Trained immunity in the pathogenesis of vitiligo
Bibliographic record
Abstract
Vitiligo is caused by an autoimmune reaction against melanocytes leading to melanocyte loss. The cause of vitiligo is an interaction between genetic susceptibility and environmental factors. Both the adaptive immune system-through cytotoxic CD8+ T cells and melanocyte specific antibodies-and the innate immune system are involved in these immune processes in vitiligo. While recent data stressed the importance of innate immunity in vitiligo, the question remains why vitiligo patients' immune response becomes overly activated. Could a long-term increase in innate memory function, described as trained immunity after vaccination and in other inflammatory diseases, play a role as an enhancer and continuous trigger in the pathogenesis of vitiligo? After exposure to certain stimuli, innate immune system is able to show an enhanced immunological response to a secondary trigger, indicating a memory function of the innate immune system, a concept termed trained immunity. Trained immunity is regulated by epigenetic reprogramming, including histone chemical modifications and changes in chromatin accessibility that cause sustained changes in the transcription of specific genes. In responses to an infection, trained immunity is beneficial. However, there are indications of a pathogenic role of trained immunity in inflammatory and autoimmune diseases, with monocytes presenting features of a trained phenotype, resulting in increased cytokine production, altered cell metabolism through mTOR signaling, and epigenetic modifications. This hypothesis paper focusses on vitiligo studies that have shown these indications, suggesting the involvement of trained immunity in vitiligo. Future studies focusing on metabolic and epigenetic changes in innate immune cell populations in vitiligo could help in elucidating the potential role of trained immunity in vitiligo pathogenesis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".