Correlation of Disease Severity, Proinflammatory Cytokines, and Reduced Brain Gray Matter Volumes in Patients with Atopic Dermatitis
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease. However, few studies have investigated brain changes associated with chronic inflammation. We hypothesized that chronic inflammation might be related to brain structural alterations in patients with AD. Objectives: To investigate the association between disease severity (Eczema Area and Severity Index [EASI]), proinflammatory cytokines, and differences in brain gray matter (GM) volume in patients with AD. Methods: Nineteen patients with AD and 19 age- and sex-matched healthy subjects were enrolled. All participants underwent clinical assessment and brain magnetic resonance imaging. Voxel-based morphometry was performed to analyze GM volume differences. Results: Patients with AD exhibited significantly decreased GM volume in many brain regions, such as bilateral precentral gyrus, right frontal pole, and right middle temporal gyrus ( P < 0.001), compared with healthy subjects. Notably, in patients with AD, the GM volume in right middle temporal gyrus was negatively associated with both EASI score and proinflammatory cytokines (sIL-2R [soluble interleukin 2 receptor] and TNF-α receptor-1), whereas the GM volume in left precentral gyrus was negatively associated with both EASI score and proinflammatory cytokines (sIL-2R and CRP). Conclusion: Patients with AD demonstrated significant brain GM volume reduction in many brain regions, which is related to disease severity and proinflammatory cytokines.
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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) | 0.002 | 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".