Deletion of the cannabinoid CB<sub>1</sub> receptor impacts on the ultrastructure of the cerebellar parallel fiber‐Purkinje cell synapses
Bibliographic record
Abstract
Abstract The cannabinoid CB1 receptor localizes to the glutamatergic parallel fiber (PF) terminals of the cerebellar granule cells and participates in synaptic plasticity, motor control and learning that are impaired in CB1 receptor knockout (CB 1‐KO) mice. However, whether ultrastructural changes at the PF‐Purkinje cell (PC) synapses occur in CB 1‐KO remains unknown. We studied this in the vermis of the spinocerebellar lobule V and the vestibulocerebellar lobule X of CB 1‐KO and wild‐type (CB 1‐WT) mice by electron microscopy. Lobule V, but not lobule X, of CB 1‐KO had significantly less and longer synapses than in CB 1‐WT. PF terminals were significantly larger in both lobules of CB 1‐KO with no changes in PC dendritic spines. The PF terminals in lobule V of CB 1‐KO contained less synaptic vesicles and lower vesicle density; by contrast, vesicle density in lobule X of CB 1‐KO remained unchangeable relative to CB 1‐WT. There were as many vesicles in lobule V of CB 1‐KO as in CB 1‐WT, but their distribution decreased drastically at 300 nm of the active zone. In lobule X of CB 1‐KO, less vesicles were found within 150 nm from the presynaptic membrane; however, no vesicles were at 450–600 nm of the active zone. A significant higher amount of synaptic vesicles close to the active zone in lobule V and X of CB 1‐KO was observed. In conclusion, the absence of CB1 receptors strikingly and distinctively impacts on the ultrastructural architecture of the PF‐PC synapses located in cerebellar lobules that differ in vulnerability to damage and motor functions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".