Potential of Fenfuro <sup>®</sup> , a novel, standardized <i>Trigonella foenum-graecum</i> (Fenugreek) seed extract, in ameliorating glycation-mediated amyloidogenesis
Bibliographic record
Abstract
The potential of Trigonella foenum-graecum or fenugreek seed extract in alleviating chronic hyperglycemia is supported by scientific evidence. In addition to its role in optimizing the insulin signaling pathway, fenugreek extract can also prevent the generation of Advanced Glycation End products (AGE) by sequestration of reactive carbonyl groups involved in the formation of Schiff base with lysine and arginine residues of protein side chains. In the present work, a patented and standardized extract of Fenfuro® was found to reduce AGE fluorescence by 75%, along with a corresponding decrease in Thioflavin T fluorescence of nearly 85%. It was determined that these significant spectral changes were due to a combined effect of the protein as well as non-protein part of the extract and not attributable to any single bioactive component. The precipitated protein from the extract itself showed Thio-T fluorescence and gave a single band around the 65 kD MW range. The non-protein supernatant, when incubated with the glycated protein, gave an enhanced AGE as well as Thio-T fluorescence thus negating any possibility of plant extract mediated quenching of fluorescence leading to false interpretations. 8-Anilinonaphthalene-1-sulfonic acid (ANS) fluorescence spectra indicated that the extract provided substantial protection against thermal denaturation.The results collectively provided significant insights for exploring newer avenues of fenugreek-based therapeutics for treating both diabetes and neurodegeneration.
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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".