Intracellular Trafficking and Decondensation Kinetics of Chitosan–pDNA Polyplexes
Bibliographic record
Abstract
The transfection efficiency (TE) of chitosan-plasmid DNA (pDNA) polyplexes can be critically modulated by the polymer degree of deacetylation (DDA) and molecular weight (MW). This study was performed to test the hypothesis that the TE dependence on chitosan MW and DDA is related to the polyplex stability, hence their intracellular decondensation/unpacking kinetics. Major barriers to nonviral gene transfer were studied by image-based quantification. Although uptake increased with increased DDA, it did not appear to be a structure-dependent process affecting TE, nor was nuclear entry. Colocalization analysis showed that all chitosans trafficked through lysosomes with similar kinetics. Fluorescent resonant energy transfer (FRET) analysis revealed a distinct relationship between TE and polyplex dissociation rate. The most efficient chitosans showed an intermediate stability and a kinetics of dissociation, which occurred in synchrony with lysosomal escape. In contrast, a rapid dissociation before lysosomal escape was found for the inefficient low DDA chitosan whereas the highly stable and inefficient complex formed by a high MW and high DDA chitosan did not dissociate even after 24 hours. This study identified that the kinetics of decondensation in relation to lysosomal escape was a most critical structure-dependent process affecting the TE of chitosan polyplexes.
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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 | 0.001 |
| 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".