Scatter Irradiation of Rat Brain Triggers Sex- and Brain Region-Specific Changes in the Expression of Non-Coding RNA Fragments
Bibliographic record
Abstract
Non-coding RNA fragments (ncRFs) are small RNA fragments processed from non-coding RNAs (ncRNAs). ncRFs have various functions and are commonly tissue-specific, and their processing is altered by exposure to stress. Information about ncRFs in the brain is scarce. Recently, we reported the brain region-specific and sex-specific expression of ncRNAs and their processing into ncRFs. Here, we analyzed the expression of ncRFs in the frontal cortex (FC), hippocampus (HIP), and cerebellum (CER) of male and female rats exposed to scatter radiation. We found multiple brain region- and sex-specific changes in response to scatter radiation. Specifically, we observed decreased miRNA expression and the increased expression of ra-ncRNA reads in HIP and CER, as well as an increased number of mtR-NA-associated reads in HIP. We also observed the appearance of sense-intronic ncRNAs-in females, in HIP and FC, and in males, in CER. In this work, we also show that tRNA-GlyGCC and tRNA-GlyCCC are most frequently processed to tRFs, in CER in females, as compared to males. An analysis of the targeted pathways revealed that tRFs and snoRFs in scatter radiation samples mapped to genes in several pathways associated with various neuronal functions. While in HIP and CER these pathways were underrepresented, in FC, they were overrepresented. Such changes may play an important role in pathologies that develop in response to scatter radiation, the effect known as "radio-brain", and may in part explain the sex-specific differences observed in animals and humans exposed to radiation and scatter radiation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".