Modeling of morphologically realistic large-scale multicellular electroporation for population-level analysis
Bibliographic record
Abstract
This study explores the dynamics of multicellular electroporation within a monolayer cluster of HCMEC/D3 cells subjected to pulsed electric fields. By proposing an automated approach capable of reconstructing realistic multicellular morphology, we accurately simulate the temporal-spatial distribution of electroporation characteristics, including pore density, pore radius, pore area ratio (PAR), and transmembrane potential (TMP) across the cell and nuclear membranes in large-scale multicellular structures comprising up to ∼400 cells. Our results reveal significant heterogeneity in the electroporation response among cells, primarily driven by variations in cell morphology and orientation relative to the applied electric field. High pore density and PAR are predominantly localized in the polar regions of cells align with the field direction, while regions perpendicular to the electric field exhibit minimal electroporation. Importantly, the results suggests that cell death induced by electroporation is not necessarily contingent on the entire cell membrane permeabilization; rather, it is closely related to the fraction of the membrane affected and the duration of the electroporated state. The findings from this study provide valuable insights into the biophysical mechanisms of electroporation, offering potential pathways for enhancing the precision and effectiveness of electroporation-based therapies. Furthermore, the proposed methodology for reconstructing large-scale 2D multicellular systems lays the groundwork for future extension to realistic 3D tissue models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".