Viral Anti-Inflammatory Reagents: The Potential for Treatment of Arthritic and Vasculitic Disorders
Bibliographic record
Abstract
Inflammatory and immune responses are inherent in the development of progressive arthritic or vasculitic disorders. Arthritis is frequently associated with accelerated forms of vasculitis; atherosclerosis being one form of accelerated vasculitis that blocks blood flow causing heart attacks and strokes. The arterial supply is central to maintaining normal articular function and acts as a conduit for inflammatory (innate) and immune (antigen dependent) cell trafficking in joints. The vasculature in some cases can become inflamed in the disease process. While treatment of severely debilitating arthritic disorders has improved, some current treatments are limited to reducing symptoms while others act as disease modifying drugs (DMARDs), but may have limited success. Many current treatments also have reported adverse side effects. Vasculitic disorders are similarly debilitating with high associated morbidity and mortality and current therapy for these disorders is only partially successful. Immune-modifying agents, which alter vascular inflammation, thus have potential for application in rheumatologic diseases. Viral immune modulating proteins reduce early arterial inflammatory responses with associated reductions in atherosclerotic plaque development and transplant rejection in a wide range of animal models. A clinical trial utilizing one such viral reagent, a secreted myxomaviral serpin, is currently in progress, assessing treatment of acute coronary syndrome, a vascular syndrome with marked up-regulation of systemic inflammatory responses. In this review we examine viral anti-inflammatory proteins as potential therapeutic reagents for arthritic and vasculopathic disorders.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".