Ionizing radiation exposure during development and growth restriction in mammals
Bibliographic record
Abstract
Exposure of the developing offspring to high doses of ionizing radiation before birth can result in short- and long-term decreases in offspring growth and size, which is known as developmental programming. This outcome in particular is an area of concern, as developmental programming with other stressors has been reported to lead to adverse effects later in life for the offspring. This thesis studied the effects of ionizing radiation exposure during development in two mammalian models: beagle dogs and BALB/c mice. The role of different irradiation regimens (fractionated, protracted and pre-treatment with a low dose of radiation) on the offspring effects were a focus of this thesis. The effects of an acute 1.82 Gy x-ray irradiation during late gestation and a number of different irradiation treatments were studied in a mouse model, with evidence for radiation-induced growth restriction in irradiated offspring. The role of the placenta, which is an important regulator of mammalian growth and development was also investigated further, to provide novel insights into the dysregulation occurring within this tissue. A number of novel differentially expressed genes, and impacted cellular pathways were identified within the placenta using RNA-sequencing technologies. Overall, this thesis provides further evidence for radiation-induced growth restriction, with information on how this response can vary based on the irradiation schedule and timing of exposure. This information is important to develop future countermeasures with the goal of limiting the adverse effects of radiation exposure.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".