In silico Identification of Immune Cell-Types and Pathways Involved in Chronic Spontaneous Urticaria
Bibliographic record
Abstract
Background: The immunopathogenesis of chronic spontaneous urticaria (CSU) is poorly understood, but recent research suggests that patients can be divided into autoallergic and autoimmune subtypes. Given that not all patients can be controlled with current treatment regimens, including anti-IgE monoclonal antibodies, a better understanding of the immune pathways involved in CSU may enable the repurposing of monoclonal antibodies used for other dermatologic diseases (e.g., Th2 and Th17 inhibitors). Therefore, we investigated the implicated immune cells and pathways by reanalyzing publicly available transcriptomic data. Methods: fold change| ≥1. Pathway analyses were conducted using ToppGene and KEGG. Cell-type enrichment was determined by CIBERSORT and xCell and was correlated with clinical characteristics. Results: Th2 (IL-4/13 signaling) and Th17-related (IL-17/23 signaling) pathways were upregulated in lesional compared to non-lesional and HC samples. In non-lesional versus lesional samples, CIBERSORT analysis revealed increased regulatory T-cells (Treg) and resting mast cells. xCell analysis established that Th1 and Th2 scores were not significantly different between lesional and HC samples. However, Th2 scores in both lesional and non-lesional samples correlated positively with disease severity. Few differentially expressed genes and pathways were identified between CSU and HC blood samples. Conclusion: Our results support the involvement of Th2 and Th17-related genes and pathways in CSU. Th2 scores associate with disease severity, which indicates the clinical relevance of these findings. Increased resting mast cell and Treg scores in non-lesional samples may suggest local suppression of wheal formation. Moreover, disease activity seemed to be restricted to the skin as there were limited findings from blood. Larger studies using next-generation sequencing will be helpful to confirm these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".