Delayed manipulation of regeneration within injured peripheral axons
Bibliographic record
Abstract
While several new translational strategies to enhance regrowth of peripheral axons have been identified, combined approaches with different targets are rare. Moreover, few have been studied after a significant delay when growth programs are already well established and regeneration-related protein expression has waned. Here we study two approaches, Rb1 (Retinoblastoma 1) knockdown that targets overall neuron plasticity, and near nerve insulin acting as a growth factor. Both are validated to boost regrowth only at the outset of regeneration. We show that local delivery of Rb1 siRNA alone, with electroporation to an area of prior sciatic nerve injury generated knockdown of Rb1 mRNA in ipsilateral lumbar dorsal root ganglia. While mice treated with Rb1-targeted siRNA, compared with scrambled control siRNA, starting 2 weeks after the onset of regeneration, had only limited behavioural or electrophysiological benefits, they had enhanced reinnervation of epidermal axons. We next confirmed that intrinsic Rb1 knockdown combined with exogenous insulin had dramatic synergistic impacts on the growth patterns of adult sensory neurons studied in vitro, prompting analysis of a combined approach in vivo. Using an identical delayed post-injury protocol, we noted that added insulin not only augmented epidermal reinnervation rendered by Rb1 knockdown alone but also improved indices of mechanical sensation and motor axon recovery. The findings illustrate that peripheral neurons that are well into attempted regrowth retain their responsiveness to both intrinsic and exogenous approaches that improve their recovery. We also identify a novel local approach to manipulate gene expression and outcome in regrowing axons.
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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".