Bioactive peptides with potential anticancer properties from various food protein sources: status of recent research, production technologies, and developments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, bioactive peptides, from natural resources, have attracted remarkable attention as nutraceutical treasures and the health benefits of their consumption have extensively been studied. Therapies based on bioactive peptides have been recognized as an innovative and promising alternative method for dangerous diseases such as cancer. Indeed, there has been enormous interest in nutraceuticals and bioactive-based chemopreventive molecules as a potential opportunity to manage chronic diseases, including cancer at different stages, rather than the traditionally used therapies. The relative safety and efficacy of these peptides in targeting only the tumor cells without affecting the normal cells make them attractive alternatives to existing pharmaceuticals for the treatment, management, and prevention of cancer, being able to act as potential physiological modulators of metabolism during their intestinal digestion. Novel bioactive peptides derived from food sources can be beneficial as anticancer nutraceuticals and provide a basis for the pharmaceutical development of food-derived bioactive peptides. Bioactive peptides can be generated through different protein hydrolysis methods and purified using advanced chromatographic techniques. Moreover, establishing bioactive peptides' efficacy and mechanism of action can provide alternative methods for cancer prevention and management. Most of the research on anticancer peptides is carried out on cell lines with very limited research being investigated in animal models or human clinical models. In this context, this review article comprehensively discusses anticancer peptides': production, isolation, therapeutic strategies, mechanism of action, and application in cancer therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it