Effect of Muscle Unloading, Reloading and Exercise on Inflammation during a Head-down Bed Rest
Bibliographic record
Abstract
Muscles are affected by unloading during head-down bed rests and by reloading through normal reambulation. This study investigated the effects of a 60 days head-down long-term bed rest with or without predefined exercise countermeasures on the development of an inflammatory reaction.Blood samples were taken before, during and after bed rest in control and exercise groups of women. They were assayed for soluble ICAM-1, VCAM-1, E- and L-selectin, IL1β, IL6, TNFα and CRP as markers of inflammation with ELISA.Head-down long-term bed rest induced plasma volume variations which had an impact on the concentrations of the inflammatory factors and led to data corrections for a reliable analysis of the results. None of the marker of inflammation, except IL6 in control group, showed a significant change from baseline during bed rest. The main results were obtained during recovery. VCAM-1 increased in all groups, ICAM-1, in the control group, and L-selectin, in the exercise group. Peaks of IL6 and CRP were observed on day 59 of bed rest for IL6 and on day 2 of recovery for CRP in the control group. Exercise during bed rest prevented the augmentation of IL6, CRP and ICAM-1. These results might suggest a shift towards pro-inflammatory conditions, prevented in part by exercises.
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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 | 0.000 |
| 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".