Schwann cells modified to secrete MANF is a potential cellular therapy for peripheral nerve regeneration
Bibliographic record
Abstract
Effective therapies for peripheral nerve repair are still lacking despite active research in this field over the past years. The limited knowledge of biomolecules that equally promote axon regeneration and glial cell dynamics, which are critical for nerve regeneration, poses a major challenge in developing effective therapies. Here, we showed that the neurotrophic factor mesencephalic astrocyte-derived neurotrophic factor (MANF) equally promotes axon regeneration and glial cell dynamics favorable for nerve regeneration. Using adult rodent models, we showed that the endogenous expression of MANF is restricted to non-peptidergic sensory neurons. However, supplementation of exogenous MANF promoted the growth of all subtypes of adult sensory neurons. We also demonstrated that exogenous MANF promotes the proliferation and migration of adult primary Schwann Cells (SCs). Furthermore, we showed that local and repeated administration of MANF to injured nerves promotes axon regeneration in mice models. Finally, we devised a therapeutic approach by programming nerve-resident SCs to locally and continuously deliver MANF to injured nerves and showed that this approach improves axon regeneration. Overall, this work developed a therapeutic approach by harnessing the power of SCs as a local delivery system for MANF for nerve 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) | 0.000 | 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".