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Record W2618957446 · doi:10.1093/neuonc/nox058

Blood-brain barrier pericyte importance in malignant gliomas: what we can learn from stroke and Alzheimer’s disease

2017· review· en· W2618957446 on OpenAlexaff
Sadhana Jackson, Ayman ElAli, Daniela Virgintino, Mark R. Gilbert

Bibliographic record

VenueNeuro-Oncology · 2017
Typereview
Languageen
FieldNeuroscience
TopicBarrier Structure and Function Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPericyteGliomaNeuroscienceDiseaseBiologyCell typeBlood–brain barrierInflammationCentral nervous systemPathologyCellMedicineEndothelial stem cellCancer researchImmunologyIn vitro

Abstract

fetched live from OpenAlex

The pericyte, a constitutive component of the central nervous system, is a poorly understood cell type that envelops the endothelial cell with the intended purpose of regulating vascular flow and endothelial cell permeability. Previous studies of pericyte function have been limited to a small number of disease processes such as ischemic stroke and Alzheimer's disease. Recently, publications have postulated a link between glioma stem cell differentiation and pericyte function. These studies suggest that there may be an important interaction of pericytes with tumor cells and other components of the tumor microenvironment in malignant primary glial neoplasms, most notably glioblastoma. This potential cellular interaction underscores the need to pursue more investigations of pericytes in malignant brain tumor biology. In this review, we summarize the functional roles of pericytes, particularly focusing on changes in pericyte biology during response to immune cells, inflammation, and hypoxic conditions. The information presented is based on the available data from studies of pericyte function in other central nervous system diseases but will serve as a foundation for research investigations to further understand the role of pericytes in malignant gliomas.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.101
GPT teacher head0.356
Teacher spread0.255 · 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
Published2017
Admission routes1
Has abstractyes

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