Virus-Based RNA Silencing Agents and Virus-Derived Expression Vectors as Gene Therapy Vehicles
Bibliographic record
Abstract
BACKGROUND: In consideration of recent developments in understanding the genomics and proteomics of viruses, the use of viral DNA / RNA sequences as well as their gene expression schemes, have found new in-roads towards the prognosis and therapy of diseases. Correspondingly, the sphere of the patenting scenario has expanded significantly. OBJECTIVES: The current review addresses patented inventions concerning the use of virus sequences as gene silencing machineries and inventions concerning the generation and application of viral sequences as expression vectors. Furthermore, this review also discusses the employment of these patents for clinical, agricultural and biotechnological applications. METHOD: Considering these objectives, the Delphion Research Intellectual Property Network database was searched using keywords such as "gene silencing", "engineered viruses" and "expression vectors" and descriptions of recent patents on the said topics were discussed. CONCLUSION: Despite several recent advances in the use of viruses as disease therapy vehicles and biotechnological vectors, these developments have yet to be proven effective in practice, in clinical and field trials.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".