Bioinspired zwitterionic polyphosphoester modified porous silicon nanoparticles for efficient oral insulin delivery
Bibliographic record
Abstract
The intestinal epithelial and mucus barriers on the gastrointestinal tract limit the bioavailability of oral protein or peptide drugs. Therefore, efficient mucus permeability and cellular internalization are required properties for oral delivery systems. To overcome these two obstacles, porous silicon nanoparticles were modified with poly (pyridyl disulfide ethylene phosphate/sulfobetaine) polymers to make P(PyEP-g-SBm)n-AmPSiNPs (m = 0.1, 0.2, 0.3 and n = 10, 20, 30) nanoparticles (NPs). The insulin-loaded P(PyEP-g-SB)-AmPSiNPs showed favorable stability and good biocompatibility in vitro. The zwitterionic dodecyl sulfobetaine (SB) coated nanoparticles improved the mucus permeability. P(PyEP-g-SBm)20 with the optimal conjugated ratio (m = 0.3) of SB units was determined by evaluating the mucus diffusion rate of NPs. The cellular uptake of P(PyEP-g-SB0.3)n-AmPSiNPs (n = 10, 20, 30) was much higher than AmPSiNPs in the presence of inhibitors (N-acetylcysteine solution and sodium chlorate) (p < 0.01) due to the enhanced charge shielding effect of P(PyEP-g-SB) modification. The P(PyEP-g-SB0.3)20-AmPSiNPs showed about 1.4-1.7 fold increase in the apparent permeability of insulin across Caco-2/HT-29-MTX cell monolayers, compared to AmPSiNPs (p < 0.01). Finally, the in vivo study showed that insulin-loaded P(PyEP-g-SB0.3)20-AmPSiNPs generated 20% reduction of the blood glucose level with an 2-fold increase in oral bioavailability. These suggested that zwitterionic polyphosphoester modified porous silicon nanoparticles, which were of enhanced mucus permeability and cellular internalization, represent a promising carrier for oral delivery of peptide and protein.
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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".