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Record W3024789895 · doi:10.21873/anticanres.12048

Docosahexaenoic Acid Monoglyceride Increases Carboplatin Activity in Lung Cancer Models by Targeting EGFR

2017· article· en· W3024789895 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnticancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsDocosahexaenoic acidCancer researchMAPK/ERK pathwayIn vivoCell growthChemistryLung cancerCarboplatinA549 cellPharmacologyKinaseBiologyPolyunsaturated fatty acidMedicineBiochemistryIn vitroFatty acidInternal medicineChemotherapyCisplatin

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: Omega 3 polyunsaturated fatty acids (PUFAs) have been shown to inhibit the induction and progression of many tumor types. The aim of this study was to determine the anticancer effect of docosahexaenoic acid monoglyceride (MAG-DHA) alone and in combination with the chemotherapeutic agent carboplatin (CBT) on lung cancer models. MATERIALS AND METHODS: Adenocarcinoma cell lines A549 and H1299 were used to evaluate the effect of combined MAG-DHA and CBT treatments both in vitro and in vivo in xenograft models. RESULTS: MAG-DHA+CBT treatment decreased cell proliferation and invasion abilities of A549 and H1299 cells. Furthermore, MAG-DHA+CBT treatment resulted in a decreased activation of epithelial growth factor receptor (EGFR) and its downstream extracellular signal-regulated kinase (ERK) in cell lysates. In A549 and H1299 xenograft mouse models, MAG-DHA+CBT treatment reduced tumor growth. CONCLUSION: Combined MAG-DHA and CBT treatment inhibited tumor growth by suppressing EGFR and ERK signaling pathways in lung carcinoma cells.

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.

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)0.001
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 score0.164
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.042
GPT teacher head0.390
Teacher spread0.348 · 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