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Extracellular HSP90 in Cancer Invasion and Metastasis: From Translational Research to Clinical Prospects

2015· article· en· W2223566416 on OpenAlexvenueno aff
Dimitra Thomaidou, Evangelia Patsavoudi

Bibliographic record

VenueJournal of Analytical Oncology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsnot available
Fundersnot available
KeywordsMetastasisEffectorExtracellularCancerHsp90Chaperone (clinical)Cancer therapyTranslational researchBiologyMedicineCancer researchImmunologyPathologyInternal medicineCell biologyHeat shock protein

Abstract

fetched live from OpenAlex

During the last decade, the extracellular molecular chaperone HSP90 (eHSP90) has been identified as a critical effector in cancer cell invasion and metastasis by virtue of its interaction with a diverse cohort of molecules that serve as key nodal points in oncogenic pathways. Thus eHSP90 has most recently emerged as a novel target in cancer therapeutics, subsequently becoming the focus of several drug development efforts. This review highlights recent studies on the mechanisms through which eHSP90 exhibits its tumor cell invasion action. It also presents latest efforts to translate this cumulative knowledge into clinical practice to disable eHSP90-driven metastasis.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.244
GPT teacher head0.508
Teacher spread0.264 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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