Cellular Trafficking and Subcellular Interactions of Cationic Gene Delivery Nanomaterials
Bibliographic record
Abstract
Various cationic nanobiomaterials have been widely used as gene delivery nanosystems (GDNSs) in vitro and in vivo. Various cellular machineries are involved in trafficking of GDNSs, whose surface functional moieties and architectural properties confer great potential to interact with cell membranes and subcellular biomolecules. It appears such intrinsic inadvertent biological functionalities may impact the outcome of the biomedical applications of these nanobiomaterials. Various advanced materials used as GDNSs may display selective phenotypic effects in target cells/tissues as a result of initiation of various signaling pathways perhaps due to its cellular interactions with plasma cell membranes and/or intracellular compartments including genetic materials. Thus, better understanding about cellular/molecular impacts of GDNSs may maximize their clinical outcomes and accordingly minimize their inevitable undesired consequences. The main focus of this review is based on the cellular trafficking and interactions of cationic gene delivery nanobiomaterials with target cells or subcellular compartments.
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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".