Cannabidiol Protects Striatal Neurons by Attenuating Endoplasmic Reticulum Stress
Bibliographic record
Abstract
Introduction: The aggregation of misfolded proteins in the endoplasmic reticulum (ER) is a pathological trait shared by many neurodegenerative disorders. This aggregation leads to the persistent activation of the unfolded protein response (UPR) and ultimately apoptosis as a result of ER stress. Cannabidiol (CBD) has been demonstrated to be neuroprotective in various cellular and animal models of neurodegeneration, which has been attributed to its antioxidant and anti-inflammatory properties. However, little is known about the role of CBD in the context of protein folding and ER stress. The purpose of this study was to investigate whether CBD is neuroprotective against an in vitro model of ER stress. Materials and Methods: Using different exposure models, mouse striatal ST Hdh Q7/Q7 cells were exposed to either the ER stress inducer thapsigargin (TG) and/or CBD. Cell viabilities assays were used to investigate the effect of CBD pre-treatment, co-treatment, and post-treatment on TG-induced cell death. Real-time quantitative polymerase chain reaction was used to measure changes in ER stress regulators and UPR genes such as glucose-regulated protein-78 (GRP78), mesencephalic astrocyte-derived neurotrophic factor (MANF), B cell lymphoma 2 (BCL-2), BCL-2 interacting mediator of cell death (BIM), and caspase-12. Results: Cell viability increased significantly when cells were pre-treated with CBD before TG exposure. An increase in the gene expression of pro-survival ER chaperone GRP78 and ER-resident neurotrophic factor MANF coincided with this effect and decreased ER-mediated pro-apoptotic markers such as BIM , and caspase-12 was observed. Conclusions: These data suggest that CBD pre-treatment is neuroprotective against TG-induced cell death. Understanding the role of ER stress in CBD-driven neuroprotection provides insight into the therapeutic potential of CBD and the role of ER dysfunction in neurodegenerative disorders.
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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".