Recent Patents Involving Virus Nucleotide Sequences; Host Defense, RNA Silencing and Expression Vector Strategies
Bibliographic record
Abstract
Improved knowledge of the molecular biology of viruses, including recent gains in virus sequence data analysis, has greatly contributed to recent innovations in medical diagnostics, therapeutics, drug development and other related areas. Virus sequences have been used for the development of vaccines and antiviral agents to block the spread of viral infections, as well as to target and battle chronic diseases such as cancer. Virus sequences are now routinely employed in a wide array of RNA silencing technologies. Viruses can also be engineered into expression vectors which in turn can be used as protein production platforms as well as delivery vehicles for gene therapies. This review article outlines a number of patents that have been recently issued with respect to virus sequence data and describes some of their biotechnological applications. Keywords: Oncolytic virus, gene silencing, virus expression vector, vaccine, antiviral agents, Virus Nucleotide, Sequences, Host Defense, RNA Silencing, Expression Vector Strategies, Viruses to Treat Diseases, Kill Cancer Cells, cytokines, hepatitis C, Prodrugs of HIV, nucleoside phosphates, neoplasms
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".