Spaceflight Disrupts Gene Expression of Estrogen Signaling in Rodent Mammary Tissue
Bibliographic record
Abstract
Life aboard spacecraft poses various dangers to astronaut health, with hazards including microgravity, radiation, and enclosed spaces. Research into mitigating these health issues includes analysis of the transcriptome of rodents sent to the International Space Station. This project investigates the effects of spaceflight on the gene expression of mammary tissue of female mice of two age groups, 10-12 and 32 weeks, in order to assess the impact of age and spaceflight on gene expression. Spaceflight-induced changes to gene expression in rodent mammary tissue could contribute to the characterization of the impact of spaceflight on female astronaut health, which has been historically underserved. Analysis of the OSD-511 dataset from NASA’s Open Science Data Repository utilized a containerized implementation of their RNA-Seq pipeline on the San José State University High Performance Computing Cluster. Seven genes were found to be differentially expressed across all comparison groups; one gene, Greb1, is implicated in hormone mediated disease. Age appears to influence biological pathways affected by spaceflight in mammary tissue, with young mice experiencing metabolic changes while older mice undergo changes to inflammatory pathways. Further research is needed to determine the mechanism of spaceflight-induced gene expression changes.
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 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 | 0.001 |
| 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".