Gabapentin actions on Kir3 currents and N‐type Ca<sup>2+</sup> channels via GABA<sub>B</sub> receptors in hippocampal pyramidal cells
Bibliographic record
Abstract
Gabapentin is a clinically effective anticonvulsant with an unclear mechanism of action. It was described as a GABA(B(1a,2)) receptor subtype-selective agonist, activating postsynaptic K(+) currents and inhibiting postsynaptic Ca(2+) channels in CA1 pyramidal cells, but without presynaptic actions. These activities appeared controversial and we therefore sought to further clarify gabapentin actions in rat hippocampal slices by characterizing K(+) currents and Ca(2+) channels targeted by gabapentin using whole-cell recording and multiphoton Ca(2+) imaging. 1) We found that gabapentin and baclofen induced inwardly rectifying K(+) currents (K(Gbp) and K(Bac), respectively), sensitive to Ba(2+) and Cs(+). 2) A constitutively active K(IR) current, independent of GABA(B) receptor activation and sensitive to Ba(2+) and Cs(+) was also present. 3) K(Gbp), K(Bac), and K(IR) currents showed some differences in sensitivity to Ba(2+) and Cs(+), indicating the possible activation of distinct Kir3 currents, independent of K(IR), by gabapentin and baclofen. 4) Gabapentin inhibition of Ca(2+) channels was abolished by omega-conotoxin GVIA, but not by omega-agatoxin IVA and nimodipine, indicating a predominant action of gabapentin on N-type Ca(2+) channels. 5) Gabapentin actions were linked to activation of pertussis toxin-sensitive G-proteins since N-ethylmaleimide (NEM) blocked K(Gbp) activation and Ca(2+) channel inhibition by gabapentin. 6) Finally, gabapentin reduced epileptiform discharges in slices via GABA(B) receptor activation. The anticonvulsant actions of gabapentin in hippocampal cells may thus involve GABA(B) receptor coupling to G-proteins and modulation of Kir3 and N-type Ca(2+) channels. Moreover, gabapentin and baclofen activation of GABA(B) receptors may couple to distinct cellular targets.
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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".