Granzyme K contributes to endothelial microvascular damage and leakage during skin inflammation
Bibliographic record
Abstract
BACKGROUND: Granzyme K (GzmK) is a serine protease with minimal presence in healthy tissues while abundant in inflamed tissues. Initially thought to play an exclusive role in immune-mediated cell death, extracellular GzmK can also promote inflammation. OBJECTIVES: To evaluate the role of GzmK in the pathogenesis of atopic dermatitis (AD), the most common inflammatory skin disease. METHODS: A panel of human AD and control samples was analysed to determine if GzmK is elevated. Next, to determine a pathological role for GzmK in AD-like skin inflammation, oxazolone-induced dermatitis was induced in GzmK-/- and wild-type (WT) mice. RESULTS: In human lesional AD samples, there was an increase in the number of GzmK+ cells compared with healthy controls. GzmK-/- mice exhibited reduced overall disease severity characterized by reductions in scaling, erosions and erythema. Surprisingly, the presence of GzmK did not notably increase the overall pro-inflammatory response or epidermal barrier permeability in WT mice; rather, GzmK impaired angiogenesis, increased microvascular damage and microhaemorrhage. Mechanistically, GzmK contributed to vessel damage through cleavage of syndecan-1, a key structural component of the glycocalyx, which coats the luminal surface of vascular endothelia. CONCLUSIONS: GzmK may provide a potential therapeutic target for skin conditions associated with persistent inflammation, vasculitis and pathological angiogenesis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".