Evaluation of In vitro Antidiabetic Potential of Thymus schimperi R. and Thymus vulgaris L.
Bibliographic record
Abstract
Diabetes has become the most common metabolic disease worldwide.In particular, type 2 diabetes is the most commonly encountered type of diabetes, which is characterized by impaired insulin secretion and/or action.One of the effective methods to control diabetes is to inhibit the activity of α-amylase and α-glucosidase enzymes which are responsible for the breakdown of starch to more simple sugars, using plant products.This study evaluated the total phenolic (TPC), total flavonoid (TFC), and antidiabetic potential of Thymus schimperi and Thymus vulgaris via in vitro inhibition of α-amylase and α-glucosidase, using the hot water and aqueous: methanol (20:80, v/v) extracts.The α-amylase inhibitory potentials of the extracts were investigated through reducing sugars analysis using 3,5-dinitrosalicylic acid color reagent (DNSA) using starch solution as substrate.The α-glucosidase inhibition was determined by pre-incubating α-glucosidase with different concentrations of the extracts followed by the addition of p-nitrophenylglucopyranoside (pNPG).Aqueous: methanol (20:80, v/v) extract of T. schimperi contained highest TPC (46.01 ± 4.54 mg GAE/g dw) and TFC (14.72 ± 1.14 mg QE/g dw) also showed stronger α-amylase inhibition activity (IC50 = 0.33 ± 0.05 mg/mL) and the hot water extract exhibited stronger α-glucosidase inhibition (IC50 = 0.05 ± 0.01 mg/mL) capacity than that of T. vulgaris.The TPC and TFC were positively related (p < 0.05) with α-amylase inhibition activity but negatively correlated (p > 0.05) with α-glucosidase inhibitory activity.These results indicated that the inhibition of these enzymes can lead to lower postprandial blood glucose.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".