Dopaminergic and GABAergic amacrine cells are direct targets of melatonin: Immunocytochemical study of mt<sub>1</sub> melatonin receptor in guinea pig retina
Bibliographic record
Abstract
Distribution of the mt1 melatonin receptor in the guinea pig retina was immunocytochemically investigated using peptide-specific anti-mt1 receptor antibody. Western blots of the guinea pig retina showed a single band at approximately 37 kilodalton (kD) immunoreactive to the anti-mt1 antibody. The most intense immunoreactivity for the mt1 receptor was detected in the cell bodies of ganglion cells. Their dendrites and axons were also immunolabeled. Subpopulations of amacrine cells, the inner plexiform layer, and the outer plexiform layer also exhibited moderate to weak immunolabeling. The mt1-positive amacrine cells were located either at the vitreal border of the inner nuclear layer or displaced in the ganglion cell layer. Double immunolabeling using antibodies to the mt1 receptor and tyrosine hydroxylase revealed that the majority of dopaminergic amacrine cells showed mt1 immunoreactivity. Almost all the ICA type dopaminergic cells were mt1 positive while the 2CA type cells less frequently exhibited mt1 immunoreaction. By double immunolabeling for the mt1 receptor and GABA, more than 50% of the mt1-immunoreactive amacrine cells were shown to be GABAergic neurons. Approximately one-third of the GABAergic amacrine cells were immunolabeled for the mt1 receptor. The present results demonstrate expression of the mt1 receptor in diverse neuronal cell types in the guinea pig retina and provide the first evidence for the direct effect of melatonin on dopaminergic and GABAergic amacrine cells via the mt1 receptor.
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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 | 0.001 |
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".