Recognition of Virus Infection and Innate Host Responses to Viral Gene Therapy Vectors
Bibliographic record
Abstract
The innate immune and inflammatory response represents one of the key stumbling blocks limiting the efficacy of viral-based therapies. Numerous human diseases could be corrected or ameliorated if viruses were harnessed to safely and effectively deliver therapeutic genes to diseased cells and tissues in vivo. Recent studies have shown that host cells recognize viruses using an elaborate network of sensor proteins localized at the plasma membrane, in endosomes, or in the cytosol. Three classes of sensors have been implicated in sensing viruses in mammalian cells-Toll-like receptors (TLRs), retinoid acid-inducible gene (RIG)-I-like receptors (RLRs), and nucleotide oligomerization domain (NOD)-like receptors (NLRs). The interaction of virus-associated nucleic acids with these sensor molecules triggers a signaling cascade that activates the principal host defense program aimed to limit or eliminate virus infection and restore tissue homeostasis. In addition, recent data strongly suggest that host cells can mount innate immune responses to viruses without prior recognition of their nucleic acids. To deliver therapeutic genes into the nuclei of diseased cells, viral gene therapy vectors must be efficient at penetrating either the plasma or endosomal membrane. The therapeutic use of high numbers of virus particles disturbs cellular homeostasis, triggering cell damage and stress pathways, or "sensing of modified self". Accumulating data indicate that the sensing of modified self might represent a powerful framework explaining the innate immune response activation by viral gene therapy vectors.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".