An <i>in vivo</i> study of a custom-made high-frequency irreversible electroporation generator on different tissues for clinically relevant ablation zones
Bibliographic record
Abstract
Purpose To examine the ablation zone, muscle contractions, and temperature increases in both rabbit liver and kidney models in vivo for a custom-made high-frequency irreversible electroporation (H-FIRE) generator.Materials and Methods A total of 18 New Zealand white rabbits were used to investigate five H-FIRE protocols (n = 3 for each protocol) and an IRE protocol (n = 3) for the performance of the designed H-FIRE device in both liver and kidney tissues. The ablation zone was determined by using histological analysis 72 h after treatment. The extent of muscle contractions and temperature change during the application of pulse energy were measured by a commercial accelerometer attached to animals and fiber optic temperature probe inserted into organs with IRE electrodes, respectively.Results All H-FIRE protocols were able to generate visible ablation zones without muscle contractions, for both liver and kidney tissues. The area of ablation zone generated in H-FIRE pulse protocols (e.g., 0.3–1 μs, 2000 V, and 90–195 bursts) appears similar to that of IRE protocol (100 μs, 1000 V, and 90 pulses) in both liver and kidney tissues. No significant temperature increase was noticed except for the protocol with the highest pulse energy (e.g., 1 μs, 2000 V, and 180 bursts).Conclusion Our work serves to complement the current H-FIRE pulse waveforms, which can be optimized to significantly improve the quality of ablation zone in terms of precision for liver and kidney tumors in clinical setting.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".