Cannabinoid type-1 (CB <sub>1</sub> ) receptors in glial cells promote neuromuscular junction repair following nerve injury
Bibliographic record
Abstract
Abstract Cannabinoids are frequently used in the treatment of neuropathic pain related to nerve injury. However, despite evidence for their roles in the regulation of axonal guidance and synapse formation during development of the central nervous system (CNS), their possible involvement in response to peripheral nerve injury remains poorly defined and the knowledge of its role is mostly related to the peripheral sensory system. Following nerve injury, contemporary to axonal repair, massive morphological and functional changes reshape synaptic elements at neuromuscular junctions (NMJs) aiming to promote their reinnervation. This process is mediated in part by Perisynaptic Schwann cells (PSCs), glial cells at the NMJ essential for its maintenance and repair. Here we investigated the novel role of Cannabinoid type-1 receptor (CB 1 R) at NMJ, in particular on PSCs, during motor nerve recovery following nerve injury. Using morphological analysis, we studied the consequences of CB 1 R pharmacological and genetic blockade following denervation and reinnervation in adult NMJs. CB 1 R blockade caused an acceleration of the denervation process followed by a great delay in reinnervation as indicated by a significant percentage of denervated NMJs, accompanied by a decrease of mono- and poly-innervated NMJs. Remarkably, a similar phenomenon was observed when CB 1 R is selectively knocked-out in glia, indicating that the protective actions of these receptors are largely glia-dependent. These data highlight a novel role of the endocannabinoid system at NMJs, where the CB 1 Rs on PSCs can control NMJ denervation and reinnervation following nerve injury. A better understanding of the functional mechanisms underlying CB 1 R role in NMJ repair may contribute to finding a new pharmacological treatment having a dual role in improvements of motor recovery and in pain-related relief.
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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 | 0.001 |
| 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".