Excess dietary iodine differentially affects thyroid gene expression in diabetes, thyroiditis‐prone versus ‐resistant BioBreeding (BB) rats
Bibliographic record
Abstract
SCOPE: To identify genes involved in the susceptibility to iodine-induced autoimmune thyroiditis. METHODS AND RESULTS: Diabetes, thyroiditis-prone (BBdp) and -resistant (BBc) rats were fed either a control or a high-iodine diet for 9 wk. Excess iodine intake increased the incidence of insulitis and thyroiditis in BBdp rats. BBdp rats fed the high-iodine diet that did not develop thyroiditis had higher mRNA levels of Fabp4, Cidec, perilipin, Pparγ and Slc36a2 than BBdp rats fed the control diet and BBc rats fed either the control or the high-iodine diet. BBdp rats fed the high-iodine diet that did develop thyroiditis had higher mRNA levels of Cidec, Icam1, Ifitm1, and Slpi than BBdp rats fed the control diet and BBc rats fed either the control or the high-iodine diet. BBdp rats that did develop thyroiditis had lower mRNA levels of Fabp4, perilipin and Slc36a2 but higher mRNA levels of Icam1, Ifitm1 and Slpi than BBdp that did not develop thyroiditis. Excess dietary iodine also increased the protein levels of Fabp4, Cidec and perilipin in BBdp rats. CONCLUSION: Differential expression of thyroid genes in BBdp versus BBc rats caused by excess dietary iodine may be implicated in autoimmune thyroiditis and insulitis pathogenesis.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".