A Single-Cell Electroporation Model for Quantitatively Estimating the Pore Area Ratio by High-Frequency Irreversible Electroporation
Bibliographic record
Abstract
The electroporation technique utilizes pulsed electric fields to induce porous defects in the cell membrane, and the technique can be used for delivering drugs into cells and killing cancer cells. To develop an electric pulse protocol in the clinic with this technique, the key issue is to understand the evolution of pores in the cell membrane during the process of electroporation. This paper presents a study to address this issue. Specifically, a mathematical model of single-cell electroporation (SCE) was developed, which includes pore area ratio (PAR) as an indicator of the electroporation dynamics and area weight for considering the 3D nature of cells. The model was employed to simulate the electroporation of a single cell with different high-frequency irreversible electroporation (H-FIRE) protocols. The simulation result has found that the change of PAR with respect to the time duration of electroporation follows a sigmoid pattern to increase under specific protocols, which is called the cumulative effect of PAR. Subsequently, the relationship between the protocol of H-FIRE, described by a set of pulse parameters such as pulse width, pulse delay, electric field strength, and pulse burst duration, and the cumulative effect of PAR was established, which thereby allows designing the protocol to kill cells effectively. The study concluded that the proposed SCE model, along with the cumulative effect of PAR, is useful in designing H-FIRE protocols for the ablation of cancer tumors in the clinic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".