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Record W2072394256 · doi:10.1002/bies.200900086

The granulin gene family: from cancer to dementia

2009· review· en· W2072394256 on OpenAlexafffund
Andrew Bateman, H.P.J. Bennett

Bibliographic record

VenueBioEssays · 2009
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
FundersCanadian Institutes of Health Research
KeywordsBiologyFrontotemporal dementiaDementiaGeneCancerDiseaseNeuroscienceGeneticsImmunologyPathologyMedicine

Abstract

fetched live from OpenAlex

The growth factor progranulin (PGRN) regulates cell division, survival, and migration. PGRN is an extracellular glycoprotein bearing multiple copies of the cysteine-rich granulin motif. With PGRN family members in plants and slime mold, it represents one of the most ancient of the extracellular regulatory proteins still extant in modern animals. PRGN has multiple biological roles. It contributes to the regulation of early embryogenesis, to adult tissue repair and inflammation. Elevated PGRN levels often occur in cancers, and PGRN immunotherapy inhibits the growth of hepatic cancer xenografts in mice. Recent studies have demonstrated roles for PGRN in neurobiology. An autosomal dominant mutation in GRN, the gene for PGRN, leads to neuronal atrophy in the frontal and temporal lobes, resulting in the disease frontotemporal lobar dementia. In this review we will discuss current knowledge of the multifaceted biology of PGRN.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.122
GPT teacher head0.413
Teacher spread0.291 · 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

Citations345
Published2009
Admission routes2
Has abstractyes

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