Enhanced oral bioavailability of baicalein using phospholipid-coated nanoparticles: A novel drug delivery system
Bibliographic record
Abstract
Phospholipid-coated nanoparticle formulation of Baicalein demonstrated superior pharmacokinetic properties, including slower absorption, prolonged circulation time, and increased bioavailability compared to free Baicalein. This formulation holds significant promise for enhancing the oral bioavailability and therapeutic efficacy of Baicalein, particularly for chronic diseases where sustained drug release is beneficial. Further research should focus on the long-term safety, efficacy, and clinical applicability of this nanoparticle formulation, including evaluations in animal models and eventual clinical trials. Additionally, exploring other lipid-based nanoparticle formulations could further improve the bioavailability and therapeutic outcomes of Baicalein and similar drugs with poor solubility. Characterized phospholipid-coated nanoparticles for the delivery of Baicalein, a flavonoid with limited bioavailability. The formulation process, utilizing the emulsification technique, allowed the formation of stable nanoparticles with optimal encapsulation efficiency (88%) and drug loading capacity (6.5%). The optimization of lecithin concentration (3%) resulted in the highest encapsulation and drug loading, demonstrating the significant role of lipid concentration in the formulation of effective drug delivery systems.
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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".