Histamine H<sub>3</sub> receptor activation increases the firing of striatal medium spiny neurons in slices from infantile rats
Bibliographic record
Abstract
Striatal medium spiny neurons (MSN) form two subpopulations (MSN-D1 and MSN-D2) according to the expression of dopamine D1 or D2 receptors and their target regions. The activation of postsynaptic histamine H1 and H2 receptors increases MSN-D1 and MSN-D2 excitability. Since MSN also express H3 receptors (H3Rs), in this work we explored the effect of their activation on MSN firing. Electrophysiological recordings (whole-cell patch-clamp, current-clamp mode) were conducted on forebrain slices from infantile rats (12–16 postnatal days). In both MSN-D1 and MSN-D2 perfusion with the H3R agonist immepip (1 µmol/L) increased neuronal firing evoked by current injection, an effect reproduced by R-α-methylhistamine (1 µmol/L) and prevented by the antagonist clobenpropit (10 µmol/L). Blockade of N- or P/Q-type voltage-activated calcium channels by ω-conotoxin-GVIA (1 µmol/L) or ω-agatoxin-TK (400 nmol/L) increased MSN firing but did not preclude the immepip effect. The potassium channel blockers 4-aminopyridine (1 mmol/L) and tetraethylammonium (300 µmol/L) increased neuronal firing and prevented the immepip action. Likewise, the KV7 channel blocker XE-991 (10 µmol/L) and the muscarinic receptor agonist carbachol (10 µmol/L) increased MSN firing frequency and occluded the immepip effect. These data indicate that the activation of postsynaptic H3Rs facilitates MSN-D1 and MSN-D2 firing by inhibiting KV7 potassium channels.
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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".