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Preclinical Evidence on the Anticancer Properties of Food Peptides

2016· review· en· W2507523944 on OpenAlexaff
Subin R. C. K. Rajendran, Chukwunonso E.C.C. Ejike, Min Gong, Chibuike C. Udenigwe

Bibliographic record

VenueProtein and Peptide Letters · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNutraceuticalProteasesCancerCancer cellApoptosisBiologyMechanism of actionChemistryBiochemistryPharmacologyEnzymeIn vitro

Abstract

fetched live from OpenAlex

Natural, synthetic and analogues of peptides have shown prospects for application in cancer chemotherapy. Notably, some food protein-derived peptides are known to possess anticancer activities in cultured cancer cells, and also in animal cancer models via different mechanisms including induction of apoptosis, cell cycle arrest, cellular membrane disruption, inhibition of intracellular signalling, topoisomerases and proteases, and antiangiogenic activity. Although the mechanism of several anticancer food peptides is yet to be clearly elucidated, there is potential for practical applications of the peptides as functional food and nutraceutical ingredients, especially in adjuvant cancer therapy. This review describes the aetiological mechanisms of cancers and the production, structures, mechanisms of action, availability, and cellular and physiological anticancer activities of the food peptides.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.102
GPT teacher head0.321
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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