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Record W2909128434 · doi:10.1002/jbmr.3668

Mechanical Loading Promotes the Expansion of Primitive Osteoprogenitors and Organizes Matrix and Vascular Morphology in Long Bone Defects

2019· article· en· W2909128434 on OpenAlexaff
Chao Liu, Pamela Cabahug‐Zuckerman, Christopher J. Stubbs, Martin Péndola, Cinyee Cai, Kenneth A. Mann, Alesha B. Castillo

Bibliographic record

VenueJournal of Bone and Mineral Research · 2019
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsYork University
FundersNational Center for Advancing Translational SciencesNational Cancer InstituteNational Institute of Dental and Craniofacial ResearchU.S. Department of Veterans Affairs
KeywordsBone healingIntramembranous ossificationExtracellular matrixMatrix (chemical analysis)PopulationAnatomyChemistryCell biologyBiomedical engineeringMaterials scienceBiologyMedicine

Abstract

fetched live from OpenAlex

Elucidating the effects of mechanical stimulation on bone repair is crucial for optimization of the healing process. Specifically, the regulatory role that mechanical loading exerts on the osteogenic stem cell pool and vascular morphology during healing is incompletely understood. Because dynamic loading has been shown to enhance osteogenesis and repair, we hypothesized that loading induces the expansion of the osteoprogenitor cell population within a healing bone defect, leading to an increased presence of osteogenic cells. We further hypothesized that loading during the repair process regulates vascular and collagen matrix morphology and spatial interactions between vessels and osteogenic cells. To address these hypotheses, we used a mechanobiological bone repair model, which produces a consistent and reproducible intramembranous repair response confined in time and space. Bilateral tibial defects were created in adult C57BL/6 mice, which were subjected to axial compressive dynamic loading either during the early cellular invasion phase on postsurgical days (PSDs) 2 to 5 or during the matrix deposition phase on PSD 5 to 8. Confocal and two-photon microscopy was used to generate high-resolution three-dimensional (3D) renderings of longitudinal thick sections of the defect on PSD 10. Endomucin (EMCN)-positive vessels, Paired related homeobox 1 (Prrx1+) stem cell antigen-1 positive (Sca-1+) primitive osteoprogenitors (OPCs), and osterix positive (Osx+) preosteoblasts were visualized and quantified using deep tissue immunohistochemistry. New bone matrix was visualized with second harmonic generation autofluorescence of collagen fibers. We found that mechanical loading during the matrix deposition phase (PSD 5 to 8) increased vessel volume and number, and aligned vessels and collagen fibers to the load-bearing direction of bone. Furthermore, loading led to a significant increase in the proliferation and number of Prrx1+ Sca-1+ primitive OPCs, but not Osx+ preosteoblasts within the defect. Together, these data illustrate the adaptation of both collagen matrix and vascular morphology to better withstand mechanical load during bone repair, and that the mechanoresponsive cell population consists of the primitive osteogenic progenitors. © 2019 American Society for Bone and Mineral Research.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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 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".

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Citations59
Published2019
Admission routes1
Has abstractyes

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