Acute Impacts of Ionizing Radiation Exposure on the Gastrointestinal Tract and Gut Microbiome in Mice
Bibliographic record
Abstract
Radiation therapy for abdominopelvic malignancies often results in damage to the gastrointestinal tract (GIT) and permanent changes in bowel function. An overlooked component of the pathophysiology of radiation-induced bowel injury is the role of the gut microbiome. The goal of this research was to identify the impacts of acute radiation exposure on the GIT and gut microbiome. C57BL/6 mice exposed to whole-body X-rays (0.1-3 Gy) were assessed for histological and microbiome changes 48 h post-radiation exposure. Within the ileum, a dose of 3 Gy significantly decreased crypt depth as well as the number of goblet cells, but increased overall goblet cell size. Overall, radiation altered the microbial distribution within each of the main phyla in a dose- and tissue-dependent manner. Within the Firmicutes phylum, high dose irradiation resulted in significant alterations in bacteria from the class Bacilli within the small bowels, and from the class Clostridia in the large bowels. The 3 Gy radiation also significantly increased the abundance of bacterial families from the Bacteroidetes phylum in the colon and feces. Overall, we identified various alterations in microbiome composition following acute radiation exposure, which could potentially lead to novel biomarkers for tracking patient toxicities or could be used as targets for mitigation strategies against radiation damage.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".