MétaCan
Menu
Back to cohort
Record W1992166679 · doi:10.4161/sgtp.28119

Modulation of osteoclast differentiation and bone resorption by Rho GTPases

2014· review· en· W1992166679 on OpenAlexfundno aff
Heiani Touaitahuata, Anne Blangy, Virginie Vives

Bibliographic record

VenueSmall GTPases · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsnot available
FundersInstitute of Cancer ResearchInstitut National Du Cancer
KeywordsOsteoclastBone resorptionCell biologyPodosomeBiologyMultinucleateResorptionActin cytoskeletonCytoskeletonBone remodeling periodCellEndocrinologyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Bone is a dynamic tissue constantly renewed through a regulated balance between bone formation and resorption. Excessive bone degradation by osteoclasts leads to pathological decreased bone density characteristic of osteolytic diseases such as post-menopausal osteoporosis or bone metastasis. Osteoclasts are multinucleated cells derived from hematopoietic stem cells via a complex differentiation process. Their unique ability to resorb bone is dependent on the formation of the actin-rich sealing zone. Within this adhesion structure, the plasma membrane differentiates into the ruffled border where protons and proteases are secreted to demineralize and degrade bone, respectively. On the bone surface, mature osteoclasts alternate between stationary resorptive and migratory phases. These are associated with profound actin cytoskeleton reorganization, until osteoclasts die of apoptosis. In this review, we highlight the role of Rho GTPases in all the steps of osteoclasts differentiation, function, and death and conclude on their interest as targets for treatment of osteolytic pathologies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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 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

Citations95
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSmall GTPasesSame topicBone Metabolism and DiseasesFrench-language works237,207