Analysing single live cells by scanning electrochemical microscopy
Bibliographic record
Abstract
Single live cell analysis methods provide information on the characteristics of individual cells, yielding not only bulk population averages but also their heterogeneity. Scanning electrochemical microscopy (SECM) offers single live cell activities along its topography with high accuracy probe tip positioning. Both intracellular and extracellular processes can be electrochemically examined through the use of SECM. This non-invasive technique allows for high resolution mapping of electrochemical measurements in or around the cell sample of interest. Reactive oxygen species and reactive nitrogen species can be determined in a non-invasive label-free method and utilized as a probe for cellular pathology and physiology. Membrane permeability and rate of membrane species transport can be quantified in SECM. The cell response to external stressors can be monitored and modelled. SECM is able to offer nanoscale mapping and low concentration detection, providing a powerful bioanalytical tool for live cell studies. Herein we present an overview of recent progress in the imaging and characterization of single live cells using SECM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".