Evidence of Anti-Proliferative Activities in Blue Mussel (Mytilus edulis) By-Products
Bibliographic record
Abstract
Shellfish waste components contain significant levels of high quality protein and are therefore a potential source for biofunctional high-value peptides. The feasibility of applying a pilot scale enzymatic hydrolysis process to whole Mytilus edulis and, by fractionation, recover hydrolysates presenting a biological activity of interest, was evaluated. Fractions were tested on four immortalized cancerous cell lines: A549, BT549, HCT15 and PC3. The 50 kDa fraction, enriched in peptides, presented anti-proliferative activity with all cell lines and results suggest a bioactive molecule synergy within the fraction. At a protein concentration of 44 µg/mL, the 50 kDa fraction induced a mortality of 90% for PC3, 89% for A549, 85% for HCT15 and of 81% for BT549 cell lines. At the low protein concentration of only 11 µg/mL the 50 kDa fraction still entails a cell mortality of 76% for A549 and 87% for PC3 cell lines. The 50 kDa fraction contains 56% of proteins, 3% of lipids and 6% of minerals on a dry weight basis and the lowest levels detected of taurine and methionine and highest levels of threonine, proline and glycine amino acids. The enzymatic hydrolysis process suggests that Mytilus edulis by-products should be viewed as high-valued products with strong potential as anti-proliferative agent and promising active ingredients in functional foods.
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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".