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Record W3189243995 · doi:10.3390/life11080791

Skeletal Effects of Bone-Targeted TGFbeta Inhibition in a Mouse Model of Duchenne Muscular Dystrophy

2021· article· en· W3189243995 on OpenAlexaff
Juliana Marulanda, Iris Boraschi-Diaz, Pierre Beauparlant, Philippe Crine, Frank Rauch

Bibliographic record

VenueLife · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill UniversityShriners Hospitals for Children - Canada
FundersShriners Hospitals for Children
KeywordsDuchenne muscular dystrophyMedicineContext (archaeology)OsteoporosisMuscular dystrophyInternal medicineEndocrinologyFemurSkeletal muscleBiologySurgery

Abstract

fetched live from OpenAlex

Duchenne muscular dystrophy (DMD) is a severe progressive muscle disease that is frequently associated with secondary osteoporosis. Previous studies have shown that TGFbeta inactivating antibody improves the muscle phenotype in mdx mice, a model of DMD. In the present study, we assessed the skeletal effects of treatment with a bone-targeted TGFbeta antibody (PCT-011) in mdx mice. Micro-computed tomography showed that 8 weeks of intraperitoneal administration of PCT-011 (10 mg per kg body mass, 3 times per week) was associated with more than twofold higher trabecular bone volume at the distal femur, which was explained by a higher trabecular number. At the femoral midshaft, PCT-011 exposure increased cortical thickness but did not significantly affect the results of three-point bending tests. Histomorphometric analyses of the lumbar vertebra 4 showed that PCT-011 treatment led to a lower bone formation rate. In conclusion, treatment with the TGFbeta antibody PCT-011 had a positive effect on bone development in mdx mice. Inhibiting TGFbeta activity thus appears to be a promising approach to treat bone fragility in the context of DMD.

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 score0.386

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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