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Record W2022589074 · doi:10.4161/cbt.25935

Novel antioxidants are not toxic to normal tissues but effectively kill cancer cells

2013· article· en· W2022589074 on OpenAlexafffund
Anna Kovalchuk, Felix Aladedunye, Rocio Rodriguez‐Juarez, Dongping Li, James Thomas, Olga Kovalchuk, Roman Przybylski

Bibliographic record

VenueCancer Biology & Therapy · 2013
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsUniversity of Lethbridge
FundersCanadian Institutes of Health ResearchAlberta Cancer Foundation
KeywordsCancerCancer cellCancer researchGeneAntioxidantGene expressionBreast cancerBiologyChemistryPharmacologyBiochemistryGenetics

Abstract

fetched live from OpenAlex

Free radicals are formed as a result of cellular processes and play a key role in predisposition to and development of numerous diseases and of premature aging. Recently, we reported the syntheses of a number of novel phenolic antioxidants for possible application in food industry. In the present study, analyses of the cellular processes and molecular gene expression effects of some of the novel antioxidants in normal human tissues and in cancer cells were undertaken. Results indicated that whereas the examined antioxidants showed no effects on morphology and gene expression of normal human oral and gingival epithelial tissues, they exerted a profound cell killing effect on breast cancer cells, including on chemotherapy-resistant breast cancer cells and on oral squamous carcinoma cells. Among the tested antioxidants, N-decyl-N-(3-methoxy-4-hydroxybenzyl)-3-(3,4-dihydroxyphenyl) propanamide and N-decyl-N-(3,5-dimethoxy-4-hydroxybenzyl)-3-(3,4-dihydroxyphenyl) propanamide were the most promising, with excellent potential for cancer treatment. Moreover, our gene expression databases can be used as a roadmap for future analysis of mechanisms of antioxidant action.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations10
Published2013
Admission routes2
Has abstractyes

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