DERIVING AN ELECTRIC CIRCUIT EQUIVALENT MODEL OF CELL MEMBRANE PORES IN ELECTROPORATION
Bibliographic record
Abstract
Electroporation is the formation of reversible pores in cell membranes under a brief pulse of high electric field. Dynamics of pore formation during electroporation suggests that the transmembrane potential would settle approximately at the threshold transmembrane potential and the transmembrane resistance would decrease significantly from the state of relaxation. The current electric circuit equivalent models for electroporation containing time-invariant, static and passive components are unable to capture the pore dynamics. A biophysically-inspired electric circuit equivalent model containing dynamic components for membrane pores has been derived using biological parameters. The model contains a voltage-controlled resistor driven by a two-stage cascaded integrator that is activated through a voltage-gated switch. Simulation results with the derived model showed higher accuracy compared to a commonly used model, where the transmembrane resistance decreased million-fold at the onset of electroporation and the transmembrane potential settled at 99.5% of the critical transmembrane potential, thus enabling improved dynamic behavior modeling ability of the pores in electroporation. The derived model allows fast and reliable analysis of this biophysical phenomenon and potentially aids in optimization of various parameters involved in electroporation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".