Activated Platelets Induce Endothelial Cell Inflammatory Response in Psoriasis via COX-1
Bibliographic record
Abstract
Objective: Patients with psoriasis have impaired vascular health and increased cardiovascular disease (CVD). Platelets are key players in the pathogenesis of vascular dysfunction in cardiovascular disease and represent therapeutic targets in cardiovascular prevention. The object of this study was to define the platelet phenotype and effector cell properties on vascular health in psoriasis and evaluate whether aspirin modulates the platelet-induced phenotype. Approach and Results: Platelets from psoriasis patients (n=45) exhibited increased platelet activation (relative to age- and gender-matched controls, n=18), which correlated with psoriasis skin severity. Isolated platelets from psoriasis patients demonstrated a 2- to 3-fold ( P <0.01) increased adhesion to human aortic endothelial cells and induced proinflammatory transcriptional changes, including upregulation of IL 8 (interleukin 8), IL1β , and Cox (cyclooxygenase)-2 Platelet RNA sequencing revealed an interferon signature and elevated expression of COX-1 , which correlated with psoriasis disease severity ( r =0.83, P =0.01). In a randomized trial of patients with psoriasis, 2 weeks of 81 mg low-dose aspirin, a COX-1 inhibitor, reduced serum thromboxane (Tx) B 2 and reduced brachial vein endothelial proinflammatory transcript expression >70% compared with the no-treatment group ( P <0.01). Improvement in brachial vein endothelial cell inflammation significantly correlated with change in serum TxB 2 ( r =0.48, P =0.02). Conclusions: In patients with psoriasis, platelets are activated and induce endothelial cell inflammation. Low-dose aspirin improved endothelial cell health in psoriasis via platelet COX-1 inhibition. These data demonstrate a previously unappreciated role of platelets in psoriasis and endothelial cell inflammation and suggests that aspirin may be effective in improving vascular health in patients with psoriasis. Registration: URL: http://www.clinicaltrials.gov . Unique identifier: NCT03228017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".