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Record W3046572973 · doi:10.1038/s41467-020-17545-2

VEGFR2 signaling drives meningeal vascular regeneration upon head injury

2020· article· en· W3046572973 on OpenAlexfundno aff
Bong Ihn Koh, Hyuek Jong Lee, Pil Ae Kwak, Myung Jin Yang, Juhee Kim, Hyung-Seok Kim, Gou Young Koh, Injune Kim

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaInstitute for Basic ScienceNational Research Foundation of KoreaUniversity of TorontoKeio UniversityNational Research FoundationUniversity College LondonKorea Brain Research Institute
KeywordsMeningesRegeneration (biology)ParenchymaMedicineStromal cellPathologyAngiogenesisWound healingAngiopoietin receptorBiologyCell biologyCancer researchImmunology

Abstract

fetched live from OpenAlex

Upon severe head injury (HI), blood vessels of the meninges and brain parenchyma are inevitably damaged. While limited vascular regeneration of the injured brain has been studied extensively, our understanding of meningeal vascular regeneration following head injury is quite limited. Here, we identify key pathways governing meningeal vascular regeneration following HI. Rapid and complete vascular regeneration in the meninges is predominantly driven by VEGFR2 signaling. Substantial increase of VEGFR2 is observed in both human patients and mouse models of HI, and endothelial cell-specific deletion of Vegfr2 in the latter inhibits meningeal vascular regeneration. We further identify the facilitating, stabilizing and arresting roles of Tie2, PDGFRβ and Dll4 signaling, respectively, in meningeal vascular regeneration. Prolonged inhibition of this angiogenic process following HI compromises immunological and stromal integrity of the injured meninges. These findings establish a molecular framework for meningeal vascular regeneration after HI, and may guide development of wound healing therapeutics.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.084
GPT teacher head0.420
Teacher spread0.336 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations34
Published2020
Admission routes1
Has abstractyes

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