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Record W4402126863 · doi:10.1016/j.matbio.2024.08.010

The integral role of fibronectin in skeletal morphogenesis and pathogenesis

2024· review· en· W4402126863 on OpenAlexafffund
Neha E. H. Dinesh, Philippe M. Campeau, Dieter P. Reinhardt

Bibliographic record

VenueMatrix Biology · 2024
Typereview
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsMcGill UniversityCentre Hospitalier Universitaire Sainte-JustineMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchFonds de recherche du QuébecRéseau de Recherche en Santé Buccodentaire et Osseuse
KeywordsFibronectinExtracellular matrixBiologyCell biologyContext (archaeology)Mesenchymal stem cellGene isoformMorphogenesisLamininChondrogenesisGeneticsGene

Abstract

fetched live from OpenAlex

Fibronectin (FN) serves as a critical organizer of extracellular matrix networks in two principal isoforms, the plasma FN and the cellular FN. While FN's pivotal role in various organ systems, including the blood vasculature, is well-established, its contribution to the development of the skeletal system is much less explored. Furthermore, the pathomechanisms of spondyloepiphyseal dysplasia caused by FN mutations remain elusive. In this minireview, we discuss findings from our recent two studies using i) an iPSC-based cell culture model to explore how FN mutations in spondyloepiphyseal dysplasia impact mesenchymal cell differentiation into chondrocytes and ii) conditional FN knockout mouse models to determine the physiological roles of FN isoforms during postnatal skeletal development. The data revealed that FN mutations cause severe intracellular and matrix defects in mesenchymal cells and impair their ability to differentiate into chondrocytes. The findings further demonstrate the important roles of both FN isoforms in orchestrating regulated chondrogenesis during skeletal development. We critically discuss the findings in the context of the existing literature.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.384
Teacher spread0.356 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes2
Has abstractyes

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