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Record W2743611078 · doi:10.18494/sam.2016.1348

Role of Bioreactors in Regeneration of Articular Cartilage

2016· article· en· W2743611078 on OpenAlexaff
Farhana Yasmin, Daniel Chen

Bibliographic record

VenueSensors and Materials · 2016
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRegeneration (biology)Articular cartilageBioreactorCartilageChemistryOsteoarthritisAnatomyBiomedical engineeringCell biologyMedicineBiologyPathology

Abstract

fetched live from OpenAlex

Interest in cartilage defect repair has been rapidly increasing with the growing number of sportsrelated injuries, traumas, congenital defects, and pathological disorders, for example, resulting in osteoarthritis (OA), a complex degenerative joint disease that affects a large number of people every year. Although modern medicine has entered into an advanced stage, current surgical procedures still have several limitations. At present, among all therapeutic approaches, tissue engineering has shown great potential for cartilage joint repair. Several methodologies of utilizing cells, scaffolds, and signalling molecules have been explored and developed. However, in regenerative cartilage therapies, one of the major challenges yet to be overcome is its inability to regenerate functional tissue owing to poor mass transfer, and limited ability in controlling physiocochemical cultural parameters during in vitro cell culture. To solve these problems, bioreactors can play a promising role because of their ability to control physiocochemical culture parameters and to provide mechanical stimulation, thereby inducing improved chondrogenesis of tissue-engineered cartilage. In this paper, we review the role of bioreactors in repairing articular cartilage defects with recent advancements in these two areas.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.119

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.218
Teacher spread0.210 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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