IRAK4 Is Overexpressed in Hidradenitis Suppurativa Skin and Correlates with Inflammatory Biomarkers
Bibliographic record
Abstract
Hidradenitis suppurativa (HS) is a chronic inflammatory disease manifesting as painful dermal nodules, abscesses, and tunnels. Activation of the IL-1R/toll-like receptor pathway is strongly implicated in the pathogenesis of HS; thus, the role of a key signaling node, IRAK4, was investigated in a noninterventional study (NCT04440410) that enrolled 30 patients with HS. IRAK4 expression was evaluated in blood and lesional, perilesional, and nonlesional skin biopsies. PBMCs expressed IRAK4, with significantly higher levels in monocytes (P ≤ .0001). Ex vivo treatment of PBMCs with KT-474, a targeted degrader of IRAK4, robustly decreased IRAK4 in all immune cell types from healthy volunteers and patients with HS. Ex vivo treatment of toll-like receptor-stimulated healthy donor monocytes with KT-474 decreased IRAK4 protein levels and inhibited inflammatory cytokine production. In HS skin samples, IRAK4 protein levels were significantly higher in lesional than in nonlesional tissue (P ≤ .0001), and IRAK4-positive immune infiltrate increased with greater disease severity. Multiple inflammatory mediators were upregulated in HS lesional skin, correlating with IRAK4 overexpression. These data confirm the significance of the IL-1R/toll-like receptor pathway in the pathogenesis of HS and provide support for ongoing clinical studies evaluating KT-474 in the treatment of HS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".