Receptor-ligand supplementation via a self-cleaving 2A peptide–based gene therapy promotes CNS axonal transport with functional recovery
Bibliographic record
Abstract
Gene replacement approaches are leading to a revolution in the treatment of previously debilitating monogenic neurological conditions. However, the application of gene therapy to complex polygenic conditions has been limited. Down-regulation or dysfunction of receptor expression in the disease state or in the presence of excess ligand has been shown to compromise therapeutic efficacy. Here, we offer evidence that combined overexpression of both brain-derived neurotrophic factor and its receptor, tropomyosin receptor kinase B, is more effective in stimulating axonal transport than either receptor administration or ligand administration alone. We also show efficacy in experimental glaucoma and humanized tauopathy models. Simultaneous administration of a ligand and its receptor by a single gene therapy vector overcomes several problems relating to ligand deficiency and receptor down-regulation that may be relevant to multiple neurodegenerative diseases. This approach shows promise as a strategy to target intrinsic mechanisms to improve neuronal function and facilitate repair.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".