Neurosteroids Exhibit Differential Effects on mIPSCs Recorded From Normal and Seizure Prone Rats
Bibliographic record
Abstract
In the perirhinal cortex of seizure prone (SP) rats, GABA(A)-mediated miniature inhibitory postsynaptic currents (mIPSCs) are smaller in amplitude but have longer deactivation phases than mIPSCs recorded in normal control (NC; outbred) rats. These differences in mIPSCs are correlated to the relatively higher alpha1 subunit expression in the NC rat strains and the higher alpha2, alpha3, and alpha5 subunit expression in the SP strain. Using patch-clamp recording, we investigated how the neurosteroids tetrahydrodeoxcorticosterone (THDOC) and allopregnanolone at physiological and pharmacological concentrations may differentially affect the mIPSCs in the perirhinal cortex of brain slices isolated from SP and NC rats. We found that 100 nM THDOC prolonged the time course and increased the amplitude of both the mono- and biphasic mIPSCs in the SP rats, but these effects were smaller in the NC rats. By comparison, allopregnanolone (100 nM) had small effects in both the NC and SP rats. At 1.0 microM, THDOC enhanced mIPSCs in both strains, but this effect was not greater in the SP rat than it was at 100 nM. By contrast, allopregnanolone (500 nM) enhanced the time course of the mIPSCs in both strains but it reduced mIPSC amplitudes as well. THDOC (100 nM) was much more effective than 100 nM allopregnanolone in inducing a tonic current in SP and NC rats. These data show that neurosteroids modulate synaptic activity at synapses having different biophysical behaviors. As differing GABA(A) receptors are targeted by subsets of interneurons, these data suggest these neurosteroids may selectively modulate one inhibitory input over another.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".