Dose–response of the cultured bovine lens to butyl, methyl and propyl parabens
Bibliographic record
Abstract
Pre-screening of cosmetic ingredients is vital for consumer safety. Previous in vivo techniques, such as the Draize test, have proved to be unreliable in predicting ocular irritancy and therefore there is a need for alternate testing methodologies. One such test is the scanning laser in vitro assay system which quantifies irritancy based on the focusing ability of the cultured bovine lens. In combination with confocal microscopy, a more thorough documentation of ocular irritancy can be achieved. This study investigates the response of cultured bovine lenses over time to butyl, methyl and propyl parabens, which are common antimicrobial agents found in cosmetic and ophthalmic products. The focusing ability of the lens was measured with an automated laser scanner over a period of 96 h. At 120 h post-treatment, the lenses were analysed by using a confocal laser scanning microscope to determine the characteristics of nuclei, and the morphology and distribution of mitochondria within the lenses. Irritancy to the three parabens was investigated at both an optical and cellular level. Each of the parabens was tested at 0.002% and 0.2%, where the 0.2% butyl paraben was found to be the most irritating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".