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Carotenoids and Carcinogenesis: Exploring the Antioxidant and Cell Signaling Roles of Carotenoids in the Prevention of Cancer

2022· review· en· W4301354872 on OpenAlexaff
Nikolina Vrdoljak

Bibliographic record

VenueCritical Reviews™ in Oncogenesis · 2022
Typereview
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCarotenoidZeaxanthinLuteinLycopeneCarcinogenesisAntioxidantCancerCancer preventionFucoxanthinBiochemistryBiologyChemistryFood scienceGenetics

Abstract

fetched live from OpenAlex

Carotenoids are lipid soluble pigments found in various fruits and vegetables and are naturally produced in photoautotrophic plants. Various studies have investigated the properties of carotenoids to determine how they are able to mitigate numerous diseases, including cancer. Carotenoids present in human serum, including β-carotene, α-carotene, lycopene, β-cryptoxanthin, zeaxanthin, and lutein have demonstrated the ability to act as anticarcinogenic agents. Prevention of disease is often described to be more effective than treatment; as cancer impacts millions of lives globally, the role of carotenoids in the prevention of oncogenesis for numerous types of cancers have been extensively researched. This review provides an in-depth analysis of the structure and properties of carotenoids, as well as the identified and potential mechanisms by which carotenoids can act as a chemopreventative agent.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.161
GPT teacher head0.401
Teacher spread0.240 · 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 teacher head, not a consensus.

Study designOther design
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

Citations14
Published2022
Admission routes1
Has abstractyes

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