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Record W2328466936 · doi:10.4172/2157-2518.s2-001

The Glycoprotein Growth Factor Progranulin Promotes Carcinogenesis and has Potential Value in Anti-cancer Therapy

2012· article· en· W2328466936 on OpenAlexafffund
Yonghua Zhang

Bibliographic record

VenueJournal of Carcinogenesis & Mutagenesis · 2012
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsRoyal Victoria Regional Health CentreMcGill UniversityRoyal Victoria Hospital
FundersCanadian Cancer Society Research Institute
KeywordsCancer researchCarcinogenesisCancerCell growthOvarian cancerMedicineKinaseBiologyBiomarkerInternal medicineCell biology

Abstract

fetched live from OpenAlex

Progranulin (PGRN) is a secreted glycoprotein growth factor with tumorigenic roles in a variety of tumors including, among others, breast, ovarian, prostate, bladder, and liver cancer.In some patients, for example with breast, ovarian or liver cancers, high PGRN expression in tumors correlated with a worse outcome.Studies using cell lines and animal models provide evidence that PGRN promotes tumor cell proliferation, migration and survival, and induces drug resistance.Increasing or decreasing PGRN production enhances or inhibits respectively the growth of PGRN-sensitive tumors in vivo.PGRN activity is associated with p44/42 mitogen-activated protein kinase as well as phosphatidylinositol 3-kinases signaling pathways.In addition, PGRN may stimulate the formation of the tumor stroma.As an extracellular regulator of tumorgenesis, PGRN is a potential therapeutic target and biomarker of prognosis in the treatment of various cancers.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.301
Teacher spread0.254 · 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

Citations14
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Carcinogenesis & MutagenesisSame topicAmyotrophic Lateral Sclerosis ResearchFrench-language works237,207