An <i>E. coli</i> -based platform for the production and assembly of anellovirus vectors
Bibliographic record
Abstract
Abstract Gene therapy offers immense potential for treating various diseases, including cancer, immunodeficiencies, and cardiovascular conditions. The efficacy of gene therapy (GT) largely depends on the vector used for gene delivery. Viral vectors, while effective, pose risks including insertional mutagenesis, immune responses, and high manufacturing costs. Non-viral vectors, although safer and easier to produce, often exhibit lower transfection efficiency and weaker transgene expression. This highlights the need for novel, more efficient vectors. Among emerging strategies, bacteriophages are gaining attention as promising GT delivery vehicles due to their adaptability and safety profile. Filamentous phages like M13 have demonstrated potential as targeted gene delivery vectors. This study proposes constructing a single-stranded DNA (ssDNA) phage-based vector incorporating a eukaryotic gene cassette. By leveraging Ff phage replication mechanisms in E. coli , the study explores encapsidating ssDNA within anellovirus capsids. These small, ssDNA viruses, known for their ability to transfect diverse tissues, offer a safer alternative to conventional viral vectors. Through successful expression and assembly of anellovirus capsid proteins, ssDNA viral particles were produced ex vivo . This innovative E. coli -based anellovirus-phagemid system provides a promising, cost-effective platform for developing next-generation viral vectors in gene therapy.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".