Adenosine A<sub>1</sub>-Receptor Modulation of Glutamate-Induced Calcium Influx in Rat Retinal Ganglion Cells
Bibliographic record
Abstract
PURPOSE: Although adenosine receptors (A(1)-Rs and A(2)-Rs) have been identified in the mammalian retina, the role of adenosine in this tissue is not fully understood. The purpose of this work was to investigate the action of adenosine on glutamate-induced calcium influx in rat retinal ganglion cells (RGCs) and to determine whether adenosine modulates RGC voltage-gated calcium channels. METHODS: Purified RGC cultures were generated from neonatal rats with a two-step panning procedure. Isolated RGCs were loaded with the ratiometric calcium-indicator dye fura-2, and the effect of adenosine (and related agonists and antagonists) on intracellular calcium levels ([Ca(2+)](i)) during exposure to glutamate (10 microM with 10 microM glycine) was assessed. The effect of adenosine on calcium channel currents was also studied in isolated RGCs with whole-cell patch-clamp techniques. In addition, the effect of adenosine on [Ca(2+)](i) was investigated in fura dextran-loaded RGCs in an intact adult rat retina preparation. RESULTS: In isolated RGCs, adenosine (10 and 100 microM) significantly reduced the glutamate-induced increase in [Ca(2+)](i) ( approximately 30%). The effect of adenosine was blocked by the A(1)-R antagonist 8-cyclopentyl-1,3-dipropylxanthine (DPCPX), but not by the A(2)-R antagonist 3,7-dimethyl-1-propargylxanthine (DMPX). Adenosine (10 microM) inhibited calcium channel currents by 43%, and again this effect was blocked by DPCPX, but not DMPX. Adenosine (100 microM) also significantly reduced the elevation of [Ca(2+)](i) in RGCs in the intact retina during exposure to N-methyl-d-aspartate (NMDA; 100 microM). CONCLUSIONS: Adenosine can inhibit glutamate-induced calcium influx and voltage-gated calcium currents in rat RGCs through A(1)-R activation. This work supports a role for adenosine as a neuromodulator of mammalian RGCs.
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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".