A Novel Micro-Linear Vector for In Vitro and In Vivo Gene Delivery and Its Application for EBV Positive Tumors
Bibliographic record
Abstract
BACKGROUND: The gene delivery vector for DNA-based therapy should ensure its transfection efficiency and safety for clinical application. The Micro-Linear vector (MiLV) was developed to improve the limitations of traditional vectors such as viral vectors and plasmids. METHODS: The MiLV which contained only the gene expression cassette was amplified by polymerase chain reaction (PCR). Its cytotoxicity, transfection efficiency in vitro and in vivo, duration of expression, pro-inflammatory responses and potential application for Epstein-Barr virus (EBV) positive tumors were evaluated. RESULTS: Transfection efficiency for exogenous genes transferred by MiLV was at least comparable with or even greater than their corresponding plasmids in eukaryotic cell lines. MiLV elevated the expression and prolonged the duration of genes in vitro and in vivo when compared with that of the plasmid. The in vivo pro-inflammatory response of MiLV group was lower than that of the plasmid group. The MEKK1 gene transferred by MiLV significantly elevated the sensitivity of B95-8 cells and transplanted tumor to the treatment of Ganciclovir (GCV) and sodium butyrate (NaB). CONCLUSIONS: The present study provides a safer, more efficient and stable MiLV gene delivery vector than plasmid. These advantages encourage further development and the preferential use of this novel vector type for clinical gene therapy studies.
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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".