Utilization of the Rat Tibial Nerve Transection Model to Evaluate Cellular and Molecular Mechanisms Underpinning Denervation-Mediated Muscle Injury
Bibliographic record
Abstract
Peripheral nerve injury denervates muscle, resulting in muscle paralysis and atrophy. This is reversible if timely muscle reinnervation occurs. With delayed reinnervation, the muscle's reparative ability declines, and muscle-resident fibro-adipogenic progenitor cells (FAPs) proliferate and differentiate, inducing fibro-fatty muscle degradation and thereby physical disability. The mechanisms by which the peripheral nerve regulates FAPs expansion and differentiation are incompletely understood. Using the rat tibial neve transection model, we demonstrated an increased FAPs content and a changing FAPs phenotype, with an increased capacity for adipocyte and fibroblast differentiation, in gastrocnemius muscle post-denervation. The FAPs response was inhibited by immediate tibial nerve repair with muscle reinnervation via neuromuscular junctions (NMJs) and sensory organs (e.g., muscle spindles) or the sensory protection of muscle (where a pure sensory nerve is sutured to the distal tibial nerve stump) with reinnervation by muscle spindles alone. We found that both procedures reduced denervation-mediated increases in glial-cell-line-derived neurotrophic factor (GDNF) in muscle and that GDNF promoted FAPs adipogenic and fibrogenic differentiation in vitro. These results suggest that the peripheral nerve controls FAPs recruitment and differentiation via the modulation of muscle GDNF expression through NMJs and muscle spindles. GDNF can serve as a therapeutic target in the management of denervation-induced muscle injury.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".