Nucleocytoplasmic Transport of Plasmid DNA: A Perilous Journey from the Cytoplasm to the Nucleus
Bibliographic record
Abstract
Nonviral vectors represent a promising approach for the safe delivery of therapeutic DNA in genetic and acquired human diseases. Before synthetic vector systems can be used for clinical applications, their limited efficacy must be addressed. At the cellular level, successful gene transfer is dependent on several additional factors including DNA uptake, release from the DNA-vector complex, and nucleocytoplasmic transport. This paper reviews the major metabolic and physical impediments that plasmid DNA vectorized by synthetic vectors encounters between the cytosol and the nucleus. Plasmid DNA that escapes the endolysosomal compartment encounters the diffusional and metabolic barriers of the cytoplasm, reducing the number of intact plasmids that reach the nuclear envelope. Nuclear translocation of DNA requires either the disassembly of the nuclear envelope during cell division or active nuclear transport via the nuclear pore complex. In the nucleus, plasmid DNA is relatively stable, but its transcription and its fate during cell division are still debated. A better understanding of the cellular and molecular basis of nonviral gene transfer during nucleocytoplasmic trafficking may provide strategies to overcome those obstacles that limit the efficiency of nonviral gene delivery. We review some of the current methods of gene transfer mediated by synthetic vectors, highlighting systems that exploit our actual knowledge of the nucleocytoplasmic transport of plasmid DNA.
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".