Nestin, A New Marker, Expressed in Müller Cells Following Retinal Injury
Bibliographic record
Abstract
PURPOSE: To investigate whether nestin would be a useful marker for retinal injury and also to ascertain a better understanding of the roles of Müller cells in the injured retina by the use of damaged rat retina. METHODS: A total of 33 adult female Wistar rats were used in this study. Three were used as controls and the remaining as retinal injury modes (6 for hypoxia; 15 for experimental glaucoma and 9 for optic nerve transection). Double immunofluorescence labeling was carried out between nestin and glutamine synthetase (GS), and between glial fibrillary acidic protein (GFAP) and GS antisera in normal and pathological retinae. RESULTS: The results showed that there were no nestin nor GFAP staining in mature Müller cells of the normal retina. A major finding was that nestin expression was induced in Müller cells subjected to hypoxia, glaucoma and optic nerve transection. CONCLUSIONS: These results suggest that nestin as well as GFAP (even more sensitive than GFAP) are useful and reliable biomarkers for retinal damage. The more intense expression of nestin, GFAP and GS in the end-feet of Müller cells suggest that they may help to maintain the retinal structural integrity and to enhance functional recovery in various retinal diseases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".