Biomarkers in Pemphigus Vulgaris: A Systematic Review
Bibliographic record
Abstract
Introduction: Pemphigus vulgaris (PV) is a rare intraepidermal blistering disease that is potentially life-threatening due to risk of infection and failure of skin barrier function. The identification of biomarkers has the potential to provide diagnostic utility and identify new therapeutic targets. The objective of this systematic review is to identify all potentially relevant PV biomarkers, categorize them, and identify trends to determine the involvement of T-cell-mediated, B-cell-1mediated, and innate immune-mediated pathways in PV pathogenesis. Methods/Results: Medline and Embase databases were searched according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, resulting in the inclusion of 66 studies that reported on a total of 2463 patients and 146 unique biomarkers. Biomarkers were categorized into T-cell-mediated, B-cell-mediated, and innate immune system pathways. The most notable biomarkers trends include elevations in IL-4, IL-6, IL-17A, anti-Dsg1/3 autoantibodies, and a reduction in T reg cells and FOXP3. Conclusion: The results of this review support current theories of PV pathogenesis, with increased T h 2 activity, increased T h 17 activity, decreased T reg activity, and production of anti-Dsg1/3 autoantibodies being observed. Targeting of IL-4 and IL-6 may provide therapeutic benefit. However, more research is required to validate biomarkers for clinical utility and assess viability as therapeutic targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".