d‐Serine enhancement of NMDA receptor‐mediated calcium increases in rat retinal ganglion cells
Bibliographic record
Abstract
NMDA receptor (NMDAR) activation is enhanced by d-serine or glycine acting at a specific binding site. Previous work has shown d-serine enhancement of NMDAR currents in retinal ganglion cells. One of the major functions of most NMDA channels is to permit calcium influx into cells. We show that d-serine enhances glutamate-induced calcium responses in immunopanned retinal ganglion cells. This effect was specific to NMDA receptors as similar results were found with NMDA, but not kainate, and was reduced or blocked by modulators of the NMDAR coagonist binding site. d-Serine and glycine enhanced glutamate-induced calcium responses in a dose-dependent manner and at equimolar concentrations there was no difference in the efficacy of the coagonists. In isolated retinas NMDA-induced calcium responses were enhanced by d-serine coapplication in 46% of ganglion cells. Endogenous d-serine degradation by treatment with d-amino acid oxidase caused a approximately 45% decrease in the NMDA-induced response that could be reversed by coapplication with d-serine. d-Serine and glycine were equally effective in enhancing glutamatergic calcium responses. Endogenous d-serine contributes to NMDAR activation in retinal wholemounts and some but not all retinal ganglion cells may experience saturating levels of d-serine or glycine.
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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) | 0.002 | 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".