Artificial gravity protects bone and prevents bone marrow adipose tissue accumulation in humans during 60 d of bed rest
Bibliographic record
Abstract
Inactivity has been associated with increased bone marrow adipose tissue (BMAT) and bone loss. Artificial gravity (AG) may prevent these complications. This randomized controlled trial investigated the effectiveness of AG at 2 g at the feet to prevent lumbar vertebral BMAT accumulation and bone loss. Twenty-four participants (16 male, 8 female) were bedridden for 60 d at 6° head down tilt. They were randomly assigned to bedrest only (n = 8), continuous supine centrifugation (cAG; 30 min/d), or intermittent supine centrifugation (iAG; 6 bouts of 5 min/d). Serial 3T magnetic resonance (MR) measured BMAT while DXA measured BMD in the lumbar vertebrae before, during, and after bedrest. After 60 d of bedrest, vertebral BMAT was higher in controls, +3.93% (95% CI: -0.28 to 8.14), compared to cAG and iAG interventions. After 60 d of bedrest, male controls BMAT increased 5.81% (95% CI: 2.01 to 9.61) compared to -1.35% (95% CI: -5.74 to 3.04) and 1.23% (95% CI: -1.53 to 3.99) for male cAG and iAG participants, respectively. This difference between interventions was significant: X2(2) = 8.487, p = .014. In addition, while control male participants showed decreased BMD after 60 d of bedrest (-0.02 g/cm2; 95% CI: -0.05 to 0.00), the male participants receiving iAG showed no decrease in BMD during bedrest (0.00 g/cm2; 95% CI: -0.04 to 0.05). The modulation of BMAT was inversely correlated with BMD at the same vertebrae. Recreating an axial force vector mechanically on horizontalized participants prevented BMAT accumulation and demineralization. These findings suggest exploring technological advances to translate these clinical benefits to populations at risk of acute or chronic bone loss.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".