Markers of Tissue Deterioration and Pain on Earth and in Space
Bibliographic record
Abstract
Purpose: Pain is an understudied physiological effect of spaceflight. Changes in inflammatory and tissue degradation markers are often associated with painful conditions. Our aim was to evaluate the changes in markers associated with tissue deterioration after a short-term spaceflight. Patients and Methods: Plasma levels of markers for systemic inflammation and tissue degeneration markers were assessed in two astronauts before and within 24 h after the 17-day Axiom Space AX-1 mission. Results: After the spaceflight, C-reactive protein (CRP) was reduced in both astronauts, while INFγ, GM-CSF, TNFα, BDNF, and all measured interleukins were consistently increased. Chemokines demonstrated variable changes, with consistent positive changes in CCL3, 4, 8, 22 and CXCL8, 9, 10, and consistent negative change in CCL8. Markers associated with tissue degradation and bone turnover demonstrated consistent increases in MMP1, MMP13, NTX and OPG, and consistent decreases in MMP3 and MMP9. Conclusion: Spaceflight induced changes in the markers of systemic inflammation, tissue deterioration, and bone resorption in two astronauts after a short, 17-day, which were often consistent with those observed in painful conditions on Earth. However, some differences, such as a consistent decrease in CRP, were noted. All records for the effect of space travel on human health are critical for improving our understanding of the effect of this unique environment on humans.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".