Translational approach to establish the cardiometabolic health effects and mechanisms of action of fish nutrients–it takes a village
Bibliographic record
Abstract
People use dietary supplements to offset nutritional deficiencies and manage metabolic dysfunction. While the beneficial effect of fish proteins on glucose homeostasis is well established, the ability of fish peptides to replicate the protein findings is less clear. With financial support from a programmatic Canadian Institutes of Health Research (CIHR) Team grant, we aimed to identify salmon peptide fractions (SPFs) with the potential to mitigate metabolic dysfunction. Additionally, the grant aims included assessing whether vitamin D, a nutrient commonly found in salmon, could potentiate the beneficial effects of salmon peptides. In parallel, technologies were developed to separate and filter the isolated peptides. We employed an integrative approach that combined nutritional interventions in animal models and human subjects to identify metabolic pathways regulated by salmon peptides and other fish nutrients. This combination of interdisciplinary expertise revealed that a SPF could be a therapeutic tool used in the prevention and management of cardiometabolic diseases. Herein, we present a perspective of our CIHR funded grant that utilized a translational approach to establish the cardiometabolic health effects and mechanisms of action of fish nutrients: from animal models to clinical trials.
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.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".