A critical role of B cells in biliary disease and sialadenitis in the NOD.c3c4 model of autoimmune cholangitis. (44.8)
Bibliographic record
Abstract
Abstract The role of B cells in the pathogenesis of PBC in humans has been a controversial problem. On the one hand, the serologic hallmark of PBC are the presence of antimitochondrial antibodies, found in >90% of patients. However, the antibodies do not correlate with disease severity and the titers do not change following therapeutic orthotopic liver transplantation. In addition, related work on a murine model of PBC, the dnTGFβRII mice, suggests that regulatory B cells may be an important contributor to disease pathogenesis. Indeed, the absence of B cells in this model exacerbates biliary pathology. To address this problem, we have taken advantage of the availability of both dnTGFβRII mice and NOD.c3c4 mice, to study the role of B cells in the natural history of disease. In particular, we generated genetically B cell deficient (Igμ-/-) NOD.c3c4 mice and compared the immunopathology of these mice to control B cell sufficient (Igμ+/+) NOD.c3c4 mice. Igμ-/- NOD.c3c4 mice not only had an amelioration of salivary gland inflammation, but also reduced numbers of inflammatory liver infiltrates, ameliorated liver inflammation, and a significantly lower prevalence of biliary cyst formation. B cell deficient mice demonstrated decreased number of non-B cells in the liver accompanied by reduced numbers of activated natural killer cells. In conclusion, B cells play a critical role in promoting liver inflammation and cyst formation as well as salivary gland pathology in autoimmune NOD.c3c4 mice.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".