Identification of novel senolytic compounds from natural food sources
Bibliographic record
Abstract
Abstract More than 23% of today’s population suffers from age-associated diseases such as arthritis, cancer, heart disease, and more. The ongoing economic impact of these diseases has been in the billions of dollars worldwide with no clear solution to date. This study addresses the underlying cause of these diseases by identifying the compounds that potentially eliminate senescent cells. Existing senolytic drugs are not abundantly found in nature, reducing accessibility. Hence, over 70,000 natural compounds available in the Canadian Food Database were used to screen penitential senolytic compounds that block PI3Kγ, reactivating apoptotic processes in senescent cells. Molecular docking results revealed 23 natural compounds that blocks the PI3Kγ. Out of 23 compounds, Cianidanol, Ellagic acid, Eriodictyol, Kaempferol and Cyanidin were found abundantly in food sources range from 85 to 735 mg/100 g. These compounds are up to 46 times more abundant in foods than proven senolytic drug Fisetin. Further, molecular dynamics results showed ligand stability for 4 nanoseconds with PI3Kγ. The five compounds are proven to eliminate cancerous cells, have the potential to prevent age-related diseases, and could even slow down natural aging.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".