Plasma miRNAome Profiling Reveals Candidate Biomarkers for Low- and High-Dose Whole-Body Ionizing Radiation Exposure
Bibliographic record
Abstract
Objective: MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression and remain stable in biological fluids, even under harsh conditions. Their stability and responsiveness to environmental stressors make them strong candidates for radiation biodosimetry. This study aimed to (1) establish a robust in vivo pipeline for miRNAome profiling and (2) identify plasma-based miRNA biomarkers of ionizing radiation at low and high doses. Methods: BALB/c mice were exposed to sham, 100 mGy, or 2 Gy of X-rays. Plasma was collected 6 h post-irradiation. Total RNA was extracted, and next-generation sequencing was used to profile the plasma miRNAome. Differentially expressed miRNAs were identified relative to sham controls, and selected candidates were validated using RT-qPCR. Results: A total of 630 unique miRNAs were detected. High-dose exposure (2 Gy) significantly upregulated 14 and downregulated 5 miRNAs. Seven miRNAs were significantly induced at 100 mGy, including miR-126a-5p and miR-133a-3p, which were exclusive to low-dose exposure. Five miRNAs were shared between both doses, indicating dose-independent responses. RT-qPCR confirmed expression trends. Conclusion: This study identified distinct and shared circulating miRNA signatures for low- and high-dose radiation exposure. These findings support the potential of miRNAs as minimally invasive, dose-stratified biomarkers for radiation biodosimetry.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".