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Record W2788404221 · doi:10.1242/dev.153460

Precise spatial restriction of BMP signaling in developing joints is perturbed upon loss of embryo movement

2018· article· en· W2788404221 on OpenAlexfundno aff
Pratik Singh, Claire A. Shea, Shashank Kumar Sonker, Rebecca A. Rolfe, Ayan T. Ray, Sandeep Kumar, Pankaj Gupta, Paula Murphy, Amitabha Bandyopadhyay

Bibliographic record

VenueDevelopment · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsnot available
FundersKyoto UniversityMinistry of Education, IndiaIrish Research CouncilInstitute of GeneticsDepartment of Science and Technology, Ministry of Science and Technology, IndiaCouncil of Scientific and Industrial Research, IndiaEuropean Molecular Biology Organization
KeywordsBiologyEmbryoCell biologyMovement (music)GeneticsComputational biology

Abstract

fetched live from OpenAlex

Dynamic mechanical loading of synovial joints is necessary for normal joint development, as evidenced in certain clinical conditions, congenital disorders and animal models where dynamic muscle contractions are reduced or absent. Although the importance of mechanical forces on joint development is unequivocal, little is known about the molecular mechanisms involved. Here, using chick and mouse embryos, we observed that molecular changes in expression of multiple genes analyzed in the absence of mechanical stimulation are consistent across species. Our results suggest that abnormal joint development in immobilized embryos involves inappropriate regulation of Wnt and BMP signaling during definition of the emerging joint territories, i.e. reduced β-catenin activation and concomitant upregulation of pSMAD1/5/8 signaling. Moreover, dynamic mechanical loading of the developing knee joint activates Smurf1 expression; our data suggest that Smurf1 insulates the joint region from pSMAD1/5/8 signaling and is essential for maintenance of joint progenitor cell fate.

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 score0.040
Threshold uncertainty score0.631

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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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

Citations39
Published2018
Admission routes1
Has abstractyes

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