Cell-Specific Markers for the Identification of Retinal Cells by Immunofluorescence Microscopy
Bibliographic record
Abstract
Identification and visualization of specific cells and cellular structures in the retina are fundamental for understanding the visual process, retinal development, disease progression, and therapeutic intervention. The increased usage of transgenic and naturally occurring mutant mice has further emphasized the need for retinal cell-specific imaging. Immunofluorescence microscopy of retinal cryosections and whole mount tissue labeled with cell-specific markers has emerged as the method of choice for identifying specific cell populations and mapping their distribution within the retina. In most cases indirect labeling methods are employed in which lightly fixed retinal samples are first labeled with a primary antibody targeted against a cell-specific protein of interest and then labeled with a fluorescent dye-tagged secondary antibody that recognizes the primary antibody. The localization and relative abundance of the protein can readily be imaged under a conventional fluorescent or confocal scanning microscope. Immunofluorescence labeling can be adapted for imaging more than one protein antigen through the use of multiple antibodies and different, nonoverlapping fluorescent dyes. A number of well-characterized immunochemical markers are now available for detecting photoreceptors, bipolar cells, amacrine cells, horizontal cells, Müller cells, and retinal pigment epithelial cells in the retina of mice, and other mammals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".