Tailoring the Surface of a Gene Delivery Vector with Carboxymethylated Dextran: A Systematic Analysis
Bibliographic record
Abstract
Polymeric nanocarriers are attractive nonviral vectors for gene delivery purposes in vivo. For such applications, numerous physiological and subcellular bottlenecks have to be overcome. In that endeavor, each structural feature of nanocarriers can be optimized with respect to its corresponding challenges. Here, we focused on the interface between a model gene delivery nanocarrier and relevant constituents of the physiological environment. We screened a library of carboxymethylated dextrans (CMD) for the electrostatic coating of positively charged nanocarriers. We evaluated the jointed influence of the CMD molecular weight and charge density upon nanocarrier coating with respect to DNase, small ions, plasma proteins, red blood cells, and target cells. A total of 4 out of 26 CMD coated nanocarriers successfully passed every screening assay, but did not yield increased reporter gene expression in target cells compared to uncoated nanocarriers. The fine-tuning of CMD for nanocarrier coating yielded a relevant shortlist of candidates that will be further tested in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".