Slow Excitation of Cultured Rat Retinal Ganglion Cells by Activating Group I Metabotropic Glutamate Receptors
Bibliographic record
Abstract
As in many CNS neurons, retinal ganglion cells (RGCs) receive fast synaptic activation through postsynaptic ionotropic receptors. However, the potential role of postsynaptic group I metabotropic glutamate receptors (mGluRs) in these neurons is unknown. In this study we first demonstrated that the selective group I mGluR agonist (S)-3,5-dihydroxyphenylglycine (DHPG) increased intracellular calcium concentration in neurons within the ganglion cell layer of the rat retina. This prompted us to use an immunopanned-RGC and cortical astroglia coculture preparation to explore the effect of group I mGluR activation on the electrophysiological properties of cultured RGCs. Using perforated patch-clamp recordings in current-clamp configuration, we found that application of DHPG increased spontaneous spiking and depolarized the resting membrane potential of RGCs. This boosting effect was attributed to an increase in membrane resistance due to blockade of a background K(+) conductance. Further experiments showed that the group I mGluR-sensitive K(+) conductance was not blocked by 3 mM Cs(+), but was sensitive to acidification. Pharmacological studies indicated that the effect of DHPG on RGCs was mediated by the mGluR1 rather than the mGluR5 receptor subtype. Our results suggest a facilitatory role for group I mGluR activation in modulating RGC excitability in the mammalian inner retina.
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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".