Downregulation of BM88 After Optic Nerve Injury
Bibliographic record
Abstract
PURPOSE: BM88 is a cell-cycle exit and neuronal differentiation protein that has been used as a marker of surviving retinal ganglion cells (RGCs) after optic nerve injury. Thy1.1 has also been used as a marker for RGC loss, but after optic nerve crush (ONC) a decrease in Thy1.1 expression precedes the loss of RGCs. The purpose of this study was to determine if BM88 expression was correlated with RGC loss after ONC and optic nerve transection (ONT) injuries. METHODS: Rats were injected with Fluorogold (FG) into the superior colliculus to label RGCs and received ONC or ONT 7 days later. Eyes were collected 2 to 28 days after injury. Retinas were labeled with BM88 and intensity of the BM88 cell labeling was measured. RESULTS: In control retinas, 98.9% of RGCs were immunoreactive (-IR) for BM88. There was a significant downregulation of BM88 by 52% to 80% of RGCs 7 days after ONC or ONT. The staining intensity of the remaining labeled cells was reduced to 41% to 51% of the control after 28 days of optic nerve injury. However, early in the injury there was a significant increase in the staining intensity of BM88. CONCLUSIONS: Nearly all BM88-IR RGCs colocalized with FG-labeled RGCs in control retinas. However, both the number of BM88-IR RGCs and their intensity decreased gradually between 4 and 28 days, preceding the loss of FG-labeled cells. These findings indicate that BM88 is not a good marker of surviving RGCs but may indicate abnormal RGC functioning, which precedes cell death.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".