Gas Chromatography/Mass Spectrometry Metabolomics of Urine and Serum from Nonhuman Primates Exposed to Ionizing Radiation: Impacts on the Tricarboxylic Acid Cycle and Protein Metabolism
Bibliographic record
Abstract
Ionizing radiation (IR) directly damages cells and tissues or indirectly damages them through reactive free radicals that may lead to longer term adverse sequelae such as cancers, persistent inflammation, or possible death. Potential exposures include nuclear reactor accidents, improper disposal of equipment containing radioactive materials or medical errors, and terrorist attacks. Metabolomics (comprehensive analysis of compounds <1 kDa) by mass spectrometry (MS) has been proposed as a tool for high-throughput biodosimetry and rapid assessment of exposed dose and triage needed. While multiple studies have been dedicated to radiation biomarker discovery, many have utilized liquid chromatography (LC) MS platforms that may not detect particular compounds (e.g., small carboxylic acids or isomers) that complementary analytical tools, such as gas chromatography (GC) time-of-flight (TOF) MS, are ideal for. The current study uses global GC-TOF-MS metabolomics to complement previous LC-MS analyses on nonhuman primate biofluids (urine and serum) 7 days after exposure to 2, 4, 6, 7, and 10 Gy IR. Multivariate data analysis was used to visualize differences between control and IR exposed groups. Univariate analysis was used to determine a combined 26 biomarkers in urine and serum that significantly changed after exposure to IR. We found several metabolites involved in tricarboxylic acid cycle function, amino acid metabolism, and host microbiota that were not previously detected by global and targeted LC-MS studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".