Quantifying Single Human Cancer Cell Redox State By Scanning Electrochemical Microscopy
Bibliographic record
Abstract
Scanning electrochemical microscopy (SECM) is increasingly applied to study and image live cells. Quantitative analyses of biological systems, however, still remain challenging. In the presented study, single human adenocarcinoma cervical cancer cells are electrochemically investigated by means of SECM. The target cell’s electrochemical signal is observed over time under the influence of green tea catechins (GTC), which are suggested to offer chemopreventive and therapeutic effects on cancer.1 The electrochemical response of living target cells is measured experimentally and the cell’s heterogeneous rate constant is determined by using a numerical model, based on forced convection during high speed SECM imaging. The beneficial effect of GTC on cancer cells could be confirmed by SECM and the presented study shows an alternative approach towards unravelling the mechanisms involved during inhibition of carcinogenesis. 1Connors, SK, Chornokur, G, Kumar NB (2012) Nutrition and Cancer, 64:4. Figure 1
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".