Nogo-A-targeting antibody promotes visual recovery and inhibits neuroinflammation after retinal injury
Bibliographic record
Abstract
N-Methyl-D-aspartate (NMDA)-induced neuronal cell death is involved in a large spectrum of diseases affecting the brain and the retina such as Alzheimer's disease and diabetic retinopathy. Associated neurological impairments may result from the inhibition of neuronal plasticity by Nogo-A. The objective of the current study was to determine the contribution of Nogo-A to NMDA excitotoxicity in the mouse retina. We observed that Nogo-A is upregulated in the mouse vitreous during NMDA-induced inflammation. Intraocular injection of a function-blocking antibody specific to Nogo-A (11C7) was carried out 2 days after NMDA-induced injury. This treatment significantly enhanced visual function recovery in injured animals. Strikingly, the expression of potent pro-inflammatory molecules was downregulated by 11C7, among which TNFα was the most durably decreased cytokine in microglia/macrophages. Additional analyses suggest that TNFα downregulation may stem from cofilin inactivation in microglia/macrophages. 11C7 also limited gliosis presumably via P.Stat3 downregulation. Diabetic retinopathy was associated with increased levels of Nogo-A in the eyes of donors. In summary, our results reveal that Nogo-A-targeting antibody can stimulate visual recovery after retinal injury and that Nogo-A is a potent modulator of excitotoxicity-induced neuroinflammation. These data may be used to design treatments against inflammatory eye diseases.
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 | 0.000 |
| 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) | 0.001 | 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".