Adverse Outcome Pathways Applied to Space Radiation Research
Bibliographic record
Abstract
Long-duration spaceflight exposes astronauts to various stressors that can alter human physiology, potentially causing immediate and long-term health effects. These stressors can damage biomolecules, cells, tissues, and organs, leading to adverse outcomes. Developing adverse outcome pathways (AOPs) relevant to radiation exposure can guide research priorities and inform risk assessments of future space exploration activities. Through expert consultation, we developed an AOP network linking 18 key events (KEs) to four non-cancer outcomes: learning and memory impairment, bone loss, abnormal vascular remodeling, and cataract development. A novel scoping review methodology informed the evidence evaluation and supported causal linkages between two KEs. The AOP network begins with the molecular initiating event (MIE) of energy deposition onto cells, which may trigger oxidative stress and DNA damage. If DNA damage is misrepaired, it could lead to gene mutations or chromosomal aberrations. In cases where these occur in critical cell cycle genes, there is a possibility of uncontrolled cellular proliferation. Persistent KEs may contribute to the activation of tissue-resident cells, suppression of anti-inflammatory processes, and promotion of chronic inflammation. This inflammatory cycle, potentially driven by mitochondrial dysfunction and immune cell activation, could lead to cell death and tissue damage. Over time, this accumulation of damage might contribute to organ-specific adverse outcomes associated with radiation exposure. This AOP network consolidates knowledge across biological levels and identifies gaps in understanding causal relationships. It aims to guide research for space traveler risk models and can also apply to other radiation exposure scenarios, such as in medical or occupational settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".