Cannabinoid receptor-mediated inhibition of calcium signaling in rat retinal ganglion cells.
Bibliographic record
Abstract
PURPOSE: The physiological actions of CB(1) cannabinoid receptors (CB(1)Rs) in mammalian retina have yet to be fully described in all cell types. Here we investigate the actions of CB(1)R activation on high-voltage-activated (HVA) Ca(2+) channel currents in purified cultures of rat retinal ganglion cells (RGCs). METHODS: Reverse transcriptase polymerase chain reaction (RT-PCR) and immunocytochemistry were used to determine the presence of CB(1)R mRNA and protein in a purified RGC culture generated from neonatal rats using a two-step panning procedure. Ruptured-patch whole-cell voltage clamp was used to test the effect of CB(1)R agonists (WIN 55,212-2) and antagonists (SR141716A, AM281) on HVA Ca(2+) channel currents. RESULTS: RT-PCR analysis confirmed CB(1)R mRNA in cultured RGCs and immunocytochemistry for CB(1)R protein revealed labeling in both the cell body and neurites of isolated RGCs. Patch-clamp recording from cultured rat RGCs showed that the CB(1)R agonist WIN 55,212-2 inhibited HVA Ca(2+) channel currents up to 50% in a concentration-dependent manner (0.5, 1, and 5 muM). The Ca(2+) channel current inhibition by WIN 55,212-2 was blocked by CB(1)R antagonists AM281 and SR141716. CONCLUSIONS: Activation of CB(1)Rs in cultured RGCs inhibits HVA Ca(2+) channel currents. These data show that cannabinoids can modify the excitability of RGCs and could affect retinal output. This finding has implications for retinal signal processing as it suggests that endogenous cannabinoids have inhibitory effects on RGCs and that exogenous cannabinoids could modulate retinal function by this pathway as well.
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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) | 0.001 | 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 | 0.001 |
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".