Interferon-γ plays a role in bone formation in vivo and rescues osteoporosis in ovariectomized mice
Bibliographic record
Abstract
Interferon γ (IFN-γ) is a cytokine produced locally in the bone microenvironment by cells of immune origin as well as mesenchymal stem cells. However, its role in normal bone remodeling is still poorly understood. In this study we first examined the consequences of IFN-γ ablation in vivo in C57BL/6 mice expressing the IFN-γ receptor knockout phenotype (IFNγR1(-/-)). Compared with their wild-type littermates (IFNγR1(+/+)), IFNγR1(-/-) mice exhibit a reduction in bone volume associated with significant changes in cortical and trabecular structural parameters characteristic of an osteoporotic phenotype. Bone histomorphometry of IFNγR1(-/-) mice showed a low-bone-turnover pattern with a decrease in bone formation, a significant reduction in osteoblast and osteoclast numbers, and a reduction in circulating levels of bone-formation and bone-resorption markers. Furthermore, administration of IFN-γ (2000 and 10,000 units) to wild-type C57BL/6 sham-operated (SHAM) and ovariectomized (OVX) female mice significantly improved bone mass and microarchitecture, mechanical properties of bone, and the ratio between bone formation and bone resorption in SHAM mice and rescued osteoporosis in OVX mice. These data therefore support an important physiologic role for IFN-γ signaling as a potential new anabolic therapeutic target for osteoporosis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".