Regeneration of neuromuscular synapses after acute and chronic denervation by inhibiting the gerozyme 15-prostaglandin dehydrogenase
Bibliographic record
Abstract
To date, there are no approved treatments for the diminished strength and paralysis that result from the loss of peripheral nerve function due to trauma, heritable neuromuscular diseases, or aging. Here, we showed that denervation resulting from transection of the sciatic nerve triggered a marked increase in the prostaglandin-degrading enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH) in skeletal muscle in mice, providing evidence that injury drives early expression of this aging-associated enzyme or gerozyme. Treating mice with a small-molecule inhibitor of 15-PGDH promoted regeneration of motor axons and formation of neuromuscular synapses leading to an acceleration in recovery of force after an acute nerve crush injury. In aged mice with chronic denervation of muscles, treatment with the 15-PGDH inhibitor increased motor neuron viability and restored neuromuscular junctions and function. These presynaptic changes synergized with previously reported muscle tissue remodeling to result in a marked increase in the strength of aged muscles. We further found that 15-PGDH aggregates defined the target fibers that are histopathologic hallmarks of human neurogenic myopathies, suggesting that the gerozyme may be involved in their etiology. Our data suggest that inhibition of 15-PGDH may constitute a therapeutic strategy to physiologically boost prostaglandin E2, restore neuromuscular connectivity, and promote recovery of strength after acute or chronic denervation due to injury, disease, or aging.
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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".