Evaluation of inhibitory effect of alpha-amylase and alpha-glucosidase by interaction phenolic compounds, soluble fiber, and protein extracted from green lentils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lentil consumption has been constantly growing due to its nutritional composition and functional properties. Lentil seeds are rich in several bioactive compounds with an effect on decreasing the symptoms of diabetes, cardiovascular disease, and aging. In this study, the effects of acetone extract (GLA extract), soluble fiber (GLSF), and protein (PGL) extracted from green lentils (concentration of 50 mg/ml) on anti-diabetic properties were investigated by measuring the inhibitory activity of alphaamylase and alpha-glucosidase. There was no significant between the inhibitory activity of alpha-amylase activity by GLA extract and PGL (p<0.05). Also GLA extract had the greatest effect on inhibition of glucosidase activity (67.08%). Fluorescence quenching had studied the changes in the tertiary structure of alpha-amylase and alpha-glucosidase using different concentrations (0, 0.25, 0.50, 1.00, 2.00, 4.00 mg/mL) of GLA extract, GLSF, and PGL. The results showed that all three compounds extracted from green lentils play as a natural source to inhibit the activity of alpha-amylase and alpha-glucosidase enzymes and be used in the production of functional foods.
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.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it