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Record W2746991909 · doi:10.1139/bcb-2017-0083

Hydrogen peroxide regulates angiogenesis-related factors in tumor cells

2017· article· en· W2746991909 on OpenAlexvenueno aff
Ana Jerónimo, Gonçalo Rodrigues, Filipe Vilas‐Boas, Gabriel G. Martins, Ana Bagulho, Carla Real

Bibliographic record

VenueBiochemistry and Cell Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsAngiogenesisCatalaseNeovascularizationZebrafishCell biologyIntracellularHydrogen peroxideBiologyCell growthCancer researchChemistryOxidative stressBiochemistryGene

Abstract

fetched live from OpenAlex

Tumor angiogenesis is required for tumor development and growth, and is regulated by several factors including ROS. H2O2 is a ROS with an important role in cell signaling, but how H2O2 regulates tumor angiogenesis is still poorly understood. We have xenografted tumor cells with altered levels of H2O2 by catalase overexpression into zebrafish embryos to study redox-induced tumor neovascularization. We found that vascular recruitment and invasion were impaired if catalase was overexpressed. In addition, the overexpression of catalase altered the transcriptional levels of several angiogenesis-related factors in tumor cells, including TIMP-3 and THBS1. These two anti-angiogenic factors were found to be H2O2-regulated by two different mechanisms: TIMP-3 expression in a cell-autonomous manner; and, THBS1 expression that was non-cell-autonomous. Our work shows that intracellular H2O2 regulates the expression of angiogenic factors and the formation of a vessel network. Understanding the molecular mechanisms that govern this multifunctional effect of H2O2 on tumor angiogenesis could be important for the development of more efficient anti-angiogenic therapies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2017
Admission routes1
Has abstractyes

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