Antibiotic Susceptibility Testing at a Screen-Printed Carbon Electrode Array
Bibliographic record
Abstract
Screen-printed carbon electrode arrays were treated to allow respiratory activity-based measurement of antibiotic susceptibility with Escherichia coli JM105. Carbon working electrodes were examined for antibiotic adsorption and were pretreated with various electrochemical and chemical protocols to minimize antibiotic adsorption. Treatment by voltammetry in basic solution or by chemical modification with poly-L-lysine or chitosan were found to be effective methods for the elimination of adsorption of the studied group of 17 antibiotics, which comprised several classes and modes of action. Measurements consisted of two-electrode amperometry of the bacterial suspension after 10 min of incubation with antibiotic followed by addition of an oxidative cocktail of ferricyanide and dichlorophenolindophenol for a further 10 min; response currents, which indicate the extent of reduction of ferricyanide to ferrocyanide by cellular respiratory activity, decrease with increasing concentration of antibiotic present in the initial 10 min incubation. IC50 values obtained for chloramphenicol with these electrode modification methods are consistent at 2.0 +/- 0.2 mM, in approximate agreement with previously reported respiration-based results for this organism but significantly higher than values reported for growth-based antibiotic susceptibility 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".