Electrochemotherapy Effectiveness Loss due to Electric Field Indentation between Needle Electrodes: A Numerical Study
Bibliographic record
Abstract
Electrochemotherapy is an anticancer treatment based on applying electric field pulses that reduce cell membrane selectivity, allowing chemotherapy drugs to enter the cells. In parallel to electrochemotherapy clinical tests, in silico experiments have helped scientists and clinicians to understand the electric field distribution through anatomically complex regions of the body. In particular, these in silico experiments allow clinicians to predict problems that may arise in treatment effectiveness. The current work presents a metastatic case of a mast cell tumor in a dog. In this specific treatment planning study, we show that using needle electrodes has a possible pitfall. The macroscopic consequence of the electroporation was assessed through a mathematical model of tissue electrical conductivity. Considering the electrical and geometrical characteristics of the case under study, we modeled an ellipsoidal tumor. Initial simulations were based on the European Standard Operating Procedures for electrochemotherapy suggestions, and then different electrodes' arrangements were evaluated. To avoid blind spots, multiple applications are usually required for large tumors, demanding electrode repositioning. An effective treatment electroporates all the tumor cells. Partially and slightly overlapping the areas increases the session's duration but also likely increases the treatment's effectiveness. It is worth noting that for a single application, the needles should not be placed close to the tumor's borders because effectiveness is highly likely to be lost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".