Phytocannabinoids promote viability and functional adipogenesis of bone marrow-derived mesenchymal stem cells through different molecular targets
Bibliographic record
Abstract
The cellular microenvironment plays a critical role in the maintenance of bone marrow-derived mesenchymal stem cells (BM-MSCs) and their subsequent cell lineage differentiation. Recent studies suggested that individuals with adipocyte-related metabolic disorders have altered function and adipogenic potential of adipose stem cell subpopulations, primarily BM-MSCs, increasing the risk of heart attack, stroke or diabetes. In this study, we explored the potential therapeutic effect of some of the most abundant non-euphoric compounds derived from the Cannabis sativa plant (or phytocannabinoids) including tetrahydrocannabivarin (THCV), cannabidiol (CBD), cannabigerol (CBG), cannabidiolic acid (CBDA) and cannabigerolic acid (CBGA), by analysing their pharmacological activity on viability of endogenous BM-MSCs as well as their ability to alter BM-MSC proliferation and differentiation into mature adipocytes. We provide evidence that CBD, CBDA, CBGA and THCV (5 µM) increase the number of viable BM-MSCs; whereas only CBG (5 µM) and CBD (5 µM) alone or in combination promote BM-MSCs maturation into adipocytes via distinct molecular mechanisms. These effects were revealed both in vitro and in vivo. In addition, phytocannabinoids prevented the insulin signalling impairment induced by palmitate in adipocytes differentiated from BM-MSCs. Our study highlights phytocannabinoids as a potential novel pharmacological tool to regain control of functional adipose tissue in unregulated energy homeostasis often occurring in metabolic disorders including type 2 diabetes mellitus (T2DM), aging and lipodystrophy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".