Osteoclast Ion Channels Potential Targets for Antiresorptive Drugs.
Bibliographic record
Abstract
This review summarizes the types of ion channels that have been identified in osteoclasts and considers their potential as targets for therapeutic agents aimed at the treatment of osteoporosis and other bone disorders. We focus on channels that have been identified using molecular and electrophysiological approaches. Numerous ion channels have been characterized, including K(+), H(+), Na(+), nonselective cation and Cl(-) channels. K(+) channels include an inward rectifier K(+) channel (Kir2.1) that is regulated by G proteins, and a transient outward rectifier K(+) channel (Kv1.3) that is regulated by cell-matrix interactions and by extracellular cations such as Ca(2+) and H(+). In addition, two classes of Ca(2+)-activated K(+) channels have been described--large and intermediate conductance channels, which are activated by increases of cytosolic Ca(2+) concentration. Other channels include stretch-activated nonselective cation channels and voltage-activated H(+) channels. A recent revelation is the presence of ligand-gated channels in osteoclasts, including P2X nucleotide receptors and glutamate-activated channels. Osteoclasts also exhibit an outwardly rectifying Cl(-) current that is activated by cell swelling. Kir2.1 and Cl(-) channels may be essential for resorptive activity because they provide pathways to compensate for charge accumulation arising from the electrogenic transport of H(+). As in other cell types, osteoclast ion channels also play important roles in setting the membrane potential, signal transduction and cell volume regulation. These channels represent potential targets for the development of antiresorptive drugs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".