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Record W2147070032 · doi:10.1517/14712598.5.3.313

Involvement of ICAM-1 in bone metabolism: a potential target in the treatment of bone diseases?

2005· review· en· W2147070032 on OpenAlexaff
Patrick Lavigne, Mohamed Benderdour, Qin Shi, Daniel Lajeunesse, Julio Fernandes

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2005
Typereview
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversité de MontréalHôpital Notre-DameHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsOsteoclastMedicineOsteoporosisRheumatoid arthritisIntercellular Adhesion Molecule-1Bone remodelingBone diseaseInflammationCell adhesion moleculeImmunologyReceptorInternal medicine

Abstract

fetched live from OpenAlex

Bone diseases such as osteoporosis, osteoarthritis and rheumatoid arthritis (RA) affect a great proportion of individuals, with debilitating consequences in terms of pain and progressive limitation of function. Existing treatment of these pathologies has been unable to alter the natural evolution of the disease and, as such, a clearer understanding of the pathophysiology is necessary in order to generate new treatment alternatives. One therapeutic strategy could involve the targeting of intercellular adhesion molecule-1 (ICAM-1; CD54). In bone, ICAM-1 is expressed at the surface of osteoblasts (Obs) and its counter-receptor, leukocyte function-associated antigen-1 (LFA-1; CD11a), at the surface of osteoclast (Oc) precursors. ICAM-1 blockade between the Ob and the pre-Oc results in an inhibition of Oc recruitment and a modulation of inflammation, which could potentially help in controlling disease activity in bone pathologies. So far, clinical studies on ICAM-1 blockade in bone diseases have been limited to RA. A better understanding of the implication of this adhesion molecule in Ob/Oc interactions and inflammatory mediation in the bone pathological state, however, is needed. As new discoveries on the role of this adhesion molecule are being reported, ICAM-1 could become a potential target for other bone diseases as well.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
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)0.0010.000

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 head0.152
GPT teacher head0.426
Teacher spread0.274 · 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

Citations22
Published2005
Admission routes1
Has abstractyes

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