Anticytokine gene therapy of autoimmune diseases
Bibliographic record
Abstract
Viral and nonviral gene therapy vectors have been successfully employed to deliver inflammatory cytokine inhibitors (anticytokines), or anti-inflammatory cytokines, such as transforming growth factor beta-1 (TGF-beta 1), which protect against experimental autoimmune diseases. These vectors carry the relevant genes into a variety of tissues, for either localised or systemic release of the encoded protein. Administration of cDNA encoding soluble IFN-gamma receptor (IFN-gamma R)/IgG-Fc fusion proteins, soluble TNF-alpha receptors, or IL-1 receptor antagonist (IL-1ra), protects against either lupus, various forms of arthritis, autoimmune diabetes, or other autoimmune diseases. These inhibitors, unlike many cytokines, have little or no toxic potential. Similarly, TGF-beta 1 gene therapy protects against numerous forms of autoimmunity, though its administration entails more risk than anticytokine therapy. We have relied on the injection of naked plasmid DNA into skeletal muscle, with or without enhancement of gene transfer by in vivo electroporation. Expression plasmids offer interesting advantages over viral vectors, since they are simple to produce, non-immunogenic and nonpathogenic. They can be repeatedly administered and after each treatment the encoded proteins are produced for relatively long periods, ranging from weeks to months. Moreover, soluble receptors which block cytokine action, encoded by gene therapy vectors, can be constructed from non-immunogenic self elements that are unlikely to be neutralised by the host immune response (unlike monoclonal antibodies [mAbs]), allowing long-term gene therapy of chronic inflammatory disorders.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".