Gene Therapy with Plasmids Encoding Cytokine- or Cytokine Receptor-IgG Chimeric Proteins
Bibliographic record
Abstract
Cytokine therapy can influence the outcome of autoimmune diseases by altering either T-helper 1 (Th1) vs T-helper 2 (Th2) balance or antigen-presenting cell (APC) function, or by shifting the balance between inflammatory and regulatory cytokines (). However, cytokine and soluble cytokine-receptor therapy have been limited by the short half-life (T1/2) of these proteins and the necessity to administer relatively large boluses of recombinant proteins (). This results in transient high systemic levels and, in the case of cytokines, toxicity and poor therapeutic efficiency. The in vivo blockade of cytokine function by monoclonal antibody therapy, although feasible, has also faced therapeutic limitations (). Moreover, the isolation and production of highly purified and stable therapeutic proteins is laborious and expensive. Gene therapy has significant advantages, allowing long-term and relatively constant delivery of cytokines or their receptors at therapeutic levels. This can be accomplished with viral gene therapy vectors, as well as plasmid DNA expression vectors (nonviral approach). Our laboratory has been particularly interested in the delivery of vectors encoding cytokines and cytokine receptors for the prevention or treatment of autoimmune diseases.
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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) | 0.000 | 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) | 0.000 | 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".