Physical Characterization of Gemini Surfactant-Based Synthetic Vectors for the Delivery of Linear Covalently Closed (LCC) DNA Ministrings
Bibliographic record
Abstract
In combination with novel linear covalently closed (LCC) DNA minivectors, referred to as DNA ministrings, a gemini surfactant-based synthetic vector for gene delivery has been shown to exhibit enhanced delivery and bioavailability while offering a heightened safety profile. Due to topological differences from conventional circular covalently closed (CCC) plasmid DNA vectors, the linear topology of LCC DNA ministrings may present differences with regards to DNA interaction and the physicochemical properties influencing DNA-surfactant interactions in the formulation of lipoplexed particles. In this study, N,N-bis(dimethylhexadecyl)-α,ω-propanediammonium(16-3-16)gemini-based synthetic vectors, incorporating either CCC plasmid or LCC DNA ministrings, were characterized and compared with respect to particle size, zeta potential, DNA encapsulation, DNase sensitivity, and in vitro transgene delivery efficacy. Through comparative analysis, differences between CCC plasmid DNA and LCC DNA ministrings led to variations in the physical properties of the resulting lipoplexes after complexation with 16-3-16 gemini surfactants. Despite the size disparities between the plasmid DNA vectors (CCC) and DNA ministrings (LCC), differences in DNA topology resulted in the generation of lipoplexes of comparable particle sizes. The capacity for ministring (LCC) derived lipoplexes to undergo complete counterion release during lipoplex formation contributed to improved DNA encapsulation, protection from DNase degradation, and in vitro transgene delivery.
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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 | 0.000 |
| 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 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".