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Record W4387772794 · doi:10.1038/s41536-023-00335-x

Repair of osteochondral defects: efficacy of a tissue-engineered hybrid implant containing both human MSC and human iPSC-cartilaginous particles

2023· article· en· W4387772794 on OpenAlexaff
S Nakagawa, Wataru Ando, Kazunori Shimomura, David A. Hart, Hiroto Hanai, George Jacob, Ryota Chijimatsu, Seido Yarimitu, Hiromichi Fujie, Seiji Okada, Noriyuki Tsumaki, Norimasa Nakamura

Bibliographic record

Venuenpj Regenerative Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersJapan Agency for Medical Research and Development
KeywordsMesenchymal stem cellInduced pluripotent stem cellBiomedical engineeringExtracellular matrixStromal cellTissue engineeringNeovascularizationAngiogenesisMaterials scienceMedicineCell biologyCancer researchBiologyEmbryonic stem cell

Abstract

fetched live from OpenAlex

Both mesenchymal stromal cells (MSC) and induced pluripotent stem cells (iPSC) offer the potential for repair of damaged connective tissues. The use of hybrid implants containing both human MSC and iPSC was investigated to assess their combined potential to yield enhanced repair of osteochondral defects. Human iPSC-CP wrapped with tissue engineered constructs (TEC) containing human MSC attained secure defect filling with good integration to adjacent tissue in a rat osteochondral injury model. The presence of living MSC in the hybrid implants was required for effective biphasic osteochondral repair. Thus, the TEC component of such hybrid implants serves several critical functions including, adhesion to the defect site via the matrix and facilitation of the repair via live MSC, as well as enhanced angiogenesis and neovascularization. Based on these encouraging studies, such hybrid implants may offer an effective future intervention for repair of complex osteochondral defects.

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)0.001
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 score0.051
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

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

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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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