Electrochemistry of Antibiotic Hybrids: Towards the Detection and Quantification of Antibiotic Resistance
Bibliographic record
Abstract
The overuse of antibiotics in human and veterinary medicine as well as in agriculture has caused antibiotic resistance to become a global problem and national and international health organizations have called for the urgent development of new treatment strategies. The increase of antibiotic resistance in Gram-negative bacteria in particular is a major cause for concern, as many Gram-negatives cause serious infections, such as pneumonia, and only few effective antibiotics have been developed due to bacterial innate defense mechanisms, including low outer membrane permeability and high number of efflux pumps. The presented study demonstrates the thorough electrochemical characterization of antibiotic hybrid drugs, currently under development at the University of Manitoba, Canada. The interaction of these antibiotic hybrids with selected biomolecules is investigated to further our understanding about in vivo processes of drug absorption, distribution, metabolism, and excretion. Furthermore, the interaction of bacteria with the newly developed investigational antibacterial therapeutics is studied towards the quantification of antibiotic resistance in pathogens to address the dire need for innovative strategies that are able to quantify efflux and influx of agents into bacterial cells for the assessment of potential new and reliable antimicrobial candidates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".