Analysis of the long-term actions of gabapentin and pregabalin in dorsal root ganglia and substantia gelatinosa
Bibliographic record
Abstract
The α2δ-ligands pregabalin (PGB) and gabapentin (GBP) are used to treat neuropathic pain. We used whole cell recording to study their long-term effects on substantia gelatinosa and dorsal root ganglion (DRG) neurons. Spinal cord slices were prepared from embryonic day 13 rat embryos and maintained in organotypic culture for >5 wk (neuronal age equivalent to young adult rats). Exposure of similarly aged DRG neurons (dissociated and cultured from postnatal day 19 rats) to GBP or PGB for 5-6 days attenuated high-voltage-activated calcium channel currents (HVA ICa). Strong effects were seen in medium-sized and in small isolectin B4-negative (IB4-) DRG neurons, whereas large neurons and small neurons that bound isolectin B4 (IB4+) were hardly affected. GBP (100 μM) or PGB (10 μM) were less effective than 20 μM Mn(2+) in suppression of HVA ICa in small DRG neurons. By contrast, 5-6 days of exposure to these α2δ-ligands was more effective than 20 μM Mn(2+) in reducing spontaneous excitatory postsynaptic currents at synapses in substantia gelatinosa. Spinal actions of gabapentinoids cannot therefore be ascribed to decreased expression of HVA Ca(2+) channels in primary afferent nerve terminals. In substantia gelatinosa, 5-6 days of exposure to PGB was more effective in inhibiting excitatory synaptic drive to putative excitatory neurons than to putative inhibitory neurons. Although spontaneous inhibitory postsynaptic currents were also attenuated, the overall long-term effect of α2δ-ligands was to decrease network excitability as monitored by confocal Ca(2+) imaging. We suggest that selective actions of α2δ-ligands on populations of DRG neurons may predict their selective attenuation of excitatory transmission onto excitatory vs. inhibitory neurons in substantia gelatinosa.
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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 | 0.001 | 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".