Involvement of ICAM-1 in bone metabolism: a potential target in the treatment of bone diseases?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".