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Record W2030592653 · doi:10.1517/14728222.2013.812074

CCN2: a novel, specific and valid target for anti-fibrotic drug intervention

2013· review· en· W2030592653 on OpenAlex
Andrew Leask

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueExpert Opinion on Therapeutic Targets · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsMatricellular proteinCTGFFibrosisGrowth factorConnective tissueCancer researchBiologyMedicineCell biologyImmunologyExtracellular matrixPathologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

INTRODUCTION: Prior attempts at developing anti-fibrotic therapies have focused on using growth factors and cytokines as targets. However, growth factors and cytokines have effects on normal physiology as well as fibrosis, making effective drug development difficult. AREAS COVERED: Matricellular proteins alter the cellular microenvironment and hence cellular signaling responses to cytokines and growth factors. A survey of Pubmed reveals that the expression pattern of matricellular proteins notably that of CCN2 (connective tissue growth factor) is often altered in pathophysiological conditions such as fibrosis. Moreover, data presented in recent publications suggests that CCN2 directly mediates fibrosis. EXPERT OPINION: As a result of these features, matricellular proteins such as CCN2, a member of the CCN family of matricellular proteins, might be ideal targets against which to develop novel therapeutic strategies.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
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 head0.106
GPT teacher head0.403
Teacher spread0.297 · 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