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Record W3157584900 · doi:10.7150/thno.58254

A 9-kDa matricellular SPARC fragment released by cathepsin D exhibits pro-tumor activity in the triple-negative breast cancer microenvironment

2021· article· en· W3157584900 on OpenAlexaff
Lindsay B. Alcaraz, Aude Mallavialle, Timothée David, Danielle Derocq, Frédéric Delolme, Cindy Dieryckx, Caroline Mollévi, Florence Boissière‐Michot, Joëlle Simony-Lafontaine, Stanislas du Manoir, Pitter F. Huesgen, Christopher M. Overall, Sophie Tartare‐Deckert, William Jacot, Thierry Chardès, Séverine Guiu, Pascal Roger, Thomas Reinheckel, Catherine Moali, Emmanuelle Liaudet‐Coopman

Bibliographic record

VenueTheranostics · 2021
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersAgence Nationale de la Recherche
KeywordsTriple-negative breast cancerMatricellular proteinCathepsin DCancer researchCancerMedicineBreast cancerChemistryInternal medicineBiologyCell biologyExtracellular matrixBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Our study establishes a novel crosstalk between proteases and matricellular proteins in the tumor microenvironment through limited SPARC proteolysis, revealing a novel targetable 9-kDa bioactive SPARC fragment for new TNBC treatments. Our study will pave the way for the development of strategies for targeting bioactive fragments from matricellular proteins in TNBC.

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 score0.003

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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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

Citations57
Published2021
Admission routes1
Has abstractyes

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