SECM stack imaging and full 3D modelling of asymmetric clusters of live cells
Bibliographic record
Abstract
• Clusters of live T24 cells were analysed by scanning electrochemical microscopy. • Tailored 3D finite element analysis simulations were conducted for the first time. • A 2D depth scan image is composed of hundreds of probe approach curves. • Membrane topography and effects of an adjacent cell can be determined at any of live cell spots. Scanning Electrochemical Microscopy (SECM) has shown great strength as a bioanalytical technique for the characterization of single live cell topography, membrane permeability and extracellular reactive oxygen species. However, care must be taken to avoid the presence of adjacent cells in close proximity. Herein, we describe how these clusters of two or more cells may contribute to a combined signal. SECM is commonly coupled with simulated theoretical probe approach curves, allowing surface geometry or electrochemical reactivity to be quantified. Our novel experimental and simulation methodologies including tailored 3D SECM imaging of the live cells are reported here. These 3D modelling techniques allow the generation of 3D x-y-z cell profiles, cell surface maps at an electrode-cell separation, depth scan maps, probe approach curves to any cell spots of interest, and surface topography. The experimental quantification of cell height and topography was performed on cell clusters with an impermeable hydrophilic redox agent, ferrocenecarboxylate, for the deconvolution of these adjacent cell signals. As a proof of concept, experimental and theoretical results were compared to established model outputs. The characterization limits of commonly employed electrode sizes were assessed. Higher complexity cell geometries were explored for the first time, leading to the characterization of these cell clusters with any electrode size. The above developments are versatile, and further demonstrate the strength of SECM as a bioanalytical technique for monitoring cellular homeostasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".