The Synergistic Effect of Glucagon-Like Peptide-1 and Chamomile Oil on Differentiation of Mesenchymal Stem Cells into Insulin-Producing Cells.
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide-1 (GLP-1) has attracted tremendous attention for treatment of diabetes. Likewise, it seems that active ingredients of chamomile oil might have anti-diabetic effects. This work was conducted to investigate the effects of the combination of GLP-1 and chamomile oil on differentiation of mesenchymal stem cells (MSCs) into functional insulin-producing cells (IPCs). MATERIALS AND METHODS: flower oil every other day for 5 days). Characterization of isolated MSCs was performed using flow cytometry, Alizarin red S staining and Oil red O staining. The expressions of genes specific for IPCs were measured using reverse transcriptase-polymerase chain reaction (RT-PCR) assay. Measurement of insulin and the cleaved connecting peptide (C-peptide) in response to different concentrations of glucose, were performed using ELISA kits. RESULTS: ) as well as insulin and C-peptide secretion in response to different glucose concentrations compared to GLP-1 or chamomile oil alone (P<0.05). CONCLUSION: Collectively, these findings establish a substantial foundation for using peptides in combination with natural products to obtain higher efficiency in regenerative medicine and peptide therapy.
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 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".