Deep brain stimulation restores the glutamatergic and GABAergic synaptic transmission and plasticity to normal levels in kindled rats
Bibliographic record
Abstract
BACKGROUND: The precise effect of low frequency stimulation (LFS) as a newly postulated, anticonvulsant therapeutic approach on seizure-induced changes in synaptic transmission has not been completely determined. HYPOTHESIS: In this study, the LFS effect on impaired, synaptic plasticity in kindled rats was investigated. METHODS: Hippocampal kindled rats received LFS (4 trials consisting of one train of 200 monophasic square waves, 0.1 ms pulse duration, 1 Hz) on four occasions. LTP induction was evaluated using whole-cell recordings of evoked excitatory and inhibitory post-synaptic potentials (EPSPs and IPSPs respectively) in CA1 neurons in hippocampal slices. In addition, the hippocampal excitatory and inhibitory post-synaptic currents (EPSCs and IPSCs), and the gene expression of NR2A, GluR2 and γ2 were evaluated. RESULTS: LTP induction was attenuated in excitatory and inhibitory synapses in hippocampal slices of kindled rats. When LFS was applied in kindled animals, LTP was induced in EPSPs and IPSPs. Moreover, LFS increased and decreased the threshold intensities of EPSCs and IPSCs respectively. In kindled animals, NR2A gene expression increased, while γ2 gene expression decreased. GluR2 gene expression did not significantly change. Applying LFS in kindled animals mitigated these changes: No significant differences were observed in NR2A, γ2 and GluR2 gene expression in the kindled+LFS and control groups. CONCLUSION: The application of LFS in kindled animals restored LTP induction in both EPSPs and IPSPs, and returned the threshold intensity for induction of EPSCs, IPSCs and gene expression to similar levels as controls.
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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) | 0.001 | 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".