Dose-response modeling reveals multifaceted molecular responses to low-dose radiation in human white blood cells
Bibliographic record
Abstract
BACKGROUND: The current radiation protection framework extrapolates health risks from high-dose exposures based on a linear, no-threshold model. However, empirical data on molecular effects below 0.1 Gy are lacking, creating uncertainties in risk assessments. To address this, we used benchmark dose (BMD) modeling, commonly applied in chemical hazard assessment, to analyze gene and protein expression changes in human white blood cells, providing insights into dose-response relationships following low-dose radiation (LDR) exposure. METHODS: Blood samples were collected from 14 participants (6 females, 8 males). Lymphocytes were isolated, cultured, and exposed to X-irradiation at nine doses (0-6 Gy) at 0.05 Gy/min. Transcriptomic and proteomic changes were assessed 24 h post-exposure. BMD modeling was applied to each endpoint, and the data were grouped into distinct dose-response patterns. Pathway analysis identified cellular functions associated with these patterns, offering insight into the biological effects of LDR. RESULTS: BMD modeling identified 1,204 genes and 168 proteins with dose-response relationships, with median BMD lower confidence limits (BMDLs) of 1.38 Gy and 0.21 Gy, respectively. Transcriptional and proteomic responses exhibited complex patterns, including exponential, biphasic, and hypersensitivity responses, with peak activity between 0.05-0.25 Gy, followed by a decline or plateau. Pathway analysis revealed changes in genes and proteins related to DNA damage, cell cycle, cellular stress, metabolism, immune function, and cancer, with DNA damage response genes showing BMDLs below 0.1 Gy. CONCLUSIONS: This study shows that molecular dose-response relationships can be complex and non-linear, emphasizing the need for further research to better understand the effects of LDR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".