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Record W2139810515 · doi:10.1186/bcr1980

Inflammation and breast cancer. Metalloproteinases as common effectors of inflammation and extracellular matrix breakdown in breast cancer

2008· review· en· W2139810515 on OpenAlexafffund
Carlo Hojilla, Geoffrey A. Wood, Rama Khokha

Bibliographic record

VenueBreast Cancer Research · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsUniversity of GuelphOntario Institute for Cancer Research
FundersNational Cancer InstituteCanadian Breast Cancer Research AllianceSusan G. Komen
KeywordsBreast cancerInflammationContext (archaeology)Matrix metalloproteinaseCancerCarcinogenesisCancer researchExtracellular matrixMedicineImmunologyBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Two rapidly evolving fields are converging to impact breast cancer: one has identified novel substrates of metalloproteinases that alter immune cell function, and the other has revealed a role for inflammation in human cancers. Evidence now shows that the mechanisms underlying these two fields interact in the context of breast cancer, providing new opportunities to understand this disease and uncover novel therapeutic strategies. The metalloproteinase class of enzymes is well studied in mammary gland development and physiology, but mostly in the context of extracellular matrix modification. Aberrant metalloproteinase expression has also been implicated in breast cancer progression, where these genes act as tumor modifiers. Here, we review how the metalloproteinase axis impacts mammary physiology and tumorigenesis and is associated with inflammatory cell influx in human breast cancer, and evaluate its potential as a regulator of inflammation in the mammary gland.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.028
GPT teacher head0.367
Teacher spread0.340 · 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 designOther design
Domainnot available
GenreReview

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

Citations70
Published2008
Admission routes2
Has abstractyes

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