The pilocarpine model of temporal lobe epilepsy
Bibliographic record
Abstract
Understanding the pathophysiogenesis of temporal lobe epilepsy (TLE) largely rests on the use of models of status epilepticus (SE), as in the case of the pilocarpine model. The main features of TLE are: (i) epileptic foci in the limbic system; (ii) an "initial precipitating injury"; (iii) the so-called "latent period"; and (iv) the presence of hippocampal sclerosis leading to reorganization of neuronal networks. Many of these characteristics can be reproduced in rodents by systemic injection of pilocarpine; in this animal model, SE is followed by a latent period and later by the appearance of spontaneous recurrent seizures (SRSs). These processes are, however, influenced by experimental conditions such as rodent species, strain, gender, age, doses and routes of pilocarpine administration, as well as combinations with other drugs administered before and/or after SE. In the attempt to limit these sources of variability, we evaluated the methodological procedures used by several investigators in the pilocarpine model; in particular, we have focused on the behavioural, electrophysiological and histopathological findings obtained with different protocols. We addressed the various experimental approaches published to date, by comparing mortality rates, onset of SRSs, neuronal damage, and network reorganization. Based on the evidence reviewed here, we propose that the pilocarpine model can be a valuable tool to investigate the mechanisms involved in TLE, and even more so when standardized to reduce mortality at the time of pilocarpine injection, differences in latent period duration, variability in the lesion extent, and SRS frequency.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".