Effects of media composition and light exposure on the electrochemical current response during scanning electrochemical microscopy live cell imaging
Bibliographic record
Abstract
Scanning Electrochemical Microscopy (SECM) has been used as a non-invasive electrochemical technique for studying cellular processes. SECM enables the quantification of cellular metabolites in real-time providing a deeper understanding of cellular responses to external stimuli. SECM imaging of living cells requires maintaining an ideal physiological environment to ensure reliable data collection on cellular reactivity. The cellular response can be directly influenced by physicochemical parameters including cell media composition, temperature and light exposure. This research demonstrates the effect of media composition on the electrochemical current signal of adenocarcinoma cervical cancer (HeLa) cells during SECM measurements using ferrocenemethanol as a redox mediator. Investigated media that are commonly used as electrolyte, are phosphate buffered saline (PBS), and Dulbecco's modified Eagle's medium (DMEM) in the absence and presence of fetal bovine serum (FBS). In addition, this research demonstrates that fluctuating light illumination impacts the stability of the cellular electrochemical current response. Our findings reveal that media composition and illumination are important parameters that must be carefully considered and monitored during SECM live cell imaging.
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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 | 0.001 |
| 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 | 0.001 | 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".