Electrochemical Quantification of Tobramycin Retention in <i>Pseudomonas aeruginosa</i> as Antimicrobial Susceptibility Indicator
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The emergence and spread of bacterial resistance to antibiotics has developed into one of the most challenging threats to public health. Antibiotic susceptibility tests (ASTs) for bacterial infections are now essential, because they provide guidance for physicians in the selection of antibiotics, to which bacteria will respond. Most current AST methods require long periods of time, because of bacterial growth and incubation, leading to a prolonged and overuse of broad-spectrum antibiotics. Thus, there is a growing demand for methods and technologies that enable rapid antibiotic susceptibility assessment. Due to advantages related to cost-effectiveness, rapid response time and high sensitivity, electrochemical detection methods are promising analytical tools that can successfully quantify antibiotic uptake and retention in clinically relevant bacterial strains. This study presents the electroanalytical quantification of tobramycin (TOB) retention in susceptible and resistant bacterial strains of Pseudomonas aeruginosa . The electrochemical behavior of TOB was characterized by voltammetry, identifying redox potentials, the current dependence on pH conditions, and the detection limit at unmodified glassy carbon electrodes. The presented methodology was able to distinguish between susceptible and resistant bacterial strains, and is also capable of identifying varying degrees of resistance against TOB. The presented approach detects the immediate interaction of bacteria with an antibiotic, without the need of complex and cost-intense equipment related to genomic testing methods.
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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 | 0.000 |
| Open science | 0.000 | 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".