, M., Yau, Y. C. W., Wang, S., Waters, V. & Kumar, A. MexXY efflux pump overexpression and aminoglycoside resistance in cystic fibrosis isolates of pseudomonas aeruginosa from chronic infections. Canadian Journal of Microbiology 63, 929–938 (2017). Germoni, L. A. P., Bremer, P. J. & Lamont, I. L. The effect of alginate lyase on the gentamicin resistance of Pseudomonas aeruginosa in mucoid biofilms. Journal of Applied Microbiology 121, 126–135 (2016). 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".