Endogenous Opioid Signaling in the Mouse Retina Modulates Pupillary Light Reflex
Bibliographic record
Abstract
Opioid peptides and their receptors are expressed in the mammalian retina; however, little is known about how they might affect visual processing. The melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs), which mediate important non-image-forming visual processes such as the pupillary light reflex (PLR), express β-endorphin-preferring, µ-opioid receptors (MORs). The objective of the present study was to elucidate if opioids, endogenous or exogenous, modulate pupillary light reflex (PLR) via MORs expressed by ipRGCs. MOR-selective agonist [D-Ala2, MePhe4, Gly-ol5]-enkephalin (DAMGO) or antagonist D-Phe-Cys-Tyr-D-Trp-Arg-Thr-Pen-Thr-NH2 (CTAP) was administered via intravitreal injection. PLR was recorded in response to light stimuli of various intensities. DAMGO eliminated PLR evoked by light with intensities below melanopsin activation threshold but not that evoked by bright blue irradiance that activated melanopsin signaling, although in the latter case, DAMGO markedly slowed pupil constriction. CTAP or genetic ablation of MORs in ipRGCs slightly enhanced dim-light-evoked PLR but not that evoked by a bright blue stimulus. Our results suggest that endogenous opioid signaling in the retina contributes to the regulation of PLR. The slowing of bright light-evoked PLR by DAMGO is consistent with the observation that systemically applied opioids accumulate in the vitreous and that patients receiving chronic opioid treatment have slow PLR.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".