Effect of sclerostin inactivation in a mouse model of severe dominant osteogenesis imperfecta
Bibliographic record
Abstract
Abstract Osteogenesis imperfecta (OI) is a rare bone disease that is associated with fractures and low bone mass. Sclerostin inhibition is being evaluated as a potential approach to increase bone mass in OI. We had previously found that in Col1a1Jrt/+ mice, a model of severe OI, treatment with an anti-sclerostin antibody had a minor effect on the skeletal phenotype. In the present study, we assessed the effect of genetic sclerostin inactivation in the Col1a1Jrt/+ mouse. We crossed Col1a1Jrt/+ mice with Sost knockout mice to generate Sost-deficient Col1a1Jrt/+ mice and assessed differences between Col1a1Jrt/+ mice with homozygous Sost deficiency and Col1a1Jrt/+ mice with heterozygous Sost deficiency. We found that Col1a1Jrt/+ mice with homozygous Sost deficiency had higher body mass, femur length, trabecular bone volume, cortical thickness and periosteal diameter as well as increased biomechanical parameters of bone strength. Differences between genotypes were larger at the age of 14 weeks than at 8 weeks of age. Transcriptome analysis of RNA extracted from the tibial diaphysis revealed only 5 differentially regulated genes. Thus, genetic inactivation of Sost increased bone mass and strength in the Col1a1Jrt/+ mouse. It appears from these observations that the degree of Sost suppression that is required for eliciting a beneficial response can vary with the genetic cause of OI.
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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) | 0.002 | 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".