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Record W2789926774 · doi:10.3390/ijms19030723

Chlorogenic Acid and Its Microbial Metabolites Exert Anti-Proliferative Effects, S-Phase Cell-Cycle Arrest and Apoptosis in Human Colon Cancer Caco-2 Cells

2018· article· en· W2789926774 on OpenAlexaff
Shima Sadeghi Ekbatan, Xiu‐Qing Li, Mohammad Ghorbani, Behnam Azadi, Stan Kubow

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsApoptosisChlorogenic acidCell growthCytotoxicityCell cycleCell cycle checkpointChemistryCaffeic acidCancer cellCaco-2BiochemistryLactate dehydrogenaseCellCancerBiologyIn vitroEnzymeFood scienceAntioxidant

Abstract

fetched live from OpenAlex

Chlorogenic acid (CGA) decreases colon cancer-cell proliferation but the combined anti-cancer effects of CGA with its major colonic microbial metabolites, caffeic acid (CA), 3-phenylpropionic acid (3-PPA) and benzoic acid (BA), needs elucidation as they occur together in colonic digesta. Caco-2 cancer cells were treated for 24 h with the four compounds individually (50–1000 µM) and as an equimolar ratio (1:1:1:1; MIX). The effective concentration to decrease cell proliferation by 50% (EC50) was lower for MIX (431 ± 51.84 µM) and CA (460 ± 21.88) versus CGA (758 ± 19.09 µM). The EC50 for cytotoxicity measured by lactate dehydrogenase release in MIX (527 ± 75.34 µM) showed more potency than CA (740 ± 38.68 µM). Cell proliferation was decreased by 3-PPA and BA at 1000 µM with no cytotoxicity. Cell-cycle arrest was induced at the S-phase by CA (100 µM), MIX (100 µM), CGA (250 µM) and 3-PPA (500 µM) with activation of caspase-3 by CGA, CA, MIX (500 and 1000 µM). Mitochondrial DNA content was reduced by 3-PPA (1000 µM). The anti-cancer effects occurred at markedly lower concentrations of each compound within MIX than when provided singly, indicating that they function together to enhance anti-colon cancer activities.

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 categoriesnone
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 score0.004
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.315
Teacher spread0.304 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations136
Published2018
Admission routes1
Has abstractyes

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