Galanin increases membrane excitability and enhances Ca<sup>2+</sup>currents in adult, acutely dissociated dorsal root ganglion neurons
Bibliographic record
Abstract
We examined the effect of galanin (10(-15) - 10(-7) M) on dispersed, mainly small-sized dorsal root ganglion (DRG) neurons in adult rats using whole-cell patch-clamp. Galanin and AR-M1896, a selective galanin type 2 receptor (GalR2) agonist, both significantly increased the number of action potentials in response to current pulses in 77% of the neurons, indicating an increase in excitability. Galanin also caused a rise in input resistance, decreased the holding current for -60 mV and depolarized the resting potential. In addition, Ca(2+) currents elicited by voltage steps were significantly increased by both galanin and AR-M1896 in nearly 70% of the cells. This enhancement was observed in 30% of the neurons in the presence of nimodipine or omega-conotoxin, but in each case approximately 60% less than without blocking either N- or L-type Ca(2+) channels, indicating modulation of both types of Ca(2+) channels. The percentage of small- and medium-sized neurons expressing GalR2 mRNA in DRGs in situ was similar to that showing increased excitability and Ca(2+) current after galanin application, i.e. approximately 70-80% of the neurons. The findings suggest that GalR2 has a role in controlling both the excitability, probably by inhibition of GIRK or leak K(+) channels, and Ca(2+) entry in a large population of presumably nociceptive neurons. The combination of the two effects, which possibly arise from separate biochemical pathways, would increase excitability and enhance intracellular Ca(2+) signalling which would enhance sensory transmission. These mechanisms involving GalR2 receptors may underlie the pronociceptive effects of galanin described in the literature.
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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 | 0.000 |
| 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".