Systems immunology integrates the complex endotypes of recessive dystrophic epidermolysis bullosa
Bibliographic record
Abstract
Endotypes are characterized by the immunological, inflammatory, metabolic, and remodelling pathways that explain the mechanisms underlying the clinical presentation (phenotype) of a disease. Recessive dystrophic epidermolysis bullosa (RDEB) is a severe blistering disease caused by COL7A1 pathogenic variants. Although underscored by animal studies, the endotypes of human RDEB are poorly understood. To fill this gap, we apply systems immunology approaches using single-cell high-dimensional techniques to capture the signature of peripheral immune cells and the diversity of metabolic profiles in RDEB adults, sampled outside of any opportunistic infection and active cancer. Our study, demonstrates the particular inflammation and immunity characteristics of RDEB adults, with activated / effector T and dysfunctional natural killer cell signatures, concomitant with an overall pro-inflammatory lipid signature. Artificial intelligence prediction models and principal component analysis stress that RDEB is not solely confined to cutaneous issues but has complex systemic endotypes marked by immune dysregulation and hyperinflammation. By characterising the phenotype-endotype association in RDEB adults, our study lays the groundwork for translational interventions that could by lessening inflammation, alleviate the everlasting suffering of RDEB patients, while awaiting curative genetic therapies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".