Non-Viral Nucleic Acid Delivery: Key Challenges and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gene therapy holds the promise of correcting a genetic defect. It can be achieved with the introduction of a normal wild-type transgene into specific cells of the patient where the endogenous gene is underexpressing or by the introduction of a therapeutic agent, such as, antisense oligonucleotides (AON) or small interfering RNA (siRNA) to inhibit transcription and/or translation of an overexpressing endogenous gene or a cancer causing oncogene. Gene therapy has been utilized for vaccination and for the treatment of several diseases, such as, cancer, viral infections and dermatological diseases. However, there are many hurdles to overcome in developing effective gene-based therapeutics, including cellular barriers, enzymatic degradation and rapid clearance after administration. Successful transfer of nucleic acids (e.g. plasmid DNA, AON, siRNA, small hairpin RNA and micro RNA) into cells usually relies on the use of efficient carriers, commonly viral or non-viral vectors. Presently, viral vectors are more efficient than non-viral systems. However, immunogenicity, inflammatory reactions and problems associated with scale-up limit their clinical use. The ideal carriers for gene delivery should be safe and yet ensure that the DNA/RNA survives the extra- and intracellular environment and efficiently transfer to the appropriate cellular compartments. This review discusses some of the strategies that have been employed to overcome the barriers towards successful gene delivery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it