Electrochemical Detection of Drug Efflux from Living Cells - Towards the Quantification of Drug Resistance in Bacteria and Cancer
Bibliographic record
Abstract
The rapid spread of drug resistance in bacteria as well as cancer has developed into a significant threat to the global public health. According to the World Health Organization (WHO), antibiotic resistance is present in every country, and various national and international health organizations, including the United Nations and the Infectious Diseases Society of America have called for the urgent development of new treatment and diagnostic strategies. The Centers for Disease Control and Prevention reports approximately 10 million deaths worldwide each year in connection with antibiotic resistance. Similarly, drug resistance in cancer is believed to be responsible for treatment failure in up to 90% of metastatic cancer patients. Cellular resistance mechanisms in both bacteria and cancer include cell membrane protein modifications, intracellular drug target alterations, and the over expression of efflux pumps. The latter are the result of an over-expression of efflux pump proteins, which enable cells to expel drugs rapidly from the cell interior, before these compounds can take effective action. Drug compounds and efflux pump proteins have recently caught the attention by the electrochemical community to develop new methodologies to understand and detect drug resistance in both bacteria and cancer by electrochemistry. Our research efforts focus specifically on drug compounds expelled from living biological cells by electrochemistry and their detection by standard electrochemical techniques, such as volatmmetry, as well as specialized instrumentation, such as scanning electrochemical microscopy (SECM). This presentation covers the characterization of common drug compounds, but also newly investigational antibiotic hybrids. Recent advances are presented towards the quantification of drug resistance in epithelial ovarian cancer (EOC), which has the highest mortality rate among gynecological cancers.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".