Cannabidiol Disrupts Mitochondrial Respiration and Metabolism and Dysregulates Trophoblast Cell Differentiation
Bibliographic record
Abstract
Trophoblast differentiation is a crucial process in the formation of the placenta where cytotrophoblasts (CTs) differentiate and fuse to form the syncytiotrophoblast (ST). The bioactive components of cannabis, such as Δ9-THC, are known to disrupt trophoblast differentiation and fusion, as well as mitochondrial dynamics and respiration. However, less is known about the impact of cannabidiol (CBD) on trophoblast differentiation. Due to the central role of mitochondria in stem cell differentiation, we evaluated the impact of CBD on trophoblast mitochondrial function and differentiation. Using BeWo b30 cells, we observed decreased levels of mRNA for markers of syncytialization (GCM1, ERVW1, hCG) following 20 µM CBD treatment during differentiation. In CTs, CBD elevated transcript levels for the mitochondrial and cellular stress markers HSP60 and HSP70, respectively. Furthermore, CBD treatment also increased the lipid peroxidation and oxidative damage marker 4-hydroxynonenal. Mitochondrial membrane potential, basal respiration and ATP production were diminished with the 20 µM CBD treatment in both sub-lineages. mRNA levels for endocannabinoid system (ECS) components (FAAH, NAPEPLD, TRPV1, CB1, CB2, PPARγ) were altered differentially by CBD in CTs and STs. Overall, we demonstrate that CBD impairs trophoblast differentiation and fusion, as well as mitochondrial bioenergetics and redox homeostasis.
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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".