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Record W3097948366 · doi:10.1159/000510582

Acetylsalicylic Acid Exerts Potent Antitumor and Antiangiogenic Effects in Cutaneous and Uveal Melanoma Cell Lines

2020· article· en· W3097948366 on OpenAlexafffund
Dominique Fausto de Souza, Thupten Tsering, Miguel N. Burnier, Vasco Bravo-Filho, Ana Beatriz Toledo Dias, Mohamed Abdouh, Alicia A. Goyeneche, Julia V. Burnier

Bibliographic record

VenueOcular Oncology and Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsMcGill University
FundersCanadian Dermatology Foundation
KeywordsMedicineMelanomaPharmacologyCancer research

Abstract

fetched live from OpenAlex

INTRODUCTION: Acetylsalicylic acid (ASA) has been investigated for a potential anticancer role in several cancers, such as colorectal, ovarian, and endometrial cancer. Moreover, ASA has been shown to abrogate various processes that contribute to tumor growth and progression. OBJECTIVE: The aim of this study was to evaluate the effects of ASA on cutaneous melanoma (CM) and uveal melanoma (UM). METHODS: Human CM and UM cells were treated with 5 mM ASA and assessed for changes in cellular functions. Antiangiogenic effects of ASA were determined using an ELISA-based assay for 10 proangiogenic cytokines, and then validated by Western blot. Finally, proteomic analysis of ASA-treated cells was performed to elucidate the changes that may be responsible for ASA-mediated effects in melanoma cells. RESULTS: Treatment with ASA significantly inhibited the proliferation, invasion, and migration capabilities, and caused a significant decrease in angiogenin and PIGF secretion in both CM and UM. Mass spectrometry revealed 179 protein changes associated with ASA in the CM and UM cell lines. CONCLUSIONS: These results suggest that ASA may be effective as an adjuvant therapy in metastatic CM and UM. Future studies are needed to determine the regulating targets that are responsible for the antitumor effects of ASA.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.797

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)0.0000.000

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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 designObservational
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

Citations6
Published2020
Admission routes2
Has abstractyes

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