Tailoring N, N, N‐trimethyl chitosan nanospheres as an efficient drug carrier to overcome endothelial barrier
Bibliographic record
Abstract
Abstract Endothelial barriers, integral components of tissue barriers, are densely packed with adherens junction and tight junction proteins, hindering the passage of drugs to target sites. Herein, a novel nanosphere comprising N, N, N‐trimethylated chitosan (TMC), with the size of 51 ± 8 nm and zeta potential of 13.2 ± 5.3 mV, has been engineered as a drug carrier to facilitate the transport of hydrophobic drugs, that is, curcumin, across endothelial barriers efficiently. The effects of pH value and temperature on the release kinetics of curcumin from TMC nanospheres have been studied. In addition, the transportation of TMC nanospheres across an in vitro endothelial barrier was evaluated over a 72‐h period. The integrity of the in vitro endothelial barrier was assessed before and after the transport of TMC nanospheres by using transepithelial electrical resistance measurements and immunofluorescence staining of tight junction proteins. The results reveal that TMC nanospheres significantly enhance the transport of curcumin across the endothelial barrier, with approximately 30% of curcumin released as compared to less than 3.3% across the barrier without the assistance of TMC nanospheres. This investigation underscores the potential of small‐sized TMC nanospheres to enhance the transport of hydrophobic drugs across endothelial barriers.
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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".