Type 2 Deiodinase and Host Responses of Sepsis and Acute Lung Injury
Bibliographic record
Abstract
The role of thyroid hormone metabolism in clinical outcomes of the critically ill remains unclear. Using preclinical models of acute lung injury (ALI), we assessed the gene and protein expression of type 2 deiodinase (DIO2), a key driver for synthesis of biologically active triiodothyronine, and addressed potential association of DIO2 genetic variants with ALI in a multiethnic cohort. DIO2 gene and protein expression levels in murine lung were validated by microarrays and immunoblotting. Lung injury was assessed by levels of bronchoalveolar lavage protein and leukocytes. Single-nucleotide polymorphisms were genotyped and ALI susceptibility association assessed. Significant increases in both DIO2 gene and D2 protein expression were observed in lung tissues from murine ALI models (LPS- and ventilator-induced lung injury), with expression directly increasing with the extent of lung injury. Mice with reduced levels of DIO2 expression (by silencing RNA) demonstrated reduced thyroxine levels in plasma and increased lung injury (increased bronchoalveolar lavage protein and leukocytes), suggesting a protective role for DIO2 in ALI. The G (Ala) allele of the Thr92Ala coding single-nucleotide polymorphism (rs225014) was protective in severe sepsis and severe sepsis-associated ALI after adjustments for age, sex, and genetic ancestry in a logistic regression model in European Americans. Our studies indicate that DIO2 is a novel ALI candidate gene, the nonsynonymous Thr92Ala coding variant of which confers ALI protection. Increased DIO2 expression may dampen the ALI inflammatory response, thereby strengthening the premise that thyroid hormone metabolism is intimately linked to the integrated response to inflammatory injury in critically ill patients.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".