Lethal Electric Field Thresholds for Cerebral Cells With Irreversible Electroporation and H-FIRE Protocols: An In Vitro Three-Dimensional Cell Model Study
Bibliographic record
Abstract
The lethal electric field (LEF) thresholds for three typical cerebral cells, including a malignant glioblastoma (GBM) cell line and two cell lines from the healthy blood-brain barrier (BBB), treated by irreversible electroporation (IRE) or high-frequency irreversible electroporation (H-FIRE) protocols were investigated in an in vitro three-dimensional (3D) cell model. A conventional IRE protocol (90 pulses, 1 Hz, and 100-μs pulse duration) and three novel H-FIRE protocols (1-3-1, 0.5-1-0.5, and 1-1-1) were used to treat the cerebral cells in both 3D single-cell and two-cell models. The electrical conductivity of the 3D cell model under different electric field strengths were characterized with the method of electrochemical impedance spectroscopy (EIS). Based on EIS, a numerical electrothermal model of electroporation was built for the determination of the LEF threshold with different protocols and temperature monitoring. Cell viability was assessed by fluorescence staining 6 h after the treatment. The results showed no thermal lethal effect on cells when these protocols were used. The LEF threshold for GBM cells was significantly lower than that of the healthy BBB cells. These results suggest the possibility of selective ablation of human cerebral GBM by IRE and H-FIRE treatments with no injury or reversible injury to healthy cells, and the potential use of IRE or H-FIRE for transient disruption of the BBB to allow chemotherapy to reach the tumor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".