Understanding the Complexities and Changes of the Astronaut Microbiome for Successful Long-Duration Space Missions
Bibliographic record
Abstract
During space missions, astronauts are faced with a variety of challenges that are unique to spaceflight and that have been known to cause physiological changes in humans over a period of time. Several of these changes occur at the microbiome level, a complex ensemble of microbial communities residing in various anatomic sites of the human body, with a pivotal role in regulating the health and behavior of the host. The microbiome is essential for day-to-day physiological activities, and alterations in microbiome composition and function have been linked to various human diseases. For these reasons, understanding the impact of spaceflight and space conditions on the microbiome of astronauts is important to assess significant health risks that can emerge during long-term missions and to develop countermeasures. Here, we review various conditions that are caused by long-term space exploration and discuss the role of the microbiome in promoting or ameliorating these conditions, as well as space-related factors that impact microbiome composition. The topics explored pertain to microgravity, radiation, immunity, bone health, cognitive function, gender differences and pharmacomicrobiomics. Connections are made between the trifecta of spaceflight, the host and the microbiome, and the significance of these interactions for successful long-term space missions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".