Subiculum network excitability is increased in a rodent model of temporal lobe epilepsy
Bibliographic record
Abstract
In this study, we used in vitro electrophysiology along with immunohistochemistry and molecular techniques to study the subiculum--a limbic structure that gates the information flow from and to the hippocampus--in pilocarpine-treated epileptic rats. Comparative data were obtained from age-matched nonepileptic controls (NEC). Subicular neurons in hippocampal-entorhinal cortex (EC) slices of epileptic rats were: (i) hyperexcitable when activated by CA1 or EC inputs; and (ii) generated spontaneous postsynaptic potentials at higher frequencies than NEC cells. Analysis of pharmacologically isolated, GABA(A) receptor-mediated inhibitory postsynaptic potentials revealed more positive reversal potentials in epileptic tissue (-67.8 +/- 6.3 mV, n = 16 vs. -74.8 +/- 3.6 mV in NEC, n = 13; P < 0.001) combined with a reduction in peak conductance (17.6 +/- 11.3 nS vs. 41.1 +/- 26.7 nS in NEC; P < 0.003). These electrophysiological data correlated in the epileptic subiculum with (i) reduced levels of mRNA expression and immunoreactivity of the neuron-specific potassium-chloride cotransporter 2; (ii) decreased number of parvalbumin-positive cells; and (iii) increased synaptophysin (a putative marker of sprouting) immunoreactivity. These findings identify an increase in network excitability within the subiculum of pilocarpine-treated, epileptic rats and point at a reduction in inhibition as an underlying mechanism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".