Electrochemical Detection of Platinum-Based Chemoresistance and Correlation with MEK1 Activity in Living Cancer Cells
Bibliographic record
Abstract
Drug resistance is responsible for most chemotherapy failures, making it an urgent issue in modern oncology. In ovarian cancer, resistance to platinum-based chemotherapeutics, such as carboplatin, is common. However, its underlying mechanisms remain poorly understood. A mitogen-activated protein kinase kinase, MEK1, may play a role in the development of this resistance mechanism. This project aims to understand this mechanism by using western blots to measure MEK1 activity in patient-obtained living ovarian cancer cells, comparing between carboplatin-susceptible and carboplatin-resistant cells. Moreover, early detection of chemoresistance can improve treatment outcomes. However, current detection methods are inefficient and time-consuming. To address this, the project also proposes an unconventional approach to identifying chemoresistance. This involves the use of scanning electrochemical microscopy for the rapid and accurate quantification of glutathione, a prominent antioxidant that may serve as a biomarker for platinum-based resistance mechanisms, in both cell lines. The findings suggest a correlation between lower MEK1 activity and increased glutathione levels in the resistant cells. This research may contribute to the development of an efficient and reliable method for detecting chemoresistance, with the potential of significantly improving cancer treatment outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".