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Record W2151346849 · doi:10.1002/jemt.10263

Angiogenesis in normal and neoplastic pituitary tissues

2003· review· en· W2151346849 on OpenAlex

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.

Bibliographic record

VenueMicroscopy Research and Technique · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsSt. Michael's Hospital
FundersNational Cancer Institute
KeywordsAngiogenesisAngiostatinBiologyCancer researchVascular endothelial growth factorMatrix metalloproteinaseThrombospondinsThrombospondin 1Fibroblast growth factorNeovascularizationPituitary tumorsInternal medicineEndocrinologyReceptorThrombospondinMedicineVEGF receptorsMetalloproteinaseGenetics

Abstract

fetched live from OpenAlex

Angiogenesis, or the formation of new blood vessels, is a dynamic process needed for embryogenesis, post-natal growth, morphogenesis, tumorigenesis, and for other biological processes. Angiogenesis is very important for tumor development and progression. This review examines the activators and inhibitors of angiogenesis with emphasis on the pituitary gland and pituitary neoplasms. Some of the proteins regulating angiogenesis in pituitary tumors such as vascular endothelial growth factor (VEGF) and VEGF receptors, fibroblasts growth factors (FGF), transforming growth factor beta (TGFB), interleukins, interferons, and matrix metalloproteinases (MMPs) and inhibitors of MMPs have been examined in animal and human pituitary tumor models. However, many other significant regulators of angiogenesis including angiopoietins, angiostatin, and thrombospondins have not been studied extensively in pituitary tumors to date. Newer concepts and developments in angiogenesis such as vasculogenic mimicry and gene therapy approaches to angiogenesis in cancer treatment are also discussed.

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)0.001
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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.051
GPT teacher head0.408
Teacher spread0.357 · 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