Effect of Delta-9-tetrahydrocannabinol and cannabidiol on milk proteins and lipid levels in HC11 cells
Bibliographic record
Abstract
Pregnant and lactating women have been discouraged from using cannabis by Health Canada. However, the increasing rate of cannabis use among pregnant women has presented an urgent need to investigate its physiological effects during the perinatal period. During pregnancy, the mammary gland (MG) undergoes remodeling, which involves alveolar differentiation of mammary epithelial cells (MECs), which is essential for breast milk production and secretion. Limited evidence has been reported on the impact of cannabis or its components, delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), on MG development or MEC differentiation. In this study, we investigated the effects of THC and CBD on the differentiation of MECs by assessing changes in cellular viability, lipid accumulation, and gene and protein expression of major milk protein and lipid synthesizing markers. using the HC11 cells as a model. We hypothesized that THC and CBD will negatively impact the synthesis of milk proteins and lipids, as well as lipid markers in HC11 cells. Our results demonstrated that THC and CBD reduced cellular viability at concentrations above 30μM and 20μM, respectively. Relative to control, 10μM THC and 10μM CBD reduced mRNA levels of milk proteins (CSN2 and WAP), lipid synthesizing and glucose transport markers (GLUT 1, HK2, FASN, FABP4, PLIN2 and LPL), as well as whey acidic protein and lipid levels. In addition, co-treatment of a CB2 antagonist with THC, and a CB2 agonist with CBD, reversed the impact of THC and CBD on the mRNA levels of key markers, respectively. In conclusion, 10μM THC and CBD altered the differentiation of HC11 cells, in part via the CB2 receptor.
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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".