Abstract 561: Serpin Suppression of Macrophage and T Helper Cell Response in Temporal Artery Biopsy Xenograft Implants from Patients with Giant Cell Inflammatory Arteritis
Bibliographic record
Abstract
Background Glucocorticoids are a principal treatment for giant cell arteritis (GCA), with some studies reporting predominate effect on T helper17 (Th17) cell activity in GCA, with lesser effects on other inflammatory cells. Serp-1 is a 55kDa myxomaviral serpin that modifies macrophage, Th1, and Th17 responses in aortic transplants in animal models. Objectives We have analyzed the effects of a virus-derived anti-inflammatory ser ine p rotease in hibitor ( serpin ) on human temporal artery (TA) biopsies isolated from patients with suspected GCA. Methods Using a newly developed “Aortic Window Xenopatch” model, human TA biopsy sections were divided implanted as full thickness grafts into the abdominal aorta of SCID mice in parallel and tested for response to Serp-1, with and without human PBMC infusion (N = 32). Results TA sections positive for arteritis (GCA pos ) displayed significantly increased inflammatory plaque when compared to negative sections (GCA neg ). Serp-1 reduced plaque in GCA pos sections (<0.01) with concommitant reduction in Th1, and Th17 splenocytes on flow cytometry (P < 0.01), with decreased IFNγ and IL-17 gene expression by RT-PCR array analysis. Serp-1 also reduced arterial CD11b + cells after PBMC infusion with reduced TNF-α expression in spleen, but not CD3 + , CCR6 + , nor CD86 + . Splenocytes from mice with GCA pos grafts had increased interleukin-1beta (Il-1β), IL-17, and CD25 expression, while IL-1β was significantly reduced by Serp-1.Serp-1 also markedly reducedt gene expression for the FII, FXa-PAR2, tPA, uPA in splenocytes (P <0.05). Conclusions Treatment with a virus-derived serpin, significantly reduces inflammation and plaque thickness in human GCA pos TA xenograft implants with associated reductions in macrophage and Th1 and Th17 responses in mice.
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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 | 0.000 |
| 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".