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Record W2165757450 · doi:10.1002/jbm.a.32568

Facilitating tissue infiltration and angiogenesis in a tubular collagen scaffold

2009· article· en· W2165757450 on OpenAlexaff
Catherine Gérard, Charles J. Doillon

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2009
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsScaffoldMaterials scienceInfiltration (HVAC)Biomedical engineeringImplantAngiogenesisBone marrowWound healingHeparinProgenitor cellStem cellMedicinePathologyCell biologySurgeryCancer researchBiology

Abstract

fetched live from OpenAlex

Among different strategies to provide blood supply to tissue-engineered devices and implants, the use of arteriovenous loops and bundles has been proposed. The aim of this study was to compare the vascularization and healing processes that took place in a one-end closed tubular collagen-based scaffold at different implantation sites in mice. These conditions were in the presence or absence of heparin and/or bone marrow cells. By 30 days, very few cell infiltrations were observed in the dorsal subcutaneous and peritoneal implants at any conditions; however, the presence of heparin and bone marrow cells improved cell infiltration toward an inflammatory reaction. The insertion of an arteriovenous bundle into the central cavity of the scaffold resulted in partial wound tissue infiltration in the control scaffolds implanted subcutaneously in the hind limb. In similar conditions, the presence of bone marrow cells and heparin resulted in dense wound tissue with numerous capillaries and a significant amount of newly deposited collagen fibers. The design of a central cavity in a porous scaffold with one closed end may facilitate invasion from the central part of the implant toward the implant wall. In addition, the presence of both a vascular component and stem/progenitor cells may lead to a vascularized implant while limiting the inflammatory reaction.

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)0.007
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.365
Teacher spread0.330 · 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

Citations27
Published2009
Admission routes1
Has abstractyes

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