Chemically induced cone degeneration in the 13-lined ground squirrel
Bibliographic record
Abstract
Abstract Animal models of retinal degeneration are critical for understanding disease and testing potential therapies. Inducing degeneration commonly involves the administration of chemicals that kill photoreceptors by disrupting metabolic pathways, signaling pathways, or protein synthesis. While chemically induced degeneration has been demonstrated in a variety of animals (mice, rats, rabbits, felines, 13-lined ground squirrels (13-LGS), pigs, chicks), few studies have used noninvasive high-resolution retinal imaging to monitor the in vivo cellular effects. Here, we used longitudinal scanning light ophthalmoscopy (SLO), optical coherence tomography, and adaptive optics SLO imaging in the euthermic, cone-dominant 13-LGS (46 animals, 52 eyes) to examine retinal structure following intravitreal injections of chemicals, which were previously shown to induce photoreceptor degeneration, throughout the active season of 2019 and 2020. We found that iodoacetic acid induced severe pan-retinal damage in all but one eye, which received the lowest concentration. While sodium nitroprusside successfully induced degeneration of the outer retinal layers, the results were variable, and damage was also observed in 50% of contralateral control eyes. Adenosine triphosphate and tunicamycin induced outer retinal specific damage with varying results, while eyes injected with thapsigargin did not show signs of degeneration. Given the variability of damage we observed, follow-up studies examining the possible physiological origins of this variability are critical. These additional studies should further advance the utility of chemically induced photoreceptor degeneration models in the cone-dominant 13-LGS.
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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 | 0.000 | 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".