Modulation of voltage-gated Ca<sup>2+</sup> channels in rat retinal ganglion cells by gabapentin
Bibliographic record
Abstract
The α2δ auxiliary subunits of voltage-gated Ca2+ channels (VGCCs) are important modulators of VGCC function. Gabapentin interacts with α2δ1 and α2δ2 subunits and is reported to reduce Ca2+ channel current amplitude (ICa). This study aimed to determine the effects of gabapentin on VGCCs in retinal ganglion cells (RGCs). Whole cell patch clamp was used to record ICa in isolated RGCs, and calcium imaging was used to measure Ca2+ transients from RGCs in situ. Immunohistochemistry was used to detect the presence of α2δ1-containing VGCCs in isolated RGCs in the absence and presence of gabapentin pretreatment. Acute administration of gabapentin reduced ICa and Ca2+ transients compared to control conditions. In isolated RGCs, pretreatment with gabapentin (4-18 h) reduced ICa, and cell surface α2δ1 staining was reduced compared to nonpretreated cells. Acute administration of gabapentin to isolated RGCs that had been pretreated further reduced ICa. These results show that gabapentin has both short-term and long-term mechanisms to reduce ICa in isolated RGCs. Some Ca2+ channel blockers have been shown to protect RGCs in retinal trauma suggesting that modulation of VGCCs by gabapentin may prevent the deleterious effects of elevated Ca2+ levels in RGCs in trauma and disease.
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 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".