Organ Culture System for Assessing the Toxicity of Intraocular Treatment Excipients and Pharmaceuticals
Bibliographic record
Abstract
As the leading cause of blindness, cataracts are a significant burden for the tens of millions of people affected globally by this condition. Chemical exposures, among other environmental factors, are an established cause of cataracts. Ocular toxicity testing can assess whether pharmaceuticals and their components may contribute to lens damage that may lead to cataracts or aid the treatment of cataracts. In vitro studies and in vivo animal testing can be used for assessing the safety of chemicals prior to clinical studies. The Draize test-the current in vivo standard for ocular toxicity and irritancy testing-has been criticized for lack of sensitivity and objective measurements of determining ocular toxicity. In vitro cell-based assays are limited as cell cultures cannot appropriately model an intact functional lens. The method described here is a sensitive in vitro alternative to animal testing, designed to evaluate the response of the intact bovine lens to treatment at both the cellular activity level and for overall refractive performance. The non-toxic reagent resazurin is metabolized in proportion to the level of cell activity. The lens laser-scanner assay measures the ability of the lens to refract incident beams of light to a single point with minimal error, directly relevant to its natural function. The method may be used to determine both acute and delayed changes in the lens, as well as the recovery of the lens from chemical or environmental exposures.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".