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Record W4310699919 · doi:10.1038/s41597-022-01837-x

Mechanical Unloading of Engineered Human Meniscus Models Under Simulated Microgravity: A Transcriptomic Study

2022· article· en· W4310699919 on OpenAlexafffund
Zhiyao Ma, David Li, Ryan K. W. Chee, Melanie Kunze, Aillette Mulet‐Sierra, Mark Sommerfeldt, Lindsey Westover, Daniel Graf, Adetola B. Adesida

Bibliographic record

VenueScientific Data · 2022
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of British ColumbiaCanada Foundation for InnovationGovernment of CanadaCanadian Institutes of Health ResearchAlberta Cancer FoundationAlberta Innovates - Health SolutionsWomen and Children's Health Research Institute
KeywordsMeniscusTranscriptomeComputer scienceComputational biologyBiologyPhysicsOpticsGene expression

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) primarily affects mechanical load-bearing joints, with the knee being the most common. The prevalence, burden and severity of knee osteoarthritis (KOA) are disproportionately higher in females, but hormonal differences alone do not explain the disproportionate incidence of KOA in females. Mechanical unloading by spaceflight microgravity has been implicated in OA development in cartilaginous tissues. However, the mechanisms and sex-dependent differences in OA-like development are not well explored. In this study, engineered meniscus constructs were generated from healthy human meniscus fibrochondrocytes (MFC) seeded onto type I collagen scaffolds and cultured under normal gravity and simulated microgravity conditions. We report the whole-genome sequences of constructs from 4 female and 4 male donors, along with the evaluation of their phenotypic characteristics. The collected data could be used as valuable resources to further explore the mechanism of KOA development in response to mechanical unloading, and to investigate the molecular basis of the observed sex differences in KOA.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.104
GPT teacher head0.348
Teacher spread0.244 · 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

Citations28
Published2022
Admission routes2
Has abstractyes

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