Exploring the potential molecular targets of hydroxymethylbutyrate and glucosamine fortified whey protein drink to modulate sarcopenia and Alzheimer's disease by in silico and in vitro studies
Bibliographic record
Abstract
Fortified foods have garnered significant attention as potential therapeutic strategies with less adverse effects and ready accessibility. However, precise formulation is required to optimize their beneficial effects. Our study aimed to unravel the mechanisms of a functional protein beverage, hydroxymethylbutyrate and glucosamine fortified whey protein drink (HG-WPD), as a possible intervention combatting sarcopenia and Alzheimer's disease (AD) by network pharmacology, molecular docking, and experimental assays. This study investigates the molecular pathways through which HG-WPD acts, by combining in silico and in vitro analyses. Our in silico study predicted the important target proteins, including acetylcholinesterase (AChE), matrix metalloproteinase 9 (MMP9), and angiotensin-converting enzyme (ACE), linked to both diseases. The molecular docking analysis showed that hydroxymethylbutyrate and glucosamine exhibited a notable binding affinity to these proteins, therefore suggesting their possible use as multi-target medicinal agents. In vitro studies on C2C12 myotubes showed that HG-WPD reduced dexamethasone-induced cell death highlighting its potential anti-sarcopenic actions. Furthermore, confirming their possible function in reducing cognitive impairment in AD, the antioxidant tests showed great activity of both substances. The results imply that HG-WPD is potentially effective for future therapeutic approaches as a functional food product with dual benefits in combatting sarcopenia and AD.
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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) | 0.000 | 0.001 |
| 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".