An Agonistic TrkB mAb Causes Sustained TrkB Activation, Delays RGC Death, and Protects the Retinal Structure in Optic Nerve Axotomy and in Glaucoma
Bibliographic record
Abstract
PURPOSE: Brain-derived neurotrophic factor (BDNF) receptors TrkB and p75(NTR) are expressed in the retina. However, exogenous BDNF does not provide retinal ganglion cells (RGCs) with long-lasting neuroprotection in vivo during optic nerve axotomy or in glaucoma rat models of neurodegeneration. The authors set out to answer the hypothesis that a selective TrkB agonist might afford more efficient neuroprotection. METHODS: Animal models of acute neurodegeneration (complete optic nerve axotomy) and chronic neurodegeneration (ocular hypertension, glaucoma) were used. After intravitreal delivery of test agents or controls, surviving RGCs were quantified. Transient or sustained activation of TrkB receptors in vivo was quantified by Western blot analysis retinal samples for TrkB-phosphotyrosine. Time-dependent changes to the neuronal retinal layers were quantified longitudinally by Fourier domain-optical coherence tomography. RESULTS: The authors show that a selective TrkB agonist caused long-lived TrkB activation and significantly delayed RGC death in these models of acute and chronic retinal injury in vivo. Importantly, using noninvasive retinal imaging, they also show that a selective TrkB agonist caused preservation of the retinal structure in both animal models, with maintenance of the layers comprising neurons and neuronal fibers. CONCLUSIONS: In animal models of acute and chronic neurodegeneration, a TrkB agonist affords long-lasting neuroprotection by causing sustained TrkB activation. The use of structural end points could have prognostic value to evaluate neuroprotection. This work contributes to the understanding of neurotrophic mechanisms underlying RGC death in glaucoma and optic nerve axotomy.
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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) | 0.001 | 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".