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Record W4400190406 · doi:10.1101/cshperspect.a041422

The Blood–Brain Barrier: Composition, Properties, and Roles in Brain Health

2024· review· en· W4400190406 on OpenAlexaff
Baptiste Lacoste, Alexandre Prat, Moises Freitas‐Andrade, Chenghua Gu

Bibliographic record

VenueCold Spring Harbor Perspectives in Biology · 2024
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversité de MontréalOttawa HospitalUniversity of Ottawa
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsBiologyBlood–brain barrierComposition (language)NeuroscienceComputational biologyCognitive scienceCentral nervous systemPsychology

Abstract

fetched live from OpenAlex

Blood vessels are critical to deliver oxygen and nutrients to tissues and organs throughout the body. The blood vessels that vascularize the central nervous system (CNS) possess unique properties, termed the blood-brain barrier (BBB), which allow these vessels to tightly regulate the movement of ions, molecules, and cells between the blood and the brain. This precise control of CNS homeostasis allows for proper neuronal function and protects the neural tissue from toxins and pathogens, and alterations of this barrier are important components of the pathogenesis and progression of various neurological diseases. The physiological barrier is coordinated by a series of physical, transport, and metabolic properties possessed by the brain endothelial cells (ECs) that form the walls of the blood vessels. These properties are regulated by interactions between different vascular, perivascular, immune, and neural cells. Understanding how these cell populations interact to regulate barrier properties is essential for understanding how the brain functions in both health and disease contexts.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.004

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 head0.044
GPT teacher head0.349
Teacher spread0.305 · 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

Citations59
Published2024
Admission routes1
Has abstractyes

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