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Record W3217025502 · doi:10.1126/sciadv.abg9509

Low-flow intussusception and metastable VEGFR2 signaling launch angiogenesis in ischemic muscle

2021· article· en· W3217025502 on OpenAlexaff
John‐Michael Arpino, Hao Yin, Emma Prescott, Sabrina C. R. Staples, Zengxuan Nong, Fuyan Li, Jacqueline Chevalier, Brittany Balint, Caroline O’Neil, Rokhsana Mortuza, Stephanie Milkovich, Jason J. Lee, Daniel Lorusso, Martin Sandig, Douglas W. Hamilton, David W. Holdsworth, Tamie L. Poepping, Christopher G. Ellis, J. Geoffrey Pickering

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsAngiogenesisIntussusception (medical disorder)MetastabilityVEGF receptorsMedicineCardiologyCell biologyInternal medicineBiologyChemistrySurgery

Abstract

fetched live from OpenAlex

Efforts to promote sprouting angiogenesis in skeletal muscles of individuals with peripheral artery disease have not been clinically successful. We discovered that, contrary to the prevailing view, angiogenesis following ischemic muscle injury in mice was not driven by endothelial sprouting. Instead, real-time imaging revealed the emergence of wide-caliber, primordial conduits with ultralow flow that rapidly transformed into a hierarchical neocirculation by transluminal bridging and intussusception. This process was accelerated by inhibiting vascular endothelial growth factor receptor-2 (VEGFR2). We probed this response by developing the first live-cell model of transluminal endothelial bridging using microfluidics. Endothelial cells subjected to ultralow shear stress could reposition inside the flowing lumen as pillars. Moreover, the low-flow lumen proved to be a privileged location for endothelial cells with reduced VEGFR2 signaling capacity, as VEGFR2 mechanosignals were boosted. These findings redefine regenerative angiogenesis in muscle as an intussusceptive process and uncover a basis for its launch.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.279
Teacher spread0.266 · 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 designBench or experimental
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

Citations22
Published2021
Admission routes1
Has abstractyes

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