Microarchitecture, but Not Bone Mechanical Properties, Is Rescued with Growth Hormone Treatment in a Mouse Model of Growth Hormone Deficiency
Bibliographic record
Abstract
Growth hormone (GH) deficiency is related to an increased fracture risk although it is not clear if this is due to compromised bone quality or a small bone size. We investigated the relationship between bone macrostructure, microarchitecture and mechanical properties in a GH-deficient (GHD) mouse model undergoing GH treatment commencing at an early (prepubertal) or late (postpubertal) time point. Microcomputed tomography images of the femur and L4 vertebra were obtained to quantify macrostructure and vertebral trabecular microarchitecture, and mechanical properties were determined using finite element analyses. In the GHD animals, bone macrostructure was 25 to 43% smaller as compared to the GH-sufficient (GHS) controls (P < 0.001). GHD animals had 20% and 19% reductions in bone volume ratio (BV/TV) and trabecular thickness (Tb.Th), respectively. Whole bone mechanical properties of the GHD mice were lower at the femur and vertebra (67% and 45% resp.) than the GHS controls (P < 0.001). Both early and late GH treatment partially recovered the bone macrostructure (15 to 32 % smaller than GHS controls) and the whole bone mechanical properties (24 to 43% larger than GHD animals) although there remained a sustained 27-52% net deficit compared to normal mice (P < 0.05). Importantly, early treatment with GH led to a recovery of BV/TV and Tb.Th with a concomitant improvement of trabecular mechanical properties. Therefore, the results suggest that GH treatment should start early, and that measurements of microarchitecture should be considered in the management of GHD.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".