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Record W4399236994 · doi:10.1016/j.reth.2024.05.007

A novel scaffold-free mesenchymal stem cell-derived tissue engineered construct for articular cartilage restoration - From basic to clinic

2024· article· en· W4399236994 on OpenAlexafffund
Kazunori Shimomura, Wataru Ando, David A. Hart, Norimasa Nakamura

Bibliographic record

VenueRegenerative Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersMinistry of Health, Labour and WelfareNew Energy and Industrial Technology Development OrganizationJapan Society for the Promotion of ScienceAlberta Innovates - Health Solutions
KeywordsMesenchymal stem cellScaffoldArticular cartilageCartilageStem cellTissue engineeringCell biologyMedicineBiomedical engineeringAnatomyPathologyBiologyOsteoarthritis

Abstract

fetched live from OpenAlex

Treatments for articular cartilage injuries are still challenging, due in part to its avascular and aneural surroundings. Since the first report of autologous chondrocyte implantation, cell-based therapies have been extensively studied with a variety of cell sources, including chondrocytes and mesenchymal stem/stromal cells (MSCs). Recently, MSC-based therapy has received considerable research attention because of the relative ease in handling for tissue harvest, and subsequent cell expansion and differentiation. Using such cells, we have originally developed a 3-dimensional scaffold-free tissue-engineered construct (TEC) through simple-cell culture methods and demonstrated its feasibility for cartilage repair and regeneration in the first-in-human clinical trial. This review summarizes our novel scaffold-free approaches to use MSC for the restoration of damaged articular cartilage, documenting the progression from basic to clinical studies.

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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.290
Teacher spread0.256 · 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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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