Cross-linked Histone as a Nanocarrier for Gut Delivery of Hydrophobic Cargos
Bibliographic record
Abstract
Delivering hydrophobic molecules through the intestine can be challenging due to limited cargo solubility and the harsh biochemical environment of the stomach. Here, we show that a protein-based nanocarrier system based on the abundant protein histone and the natural cross-linker genipin can deliver hydrophobic cargos, such as dyes and therapeutic molecules, through the gastrointestinal tract. Using hydrophobic near-infrared dyes as model cargos, a panel of potential protein carriers was screened, and histone was identified as the one with the best loading capability. The resulting nanoparticles had a positive ζ potential and were mucoadhesive. Cross-linking of the amine-rich nanocarrier with genipin was particularly effective relative to other proteins and increased the stability of the system during incubation with pepsin. Cross-linking was required for successful delivery of a hydrophobic dye to the colon of mice after oral gavage. To assess the platform for therapeutic delivery, another hydrophobic model compound, curcumin, was delivered using cross-linked histone nanoparticles in a murine colitis model and significantly alleviated the disease. Taken together, these results demonstrate that histone is a cationic, mucoadhesive, and cross-linkable protein nanocarrier that can be considered for oral delivery.
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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".