Medullary Thymic Epithelial Cells and Central Tolerance in Autoimmune Hepatitis Development: Novel Perspective from a New Mouse Model
Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is an immune-mediated disorder that affects the liver parenchyma. Diagnosis usually occurs at the later stages of the disease, complicating efforts towards understanding the causes of disease development. While animal models are useful for studying the etiology of autoimmune disorders, most of the existing animal models of AIH do not recapitulate the chronic course of the human condition. In addition, approaches to mimic AIH-associated liver inflammation have instead led to liver tolerance, consistent with the high tolerogenic capacity of the liver. Recently, we described a new mouse model that exhibited spontaneous and chronic liver inflammation that recapitulated the known histopathological and immunological parameters of AIH. The approach involved liver-extrinsic genetic engineering that interfered with the induction of T-cell tolerance in the thymus, the very process thought to inhibit AIH induction by liver-specific expression of exogenous antigens. The mutation led to depletion of specialized thymic epithelial cells that present self-antigens and eliminate autoreactive T-cells before they exit the thymus. Based on our findings, which are summarized below, we believe that this mouse model represents a relevant experimental tool towards elucidating the cellular and molecular aspects of AIH development and developing novel therapeutic strategies for treating this disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".