Targeted Nanostructured Lipid Carrier for Brain Delivery of Artemisinin: Design, Preparation, Characterization, Optimization and Cell Toxicity
Bibliographic record
Abstract
In the present study, a transferrin-conjugated nanostructured lipid carrier (TF-NLCs) for brain delivery of artemisinin (ART) was developed. ART-loaded NLCs (ART-NLCs) were prepared using solvent evaporation method and the impact of various formulation or process variables on the responses were assessed using a Taguchi design. Optimized ART-NLC was then coupled with transferrin as targeting ligand and its in vitro cytotoxicity was investigated against U-87MG brain cancer cell line. As a result, the following values are suggested by the software to prepare the optimized formulation: 20 mg Compritol®, 0.25% Tween 80, 5 mg oleic acid, 2.5 mL dichloromethane and 4 min sonication. Mean particle size (PS), zeta potential (ZP), polydispersity index (PDI), entrapment efficiency (EE), mean release time (MRT) of adopted formulation were confirmed to be 145 ± 12.5 nm, 24.3 ± 1.5 mV, 0.513 ± 0.021, 82.3 ± 7.3 % and 24.0 ± 1.1 h, respectively. Following conjugation of optimized ART-NLCs with TF, PS and MRT were increased, while ZP, and EE were decreased significantly. TF-ART-NLCs showed higher cytotoxic activity compared to non-targeted NLCs and free drug. These results indicated that the TF-ART-NLCs could potentially be exploited as a delivery system for anticancer and antimalarial drug ART in brain tumors and malaria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".