Poly(amidoamine) dendrimer complexes as a platform for gene delivery
Bibliographic record
Abstract
INTRODUCTION: Gene therapy is one of the most effective ways to treat major infectious diseases, cancer and genetic disorders. It is based on several viral and non-viral systems for nucleic acid delivery. The number of clinical trials based on application of non-viral drug and gene delivery systems is rapidly increasing. AREAS COVERED: This review discusses and summarizes recent advances in poly(amidoamine) dendrimers as effective gene carriers in vitro and in vivo, and their advantages and disadvantages relative to viral vectors and other non-viral systems (liposomes, linear polymers) are considered. EXPERT OPINION: In this regard, dendrimers are non-immunogenic and have the highest efficiency of transfection among other non-viral systems, and none of the drawbacks characteristic for viral systems. The toxicity of dendrimers both in vitro and in vivo is an important question that has been addressed on many occasions. Several non-toxic and efficient multifunctional dendrimer-based conjugates for gene delivery, along with modifications to improve transfection efficiency while decreasing cytotoxicity, are discussed. Twelve paradigms that affected the development of dendrimer-based gene delivery are described. The conclusion is that dendrimers are promising candidates for gene delivery, but this is just the beginning and further studies are required before using them in human 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".