Identifying quantitative trait loci involved in radiation-induced lung disease in mice
Bibliographic record
Abstract
The goal of this thesis was to identify genetic loci involved in radiation-induced lung disease in mice. Phenotypic analysis was completed for alveolitis, pulmonary fibrosis, survival time, mast cells/mm2 and cell counts in bronchoalveolar lavage (BAL) to assess lung damage. Genome scans and linkage analysis were completed for two backcross cohorts (75%C3H/HeJ-25%C57Bl/6J or 25%C3H/HeJ-75%C57BU6J) using each of the phenotypes measured. Putative susceptibility loci were identified for alveolitis on chromosomes 12 (LOD=3.35), 14 (LOD=2.22) and 19 (LOD=1.7) in the mostly C3H/HeJ backcross cohort and on chromosome 14 (LOD=2.7) in the mostly C57BI/6J backcross cohort. Linkage using the pulmonary fibrosis scores provided supportive evidence for a quantitative trait loci (QTL) on chromosome 17 (LOD=2) in the mostly C57BI/6J background, and two unique putative linkage regions were localized on chromosome 2 (LOD=2.2) in the mostly C3H/HeJ cohort and 14 (LOD=2) in the mostly C57BI/6J cohort. Supporting evidence for linkage on chromosomes 12 and 14 in the mostly C3H/HeJ background and chromosome 14 in the mostly C57BI/6J background was obtained using cellular markers. Additionally, survival time mapped to the linkage regions isolated for alveolitis and pulmonary fibrosis, suggesting that the development of radiation-induced lung disease influences the survival time of the mice. Within each cohort there were sex dependent linkage intervals, indicating that male and female mice may possess different factors involved in the development of alveolitis and/or pulmonary fibrosis. These results suggest that there are multiple genetic loci involved in the development of radiation-induced lung disease in mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".