Increase Alginate-Chitosan Nanoparticles Transport Efficiency Through the Epithelium by Attaching nt-PE onto Surface
Bibliographic record
Abstract
Nanotechnology has brought drug delivery system into a brand new age---the appearance of Nano delivery system.It has been widely explored in different therapeutic areas.For oral protein delivery, the application of Nano delivery system is limited by the low transport efficiency through the epithelium in the small intestine.The efficient transport of nano delivery system through epithelium requires optimized surface characteristics and specific transport pathway.In this study, chitosan and alginate are chosen for making nanoparticles, as they are bioadhensive, biodegradable and can be modified for the surface modification.The pathway of nanoparticles go across the epithelium is designed to mimic the pathway of virus invasion in the body.Study has shown non-toxic form of pseudomonas exotoxin (nt-PE) can go across the polarized cells (epithelial cells) [1].Our hypothesis is that the transport efficiency of alginate-chitosan nanoparticles through the epithelium can be increased after attaching nt-PE onto the surface.Alginate-chitosan nanoparticles were made by ion gelation, the particle size are in the size range of 210± 18 nm and the zeta potential is -7±3 mV.After attaching nt-PE onto nanoparticle surface, nanoparticles are in the size range of 192±17 nm, and the zeta potential is -10±4 mV.Nt-PE decorated nanoparticles are still in the spherical shape as indicated under Transmission Electron Microscope.This nano-delivery system was tested on Caco-2 cells, an in vitro model of the human intestinal epithelium.The transport efficiency of nt-PE modified nanoparticles are 2 fold more than the unmodified nanoparticles.Nt-PE modified nanoparticles have shown the potential to go across the epithelium.The in vivo transport study is undergoing.
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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) | 0.001 | 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".