Type 2 immunity is protective in metabolic disease but exacerbates NAFLD collaboratively with TGF-β
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is now the most common progressive liver disease in developed countries and is the second leading indication for liver transplantation due to the extensive fibrosis it causes. NAFLD progression is thought to be tied to chronic low-level type 1 inflammation originating in the adipose tissue during obesity; however, the specific immunological mechanisms regulating the progression of NAFLD-associated fibrosis in the liver are unclear. To investigate the immunopathogenesis of NAFLD more completely, we investigated adipose dysfunction, nonalcoholic steatohepatitis (NASH), and fibrosis in mice that develop polarized type 1 or type 2 immune responses. Unexpectedly, obese interleukin-10 (IL-10)/IL-4-deficient mice (type 1-polarized) were highly resistant to NASH. This protection was associated with an increased hepatic interferon-γ (IFN-γ) signature. Conversely, IFN-γ-deficient mice progressed rapidly to NASH with evidence of fibrosis dependent on transforming growth factor-β (TGF-β) and IL-13 signaling. Unlike increasing type 1 inflammation and the marked loss of eosinophils seen in expanding adipose tissue, progression of NASH was associated with increasing eosinophilic type 2 liver inflammation in mice and human patient biopsies. Finally, simultaneous inhibition of TGF-β and IL-13 signaling attenuated the fibrotic machinery more completely than TGF-β alone in NAFLD-associated fibrosis. Thus, although type 2 immunity maintains healthy metabolic signaling in adipose tissues, it exacerbates the progression of NAFLD collaboratively with TGF-β in the liver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".