Chitosan-coated nano-liposomes for the oral delivery of berberine hydrochloride
Bibliographic record
Abstract
Berberine hydrochloride (BH) possesses various pharmacological properties including anticancer; unfortunately, it has low oral bioavailability and potential side effects for its parenteral administration. Nanoscale delivery carriers can increase the oral bioavailability of BH. Chitosan has interesting biopharmaceutical properties such as nontoxicity, biocompatibility, biodegradability, and mucoadhesiveness, and the ability to open epithelial tight junctions. This study aims to engineer a chitosan-coated nano-liposomal carrier for the oral delivery of BH. The engineered formulation had a size in the nanoscale range. Chitosan-coated nano-liposomes displayed better stability and slower BH release in the simulated gastrointestinal (GI) environment as compared to the uncoated ones. All values of pharmacokinetic analysis for chitosan-coated nano-liposomes were higher than for uncoated ones. These findings demonstrate that chitosan-coated nano-liposomes are more efficient than uncoated ones for the oral delivery of BH. It can be concluded that the stability and delayed BH release in the simulated GI environment were improved with engineered chitosan-coated nano-liposomes. Moreover, since desirable in vitro and in vivo characteristics were achieved, they are promising release devices for the oral delivery of BH increasing the bioavailability of the drug.
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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".