Delta-9-tetrahydrocannabinol inhibits invasion of HTR8/SVneo human extravillous trophoblast cells and negatively impacts mitochondrial function
Bibliographic record
Abstract
Prenatal cannabis use is a significant problem and poses important health risks for the developing fetus. The molecular mechanisms underlying these changes are not fully elucidated but are thought to be attributed to delta-9-tetrahydrocannabinol (THC), the main bioactive constituent of cannabis. It has been reported that THC may target the mitochondria in several tissue types, including placental tissue and trophoblast cell lines, and alter their function. In the present study, in response to 48-h THC treatment of the human extravillous trophoblast cell line HTR8/SVneo, we demonstrate that cell proliferation and invasion are significantly reduced. We further demonstrate THC-treatment elevated levels of cellular reactive oxygen species and markers of lipid damage. This was accompanied by evidence of increased mitochondrial fission. We also observed increased expression of cellular stress markers, HSP70 and HSP60, following exposure to THC. These effects were coincident with reduced mitochondrial respiratory function and a decrease in mitochondrial membrane potential. Taken together, our results suggest that THC can induce mitochondrial dysfunction and reduce trophoblast invasion; outcomes that have been previously linked to poor placentation. We also demonstrate that these changes in HTR8/SVneo biology may be variably mediated by cannabinoid receptors CB1 and CB2.
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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) | 0.002 | 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".