At the Intersection of Biomaterials and Gene Therapy: Progress in Non-viral Delivery of Nucleic Acids
Bibliographic record
Abstract
Biomaterials play critical roles in technologies intended to deliver therapeutic agents in clinical settings. Recent explosion of our understanding of how cells utilize nucleic acids has garnered excitement to develop a range of older (e.g., antisense oligonucleotides, plasmid DNA and transposons) and emerging (e.g., short interfering RNA, messenger RNA and non-coding RNAs) nucleic acids agents for therapy of a wide range of diseases. This review will summarize biomaterials-centered advances to undertake effective utilization of nucleic acids for therapeutic purposes. We first review various types of nucleic acids and their unique abilities to deliver a range of clinical outcomes. Using recently emerging T-cell based therapy as a case in point, we summarize various possibilities for utilizing biomaterials to make an impact in this exciting therapeutic intervention technology, with the belief that this modality will serve as a therapeutic paradigm for other types of cellular therapies in the near future. We subsequently focus on contributions of biomaterials in emerging nucleic acid technologies, specifically focusing on the design of intelligent nanoparticles, deployment of mRNA as an alternative to plasmid DNA, long-acting (integrating) expression systems, and in vitro/in vivo expansion of engineered T-cells. We articulate the role of biomaterials in these emerging nucleic acid technologies in order to enhance the clinical impact of nucleic acids in the near future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".