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Record W162320534 · doi:10.1007/978-1-60761-938-3_1

Morphology and Properties of Brain Endothelial Cells

2010· review· en· W162320534 on OpenAlexaff
Sukriti Nag

Bibliographic record

VenueMethods in molecular biology · 2010
Typereview
Languageen
FieldNeuroscience
TopicBarrier Structure and Function Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCaveolaeVascular permeabilityCell biologyEndothelial stem cellEndotheliumPermeability (electromagnetism)Tight junctionMatrix metalloproteinaseVascular endothelial growth factor BBlood–brain barrierBiologyChemistryNeurosciencePathologyMedicineVascular endothelial growth factorCancer researchVascular endothelial growth factor ASignal transductionBiochemistryIn vitroCentral nervous systemVEGF receptorsEndocrinologyMembrane

Abstract

fetched live from OpenAlex

The molecular advances in various aspects of brain endothelial cell function in steady states are considerable and difficult to summarize in one chapter. Therefore, this chapter focuses on endothelial permeability mechanisms in steady states and disease namely vasogenic edema. The morphology and properties of caveolae and tight junctions that are involved in endothelial permeability to macromolecules are reviewed. Endothelial transport functions are briefly reviewed. Diseases with alterations of endothelial permeability are mentioned and details are provided of the molecular alterations in caveolae and tight junctions in vasogenic edema. Other factors involved in increased endothelial permeability such as the matrix metalloproteinases are briefly discussed. Of the modulators of endothelial permeability, angioneurins such as the vascular endothelial growth factors and angiopoietins are discussed. The chapter concludes with a brief discussion on delivery of therapeutic substances across endothelium.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.087
GPT teacher head0.411
Teacher spread0.325 · 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

Citations44
Published2010
Admission routes1
Has abstractyes

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