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Record W1973364635 · doi:10.1002/jor.22028

Mesenchymal stem cell transplantation to promote bone healing

2012· article· en· W1973364635 on OpenAlexafffund
Chan Gao, Jan Seuntjens, Gabriel Kaufman, Nicolas Tran‐Khanh, Alison Butler, Ailian Li, Huifen Wang, Michael D. Buschmann, Edward J. Harvey, Janet E. Henderson

Bibliographic record

VenueJournal of Orthopaedic Research® · 2012
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsBritish Columbia Institute of TechnologyPolytechnique MontréalWSP (Canada)McGill University Health Centre
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsOsseointegrationMesenchymal stem cellMedicineFemurBone healingTransplantationOsteopeniaStromal cellImplantDentistryOsteoporosisSurgeryPathologyBone mineral

Abstract

fetched live from OpenAlex

An overall decline in the availability of osteogenic precursor cells and growth factors in the bone marrow microenvironment have been associated with impaired bone formation and osteopenia in humans. The objective of the current study was to determine if transplantation of mesenchymal stromal cells (MSC) from a healthy, young donor mouse into an osteopenic recipient mouse could enhance osseointegration of a femoral implant. MSC harvested from normal young adult mice differentiated into bone forming osteoblasts when cultured on implant grade titanium surfaces ex vivo and promoted bone formation around titanium-coated rods implanted in the femoral canal of osteopenic recipient mice. Micro computed tomographic imaging and histological analyses showed more, better quality, bone in the femur that received the MSC transplant compared with the contra-lateral control femur that received carrier alone. These results provide pre-clinical evidence that MSC transplantation promotes peri-implant bone regeneration and suggest the approach could be used in a clinical setting to enhance bone regeneration and healing in patients with poor quality bone.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.360
Teacher spread0.296 · 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 designOther design
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

Citations43
Published2012
Admission routes2
Has abstractyes

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