Differences in the co-distribution of the Cannabinoid Receptor-1, FAAH, and MAGL in the human and mouse brain could be limiting clinical translation of endocannabinoid outcomes in mouse models of Alzheimer disease
Bibliographic record
Abstract
Abstract Endocannabinoid system (ECS) outcomes in mouse models of Alzheimer disease (AD) do not always align with the clinical disease course. We compared the distribution of the Cannabinoid Receptor-1 (CB1R) and the enzymes, FAAH and MAGL, in autopsy AD brain samples as well as in the ‘J20’ (hAPP Swe/Ind ) mouse model of AD. Both sources revealed several anti-CB1R immunoreactive species, e.g . one at 47-kDa (corresponding to the protein-coding sequence) and a reported putative splice variant at 37 kDa. We did not observe any changes in the mean expression in CB1R, FAAH or MAGL in the human samples, but did observe sex- and genotype-specific changes in the mouse brain. Regression analysis revealed strong sex- and APOE ε4-dependent associations among the CB1Rs as well as between CB1Rs and MAGL (but not FAAH) in human cortical (but not hippocampal) samples. In the J20 mouse, associations between CB1Rs were limited to hippocampal samples, whereas associations between CB1R and both MAGL and FAAH were observed in the cortex. A diagnosis of AD disrupted any associations in the human dataset, whereas in several instances, the associations were enhanced by the hAPP Swe/Ind transgene. This inferred species-dependent regulation of the ECS could impact the clinical translational of ECS outcomes in preclinical models of AD pathology.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".