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Record W2749610770 · doi:10.1161/atvbaha.117.309993

Tortuous Microvessels Contribute to Wound Healing via Sprouting Angiogenesis

2017· article· en· W2749610770 on OpenAlexaff
Diana C. Chong, Zhixian Yu, Hailey E. Brighton, James E. Bear, Victoria L. Bautch

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2017
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsWestern University
FundersNational Heart, Lung, and Blood Institute
KeywordsWound healingMicrocirculationAngiogenesisNeovascularizationIntravital microscopySproutingSprouting angiogenesisPathologyBlood vesselBlood flowCell biologyAnatomyMedicineBiologyImmunologyCancer researchInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Wound healing is accompanied by neoangiogenesis, and new vessels are thought to originate primarily from the microcirculation; however, how these vessels form and resolve during wound healing is poorly understood. Here, we investigated properties of the smallest capillaries during wound healing to determine their spatial organization and the kinetics of formation and resolution. APPROACH AND RESULTS: We used intravital imaging and high-resolution microscopy to identify a new type of vessel in wounds, called tortuous microvessels. Longitudinal studies showed that tortuous microvessels increased in frequency after injury, normalized as the wound healed, and were closely associated with the wound site. Tortuous microvessels had aberrant cell shapes, increased permeability, and distinct interactions with circulating microspheres, suggesting altered flow dynamics. Moreover, tortuous microvessels disproportionately contributed to wound angiogenesis by sprouting exuberantly and significantly more frequently than nearby normal capillaries. CONCLUSIONS: A new type of transient wound vessel, tortuous microvessels, sprout dynamically and disproportionately contribute to wound-healing neoangiogenesis, likely as a result of altered properties downstream of flow disturbances. These new findings suggest entry points for therapeutic intervention.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.053
GPT teacher head0.337
Teacher spread0.285 · 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 designObservational
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

Citations53
Published2017
Admission routes1
Has abstractyes

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