Development of a low-dose anti-resorptive drug regimen reveals synergistic suppression of bone formation when coupled with disuse
Bibliographic record
Abstract
Safe and effective countermeasures to spaceflight-induced osteoporosis are required to mitigate the potential for mission-critical fractures and ensure long-term bone health in astronauts. Two anti-resorptive drugs, the bisphosphonate zoledronic acid (ZOL) and the anti-receptor activator of NF-kappaB ligand protein osteoprotegerin (OPG), were investigated to find the minimum, comparable doses that yield a maximal increase in bone quality, while minimizing deleterious effects on turnover and mineralization. Through a series of five trials in normally loaded female mice (n = 56/trial), analysis of trabecular volume fraction and connectivity using microcomputed tomography, along with biomechanical testing, quantitative histomorphometry, and compositional analysis, was used to select 45 microg/kg ZOL and 500 microg/kg OPG as doses that satisfy these criteria. These doses were then examined for their ability to mitigate bone loss following short-term unloading through hindlimb suspension (HLS). Seventy-two mice were prophylactically administered ZOL, OPG, or PBS and assigned to loaded control or 2-wk HLS groups (n = 12 for each of 6 groups). Both anti-resorptives were able to preserve trabecular microarchitecture and femoral elastic and maximum force in HLS mice (+30-40% ZOL/OPG vs. PBS). In HLS mice, anti-resorptive dosing reduced resorption perimeter at the femoral endocortical surface by 30% vs. PBS. In loaded control mice, anti-resorptives produced no change in bone formation rate; however, reductions in bone formation rate brought about by HLS were exacerbated by anti-resorptive treatment, suggesting synergistic inhibition of osteoblasts during disuse. Refined anti-resorptive dosing will tend to target countermeasures to the period of disuse, resulting in faster recovery and less adverse effects for astronauts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".