The role of interleukins in vitiligo: a systematic review
Bibliographic record
Abstract
Vitiligo is a disorder of the skin that causes depigmentation and asymptomatic macules whose exact cause is still unclear. Although its aetiology is not fully elucidated, the main theory of its pathomechanism is that it is associated with the autoimmune process. There is few summarized information about the role of inflammatory mediators, as interleukins, in vitiligo, so our aim was to present a systematic review of the role of interleukins in vitiligo, focusing on interleukins. In this review, we included all studies assessing interleukin levels in vitiligo patients conducted up to June 2017. Quality assessment of these studies was performed using the Newcastle-Ottawa Scale (NOS). The interleukins mainly involved were IL-2, IL-4, IL-6, IL-10 and IL-17. The studies highlight the crucial role of IL-17 in the onset and progression of the disease, and its synergistic action with IL-2, IL-6 and IL-33. Dysregulated levels of the interleukins were also correlated with the stage of disease, the affected skin surface area, and indicated as the main factor for lymphocyte infiltration found in depigmented regions. These findings illustrate the growing need for new therapies targeting vitiligo and further research into the role of interleukins as an area of particular interest.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".