Point of care testing of biochemical markers for monitoring astronauts during long duration missions in deep space
Bibliographic record
Abstract
BACKGROUND: Investigations into human performance and health status during long-duration spaceflights are ongoing aboard the International Space Station (ISS) and are critical for planning missions beyond Low Earth Orbit (LEO). This review evaluates the current evidence on point-of-care-testing (POCT) in space, discussing requirements for POCT to support astronaut health during extended deep space missions and the potential for technology transfer to terrestrial healthcare. CONTENTS: Microgravity disrupts biochemical and hormonal regulation, leading to reversible homeostatic dysregulation across multiple organ systems in astronauts during spaceflight in LEO. Missions beyond LEO may significantly increase risks of morbidity and mortality in particular for cardiovascular disease. The ability to assess key organ functions by expanding the range of detectable biomarkers on POCTs is crucial for ongoing health monitoring, urgent clinical decisions, and future mission planning. The recent implementation of high-sensitivity cardiac troponin I on POCT will be essential for early identifying myocardial injury and facilitating telemedicine support. Furthermore, the development and use of reference change values (RCV) in space environment, derived from biomarkers measured longitudinally in real-time onboard, will be crucial to differentiate clinically relevant changes in biomarkers levels from alterations induced by microgravity. This approach may overcome the use of traditional cut-off values which are assessed and generally applied under stable terrestrial conditions. SUMMARY: Clinical laboratory capabilities on the ISS are currently minimal. As missions lengthen, and extend beyond LEO, enhanced POCT is needed. Limited data on POCT performance in space highlight the importance of laboratory professionals' involvement to ensure high-quality testing and accurate interpretation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".