Differential Serum Levels of Eosinophilic Eotaxins in Primary Sclerosing Cholangitis, Primary Biliary Cirrhosis, and Autoimmune Hepatitis
Bibliographic record
Abstract
To investigate pathogenic mechanisms of primary sclerosing cholangitis (PSC), primary biliary cirrhosis (PBC), and autoimmune hepatitis (AIH), serum levels of 26 chemokines and cytokines were determined and compared with patients with chronic hepatitis C or in healthy controls. The chemokine eotaxin-3 (E3; CCL26), which recruits eosinophils to sites of inflammation, was found to be highly elevated in all PSC, PBC, and AIH patients compared with HCV patients and healthy controls. Eotaxin-1 (E1; CCL11), another eosinophil-specific chemokine, was elevated in PSC but reduced in PBC and AIH, while the macrophage-derived chemokine (MDC; CCL22) was lower in all PSC, PBC, and AIH patients compared with HCV patients and controls. By incorporating levels of the interleukin (IL)-15 into a diagnostic algorithm, PSC, PBC, and AIH patients could each be differentiated with good sensitivity and specificity. These findings represent the first study to compare the level of serum cytokine/chemokine levels among these related autoimmune-like liver diseases. Furthermore, our data indicate that the measurement of serum E3, E1, CCL22, and IL-15 levels can aid in the diagnosis of these clinically challenging diseases and shed light on the potential pathogenic mechanisms underlying these diseases. By suggesting a potential role for an allergic phenomenon involving eosinophils, which may define them as liver-specific allergic diseases, this may open up potential new therapeutic avenues by abrogating the action of these disease-associated immune modulators.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".