<i>Sost</i> deficiency leads to reduced mechanical strains at the tibia midshaft in strain-matched <i>in vivo</i> loading experiments in mice
Bibliographic record
Abstract
Sclerostin, a product of the Sost gene, is a Wnt-inhibitor and thus negatively regulates bone accrual. Canonical Wnt/β-catenin signalling is also known to be activated in mechanotransduction. Sclerostin neutralizing antibodies are being tested in ongoing clinical trials to target osteoporosis and osteogenesis imperfecta but their interaction with mechanical stimuli on bone formation remains unclear. Sost knockout (KO) mice were examined to gain insight into how long-term Sost deficiency alters the local mechanical environment within the bone. This knowledge is crucial as the strain environment regulates bone adaptation. We characterized the bone geometry at the tibial midshaft of young and adult Sost KO and age-matched littermate control (LC) mice using microcomputed tomography imaging. The cortical area and the minimal and maximal moment of inertia were higher in Sost KO than in LC mice, whereas no difference was detected in either the anterior–posterior or medio-lateral bone curvature. Differences observed between age-matched genotypes were greater in adult mice. We analysed the local mechanical environment in the bone using finite-element models (FEMs), which showed that strains in the tibiae of Sost KO mice are lower than in age-matched LC mice at the diaphyseal midshaft, a region commonly used to assess cortical bone formation and resorption. Our FEMs also suggested that tissue mineral density is only a minor contributor to the strain distribution in tibial cortical bone from Sost KO mice compared to bone geometry. Furthermore, they indicated that although strain gauging experiments matched strains at the gauge site, strains along the tibial length were not comparable between age-matched Sost KO and LC mice or between young and adult animals within the same genotype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".