Anatomical location of injected microglia in different activation states and time course of injury determines survival of retinal ganglion cells after optic nerve crush
Bibliographic record
Abstract
Background: Activated microglia release harmful substances to retinal ganglion cells (RGCs), but may also benefit by removing cellular debris and secreting neurotrophic factors. These paradoxical roles remain controversial because the nature and time-course of the injury that defines their role is unknown. The aim of this study was to determine if pharmacological manipulation of microglia to acquire a pro-inflammatory or pro-survival phenotype will exacerbate or enhance neuronal survival after injury.Material and methods: Treated HAP I (highly aggressively proliferating immortalized) microglia were injected into the vitreous or tail vein (T V) of female Sprague-Dawley rats. Retinas were examined at 4-14 days following optic nerve crush (ONC) and the number of surviving RGCs was determined.Results: Injection of untreated HAP I cells resulted in the greater loss of RGCs early after ONC when injected into the vitreous and later after ONC when injected into the T V. LP S activated HAP I cells injected into the vitreous resulted in greater RGC loss with and without injury. When injected into the T V with ONC there was no loss of RGCs 4 days after ONC but greater loss afterwards. Minocycline treated HAP I cells injected into the vitreous resulted in greater RGC survival than untreated HAP I cells. However, when injected into the T V with ONC there was greater loss of RGCs. These results suggest that optic nerve signals attract extrinsic microglia to the retina, resulting in a proinflammatory response.Conclusion: Neuroprotection or cytotoxicity of microglia depends on the type of activation, time course of the injury, and if they act on the axon or cell body.
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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) | 0.001 | 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".