Nonlinear Dose-Response Relationship in the Immune System Following Exposure to Ionizing Radiation: Mechanisms and Implications
Bibliographic record
Abstract
The health effects of low-dose radiation (LDR) have been the concern of the academic spheres, regulatory bodies, governments, and the public. Among these effects, the most important is carcinogenesis. In view of the importance of immune surveillance in cancer control, the dose-response relationship of the changes in different cell types of the immune system after whole-body irradiation is analyzed on the basis of systemic data from the author's laboratory in combination with recent reports in the literature. For T lymphocytes J- or inverted J-shaped curves are usually demonstrated after irradiation, while for macrophages dose-response curves of chiefly stimulation with irregular patterns are often observed. The intercellular reactions between the antigen presenting cell (APC) and T lymphocyte (TLC) in the immunologic synapse via expression of surface molecules and secretion of cytokines by the two cell types after different doses of radiation are illustrated. The different pathways of signal transduction thus facilitated in the T lymphocyte by different doses of radiation are analyzed to explain the mechanism of the phenomenon of low-dose stimulation and high-dose suppression of immunity. Experimental and clinical data are cited to show that LDR retards tumor growth, reduces metastasis, increases the efficacy of conventional radiotherapy and chemotherapy as well as alleviates the suppression of immunity due to tumor burden. The incidence of thymic lymphoma after high-dose radiation is lowered by preexposure to low-dose radiation, and its mechanism is supposed to be related to the stimulation of anticancer immunity induced by low-dose radiation. Recent reports on lowering of standardized cancer mortality rate and all cause death rate of cohorts occupationally exposed to low-dose radiation from the US, UK, and Canada are cited. dose-response curvesimmune surveillancemolecular and cellular mechanismscancer risk.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".