Bibliographic record
Abstract
Autoimmune diseases often present with cutaneous symptoms that contribute to dysfunction, disfigurement, and in many cases, reduced quality-of-life. Unfortunately, treatment options for many autoimmune skin diseases are limited. Local and systemic corticosteroids remain the current standard-of-care but are associated with significant adverse effects. Hence, there is an unmet need for novel therapies that block molecular drivers of disease in a local and/or targeted manner. Granzyme B (GzmB) is a serine protease with known cytotoxic activity and emerging extracellular functions, including the cleavage of cell-cell junctions, basement membranes, cell receptors, and other structural proteins. While minimal to absent in healthy skin, GzmB is markedly elevated in alopecia areata, interface dermatitis, pemphigoid disease, psoriasis, systemic sclerosis, and vitiligo. This review will discuss the role of GzmB in immunity, blistering, apoptosis, and barrier dysfunction in the context of autoimmune skin disease. GzmB plays a causal role in the development of pemphigoid disease and carries diagnostic and prognostic significance in cutaneous lupus erythematosus, vitiligo, and alopecia areata. Taken together, these data support GzmB as a promising therapeutic target for autoimmune skin diseases impacted by impaired barrier function, inflammation, and/or blistering.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".