Prevalence and Clinical Relevance of Anti-FcϵRI Autoantibody in Crohn’s Disease
Bibliographic record
Abstract
Yue Yin,1,* Yusen Hu,2,* Yanning Li,1 Xia Peng,1 Huanjin Liao,1 Wei Shen,3 Li Li1 1Department of Laboratory Medicine, Shanghai General Hospital, Shanghai, People’s Republic of China; 2Department of Gastroenterology, Shanghai General Hospital, Shanghai, People’s Republic of China; 3Department of Laboratory Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li Li, Department of Laboratory Medicine, Shanghai General Hospital, Shanghai, People’s Republic of China, Email annylish@126.com Wei Shen, Department of Laboratory Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, People’s Republic of China, Email applessw@163.comBackground: Mast cells can be activated in various ways and were shown to be involved in the development of Crohn’s disease (CD). The diagnosis of CD is still challenging, and seeking novel biomarkers is a worthwhile endeavor.Methods: An indirect enzyme-linked immunosorbent assay (ELISA) was successfully established for semi-quantitative detection of IgG anti-FcϵRI in serum using human FcϵRIα coated microplates and an enzyme-labeled anti-human IgG as secondary antibodies. The optimal working conditions were explored, followed by conducting the method evaluation. The serum samples and clinical data of 117 CD patients and 75 healthy controls were collected. IgE was measured by the rate turbidity turbidimetry; IgG anti-IgE and IgG anti-FcϵRI were detected by ELISA. IgG anti-pancreatic antibody (PAB) and anti-Saccharomyces cerevisiae antibody (ASCA) were determined by indirect immunofluorescence assay. Data were analyzed concerning the clinical characteristics.Results: IgG anti-FcϵRI was an effective marker for CD (P < 0.001), but IgE and IgG anti-IgE (P = 0.089, 0.219, respectively) were not. There was a positive correlation between anti-IgE and anti-FcϵRI (R = 0.380, P < 0.001). Anti-FcϵRI positive patients behaved with higher disease activity [OR: 1.478 (1.200~1.821), P < 0.001], but were less likely to be located in L4 among Montreal classification [OR: 0.253 (0.077~0.837), P = 0.024]. Existing indicators, PAB and ASCA, behaved with high specificity (both > 95%) with low sensitivity (both < 30%). The combination of anti-FcϵRI with existing markers significantly improved the diagnostic efficiency [AUC: 0.879 (0.831~0.928)].Conclusion: An ELISA for the detection of anti-FcϵRI was established and validated, which may contribute to facilitating research on Crohn’s diseases. Anti-FcϵRI positive CD patients were associated with higher disease activity indices, suggesting its potential value in the diagnosis and management of CD.Keywords: Crohn’s disease, anti-FcϵRI, biomarkers, autoantibodies, mast cells
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".