Multi-omic Analysis of Non-human Primate Heart after Partial-body Radiation with Minimal Bone Marrow Sparing
Bibliographic record
Abstract
ABSTRACT: High-dose radiation exposure results in hematopoietic and gastrointestinal acute radiation syndromes followed by delayed effects of acute radiation exposure, which encompasses multiple organs, including heart, kidney, and lung. Here we sought to further characterize the natural history of radiation-induced heart injury via determination of differential protein and metabolite expression in the heart. We quantitatively profiled the proteome and metabolome of left and right ventricle from non-human primates following 12 Gy partial body irradiation with 2.5% bone marrow sparing over a time period of 3 wk. Global proteome profiling identified more than 2,200 unique proteins, with 220 and 286 in the left and right ventricles, respectively, showing significant responses across at least three time points compared to baseline levels. High-throughput targeted metabolomics analyzed a total of 229 metabolites and metabolite combinations, with 18 and 22 in the left and right ventricles, respectively, showing significant responses compared to baseline levels. Bioinformatic analysis performed on metabolomic and proteomic data revealed pathways related to inflammation, energy metabolism, and myocardial remodeling were dysregulated. Additionally, we observed dysregulation of the retinoid homeostasis pathway, including significant post-radiation decreases in retinoic acid, an active metabolite of vitamin A. Significant differences between left and right ventricles in the pathology of radiation-induced injury were identified. This multi-omic study characterizes the natural history and molecular mechanisms of radiation-induced heart injury in NHP exposed to PBI with minimal bone marrow sparing.
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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 | 0.001 | 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".