Serpin Treatment Suppresses Inflammatory Vascular Lesions in Temporal Artery Implants (TAI) from Patients with Giant Cell Arteritis
Bibliographic record
Abstract
Giant cell arteritis (GCA) and Takayasu's disease are inflammatory vasculitic syndromes (IVS) causing sudden blindness and widespread arterial obstruction and aneurysm formation. Glucocorticoids and aspirin are mainstays of treatment, predominantly targeting T cells. Serp-1, a Myxomavirus-derived serpin, blocks macrophage and T cells in a wide range of animal models. Serp-1 also reduced markers of myocardial injury in a Phase IIa clinical trial for unstable coronary disease. In recent work, we detected improved survival and decreased arterial inflammation in a mouse Herpesvirus model of IVS. Here we examine Serp-1 treatment of human temporal artery (TA) biopsies from patients with suspected TA GCA arteritis after implant (TAI) into the aorta of immunodeficient SCID (severe combined immunodeficiency) mice. TAI positive for arteritis (GCApos) had significantly increased inflammation and plaque when compared to negative TAI (GCAneg). Serp-1 significantly reduced intimal inflammation and CD11b+ cell infiltrates in TAI, with reduced splenocyte Th1, Th17, and Treg. Splenocytes from mice with GCApos grafts had increased gene expression for interleukin-1 beta (IL-1β), IL-17, and CD25 and decreased Factor II. Serp-1 decreased IL-1β expression. In conclusion, GCApos TAI xenografts in mice provide a viable disease model and have increased intimal inflammation as expected and Serp-1 significantly reduces vascular inflammatory lesions with reduced IL-1β.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".