Assessment of multidrug resistance on cell coculture patterns using scanning electrochemical microscopy
Bibliographic record
Abstract
The emergence of resistance to multiple unrelated chemotherapeutic drugs impedes the treatment of several cancers. Although the involvement of ATP-binding cassette transporters has long been known, there is no in situ method capable of tracking this transporter-related resistance at the single-cell level without interfering with the cell's environment or metabolism. Here, we demonstrate that scanning electrochemical microscopy (SECM) can quantitatively and noninvasively track multidrug resistance-related protein 1-dependent multidrug resistance in patterned adenocarcinoma cervical cancer cells. Nonresistant human cancer cells and their multidrug resistant variants are arranged in a side-by-side format using a stencil-based patterning scheme, allowing for precise positioning of target cells underneath the SECM sensor. SECM measurements of the patterned cells, performed with ferrocenemethanol and [Ru(NH3)6](3+) serving as electrochemical indicators, are used to establish a kinetic "map" of constant-height SECM scans, free of topography contributions. The concept underlying the work described herein may help evaluate the effectiveness of treatment administration strategies targeting reduced drug efflux.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".