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Record W2059133755 · doi:10.2174/22115501113029990017

Osteocyte biology and space flight

2013· article· en· W2059133755 on OpenAlexaff
Paola Divieti Pajevic, Jordan Spatz, Jenna Garr, Chris Adamson, Lowell Misener

Bibliographic record

VenueCurrent Biotechnology · 2013
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsCanadian Association of Physical Medicine and Rehabilitation
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsOsteocyteTransduction (biophysics)WeightlessnessSclerostinCell biologyNeuroscienceSignal transductionHomeostasisBone cellMechanotransductionBiologyWnt signaling pathwayOsteoblastIn vitroBiophysicsPhysicsBiochemistry

Abstract

fetched live from OpenAlex

The last decade has seen an impressive expansion of our understanding of the role of osteocytes in skeletal homeostasis. These amazing cells, deeply embedded into the mineralized matrix, are the key regulators of bone homeostasis and skeletal mechano sensation and transduction. They are the cells that can sense the mechanical forces applied to the bone and then translate these forces into biological responses. They are also ideally positioned to detect and respond to hormonal stimuli and to coordinate the function of osteoblasts and osteoclasts through the production and secretion of molecules such as Sclerostin and RANKL. How osteocytes perceive mechanical forces and translate them into biological responses in still an open question. Novel "in vitro" models as well the opportunity to study these cells under microgravity condition, will allow a closer look at the molecular and cellular mechanisms of mechano transduction. This article highlights novel investigations on osteocytes and discusses their significance in our understanding of skeletal mechano transduction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.295
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2013
Admission routes1
Has abstractyes

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