Disruption of claudin-18 diminishes ovariectomy-induced bone loss in mice
Bibliographic record
Abstract
Claudin-18 (Cldn-18), a member of the tight junction family of proteins, is a negative regulator of RANKL-induced osteoclast differentiation and bone resorption (BR) in vivo. Since estrogen deficiency decreases bone mass in part by a RANKL-mediated increase in BR, we evaluated whether estrogen regulates Cldn-18 expression in bone. We found that Cldn-18 expression was reduced in the bones of estrogen deficient mice, whereas it was increased by estrogen treatment in osteoblasts and osteoclasts in vitro. We next evaluated the role of Cldn-18 in mediating estrogen-induced bone loss. Cldn-18 knockout (KO) and littermate wild-type (WT) mice were ovariectomized (OVX) or sham operated at 6 wk of age, and the skeletal phenotype was evaluated at 14 wk of age. PIXImus revealed that total body, femur, and lumbar BMD were reduced 8-13% (P < 0.05) after 8 wk of OVX compared with sham in WT mice. As expected, total body, femur, and lumbar BMD were reduced 14-21% (P < 0.05) in Cldn-18 KO sham mice compared with sham WT mice. However, ovariectomy failed to induce significant changes in BMD of total body, femur, or vertebra in the Cldn-18 KO mice. μCT analysis of the distal femur revealed that trabecular (Tb) bone volume was decreased 50% in the OVX WT mice compared with sham that was caused by a 26% decrease in Tb number and a 30% increase in Tb separation (all P < 0.05). By contrast, none of the Tb parameters were significantly different in OVX Cldn-18 KO mice compared with sham KO mice. Histomorphometric analyses at the Tb site revealed that neither osteoclast surface nor osteoclast perimeter was increased significantly as a consequence of OVX in either genotype at the time point examined. Based on our findings, we conclude that the estrogen effects on osteoclasts may in part be mediated via regulation of Cldn-18 signaling.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".