Gene expression profiling in psoriatic scalp hair follicles: clobetasol propionate shampoo 0.05% normalizes psoriasis disease markers
Bibliographic record
Abstract
BACKGROUND: Clobetasol propionate shampoo is effective and safe in treatment of scalp psoriasis (SP). Gene expression profiling of psoriatic skin biopsies led to the identification of numerous disease-related genes. However, it remained unknown whether the gene expression profile of hair follicles of SP patients was also affected. OBJECTIVES: To determine whether psoriasis-related genes are differentially regulated in the hair follicles of SP patients and whether the modulation of these genes can be correlated with clinical severity scores. METHODS: A single arm, open study was conducted in three centres. SP patients received daily treatment with clobetasol propionate shampoo. At Baseline, Weeks 2 and 4, investigators assessed clinical severity parameters and collected scalp hair follicles in anagen phase. Total RNA extracted from hair follicles was used to determine the expression level of 44 genes, which were reported previously to be upregulated in the skin of psoriasis patients. RESULTS: RNA of good quality and sufficient quantity was obtained from hair follicles of psoriasis patients and healthy volunteers (HV). The expression level of 10 inflammation-related genes was significantly increased in psoriatic hair follicles. The patient's exploratory transcriptomic score, defined as the mean fold modulation of these 10 genes compared with HV, correlated with clinical severity scores. Clobetasol propionate shampoo was effective in decreasing both the exploratory transcriptomics and the clinical severity scores. CONCLUSION: Hair follicles of SP patients are affected by the inflammatory process. The change in the expression level of inflammation-related genes correlates with the severity of the disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".