The Impact of Major and Minor Phytocannabinoids on the Maintenance and Function of INS-1 β-Cells Under High-Glucose and High-Lipid Conditions
Bibliographic record
Abstract
Type 2 diabetes mellites (T2DM) is the most common form of diabetes and affects a significant portion of the population. Obesity-related increases in free fatty acids and glucose in the diet contribute to β-cell dysfunction and loss, ultimately leading to the onset of T2DM. The endocannabinoid system, which is present throughout the body, plays a vital role in regulating various physiological processes, including those in the pancreas. This system has been implicated in metabolic disorders like obesity and diabetes, as it helps to regulate appetite, food intake, and fat production. Phytocannabinoids from Cannabis sativa have the potential to influence the endocannabinoid system, offering a promising therapeutic approach for diabetes and its complications. Using high-glucose–high-lipid (HGHL)-induced INS-1 β-cells, we investigated the protective effects of two major (THC and CBD) and three minor (THCV, CBC, and CBG) phytocannabinoids on high glucose–high lipid (HGHL)-induced apoptosis, cell cycle disruption, and impaired function of beta-cells. Our results showed that all five phytocannabinoids reduced HGHL-induced apoptosis, likely by decreasing TXNIP protein levels. Additionally, THC and all three minor phytocannabinoids provided protective effects against functional impairments caused by HGHL exposure.
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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".