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Record W2910486038 · doi:10.1002/mus.26415

Ascorbic acid diminishes bone morphogenetic protein 2‐induced osteogenic differentiation of muscle precursor cells

2019· article· en· W2910486038 on OpenAlexaff
Paige C. Ferrazzo, Sarah Niccoli, Neelam Khaper, Christopher R. Rathbone, Simon J. Lees

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsNOSM UniversityLakehead University
Fundersnot available
KeywordsAscorbic acidAlkaline phosphataseBone morphogenetic protein 2Superoxide dismutaseOxidative stressChemistryCatalaseSkeletal muscleEndocrinologyInternal medicineBone morphogenetic proteinCell biologyBiochemistryBiologyMedicineEnzyme

Abstract

fetched live from OpenAlex

INTRODUCTION: Muscle precursor cells (MPC) are integral to the maintenance of skeletal muscle and have recently been implicated in playing a role in bone repair. The primary objective of this study was to understand better the role of oxidative stress during the osteogenic differentiation of MPCs. METHODS: Muscle precursor cells were treated with various combinations of ascorbic acid (AA), bone morphogenetic protein (BMP)-2, and either a superoxide dismutase analog (4-hydroxy-TEMPO [TEMPOL]) or polyethyleneglycol-conjugated catalase. Muscle precursor cell proliferation and differentiation were determined, and alkaline phosphatase activity was measured as an index of osteogenic differentiation. RESULTS: After treatment with 200 μM AA, superoxide was increased 1.5-fold, whereas AA in combination with 100 ng/ml BMP-2 did not increase alkaline phosphatase (ALP) activity. When cells were treated with TEMPOL in combination with 100 ng/ml BMP-2 and 200 μM AA, ALP activity significantly increased. DISCUSSION: These data suggest that increasing oxidative stress with AA induces sublethal oxidative stress that prevents BMP-2-induced osteogenic differentiation of MPCs. Muscle Nerve 59:501-508, 2019.

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 categoriesMeta-epidemiology (narrow)
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 score0.081
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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.

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

Citations7
Published2019
Admission routes1
Has abstractyes

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