Improved Immunohistochemistry of Mouse Eye Sections Using Davidson's Fixative and Melanin Bleaching
Bibliographic record
Abstract
Immunohistochemistry (IHC) and immunofluorescence (IF) are fundamental molecular biology techniques to assess protein expression. However, the melanin present normally in the eye in the uveal tract (choroid, iris, and ciliary body) and the retinal pigment epithelium (RPE) poses a significant challenge for IHC and IF. This is because melanin interferes with both chromogenic and fluorescent detection methods. Additionally, formalin fixation, which is commonly used for IHC, can result in shrinkage and loss of cellular detail in the eye. This protocol provides an optimized approach using Davidson's fixative with a hydrogen peroxide bleaching step to eliminate melanin interference in the mouse eye, improving the quality and interpretability of IHC analyses of the uveal tract and RPE. It is particularly useful for the analysis of uveal melanoma. Key features • Davidson's fixative minimizes shrinkage of ocular tissues and preserves cell structure better than formalin. • Hydrogen peroxide bleaching step removes all melanin, even in highly pigmented samples. • The protocol is compatible with other tissues that require melanin removal.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".