Selective stimulation of GalR1 and GalR2 in rat substantia gelatinosa reveals a cellular basis for the anti- and pro-nociceptive actions of galanin
Bibliographic record
Abstract
Galanin modulates spinal nociceptive processing by interacting with two receptors, GalR1 and GalR2. The underlying neurophysiological mechanisms were examined by whole-cell recording from identified neurons in the substantia gelatinosa of young adult rats. GalR1 was activated with a 'cocktail' containing the GalR1/2 agonist, AR-M 961 (0.5 microM), in the presence of the GalR2 antagonist, M871 (1.0-2.5 microM). GalR2 was activated with the selective agonist, AR-M 1896 (0.5-1.0 microM). Application of the 'GalR1 agonist cocktail' often activated an inwardly-rectifying conductance in delay firing (excitatory) and tonically firing (inhibitory) neurons. This conductance was not activated by AR-M 1896 which instead decreased or increased an outwardly-rectifying conductance at voltages positive to -70 mV. Despite this variability in its actions on current-voltage relationships, AR-M 1896 very consistently decreased membrane excitability, as measured by cumulative action potential latency in response to a depolarizing current ramp. This strong GalR2-mediated effect was seen in neurons where membrane conductance was decreased, and where membrane excitability might be predicted to increase. GalR2 was also located presynaptically, as AR-M 1896 increased the interevent interval of spontaneous EPSCs in both delay and tonic cells. By contrast, the 'GalR1 agonist cocktail' had little effect on spontaneous EPSCs, suggesting that presynaptic terminals do not express GalR1. These diverse actions of GalR1 and GalR2 activation on both inhibitory and excitatory neurons are discussed in relation to the known spinal antinociceptive and pro-nociceptive actions of galanin, to the possible association of GalR1 with the inhibitory G-protein, G(i/o) and to report that GalR2 activation suppresses Ca2+ channel currents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".