Electrochemical Detection of Drug Uptake and Retention in Bacteria and Cancer
Bibliographic record
Abstract
Drug resistance in bacteria and cancer is a growing problem that severely increases the number of deaths worldwide.[1,2] According to the World Health Organization, drug resistance is present in every country, and various national and international health organizations have called for the urgent development of new treatment and diagnostic strategies.[3] This presentation outlines innovative and interdisciplinary approaches to recognize and quantify drug resistance in bacteria and cancer cells by electrochemistry. Electroanalytical techniques are cost efficient, sensitive and the transparency of a liquid sample is irrelevant, allowing direct in vitro analysis of blood, urine, and saliva samples. This presentation covers the characterization of some of the most important commercial drugs, and new investigational antibiotic hybrids by electrochemistry.[4] Building on this exploration of drug electrochemistry, the quantification of drug uptake and retention in biological cells is presented. This research forms the basis for the development of a point-of-care biosensors to identify drug resistance in patient samples. This technology will advance clinical treatment from the current trial-and-error approach to the prescription of evidence-based personalized drug regimens. [1] J. Sun, A. R. Warden, J. Huang, W. Wang, and X. Ding, Anal. Chem., 91, 7524–7530 (2019). [2] R. Article, J. Pathol., 205, 275–292 (2005). [3] World Health Organization (WHO). Global action plan on antimicrobial resistance; (2015). [4] R. Islam, H.T. Le Luu, S. Kuss, J. Electrochem. Soc., 167, 045501 (2020).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".