INFLUENCE OF CELLULAR PROPERTIES ON THE ELECTRIC FIELD DISTRIBUTION AROUND A SINGLE CELL
Bibliographic record
Abstract
Electric flelds have been widely used for the treatment of neurological diseases, using techniques such as non-invasive brain stimulation. An electric current controls cell excitability by imposing voltage changes across the cell membrane. At the same time, the presence of the cell itself causes a re-distribution of the local electric fleld. Computation of the electric fleld distribution at a single cell microscopic level is essential in understanding the mechanism of electric stimulation. In addition, the impact of the cellular biophysical properties on the fleld distribution in the vicinity of the cell should also be addressed. In this paper, we have begun by flrst computing the fleld distribution around and within a spherical model cell. The electric flelds in the three regions difiered by several orders of magnitude. The fleld intensity in the extracellular space was of the same order as that of the externally applied fleld, while in the membrane, it was calculated to be several thousand times greater than the applied fleld. In contrast, the fleld intensity inside the cell was greatly attenuated to approximately 1/133th of the applied fleld. We then performed a
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".