Nitric Oxide (NO) Mediates the Inhibition of Form-Deprivation Myopia by Atropine in Chicks
Bibliographic record
Abstract
Myopia is the most common childhood refractive disorder. Atropine inhibits myopia progression, but its mechanism is unknown. Here, we show that myopia-prevention by atropine requires production of nitric oxide (NO). Form-deprivation myopia (FDM) was induced in week-old chicks by diffusers over the right eye (OD); the left eye (OS) remained ungoggled. On post-goggling days 1, 3, and 5, OD received intravitreally 20 µL of phosphate-buffered saline (vehicle), or vehicle plus: NO source: L-arginine (L-Arg, 60-6,000 nmol) or sodium nitroprusside (SNP, 10-1,000 nmol); atropine (240 nmol); NO inhibitors: L-NIO or L-NMMA (6 nmol); negative controls: D-Arg (10 µmol) or D-NMMA (6 nmol); or atropine plus L-NIO, L-NMMA, or D-NMMA; OS received vehicle. On day 6 post-goggling, refractive error, axial length, equatorial diameter, and wet weight were measured. Vehicle-injected goggled eyes developed significant FDM. This was inhibited by L-Arg (ED50 = 400 nmol) or SNP (ED50 = 20 nmol), but not D-Arg. Higher-dose SNP, but not L-Arg, was toxic to retina/RPE. Atropine inhibited FDM as expected; adding NOS-inhibitors (L-NIO, L-NMMA) to atropine inhibited this effect dose-dependently, but adding D-NMMA did not. Equatorial diameter, wet weight, and metrics of control eyes were not affected by any treatment. In summary, intraocular NO inhibits myopia dose-dependently and is obligatory for inhibition of myopia by atropine.
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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 | 0.001 |
| 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".