Physicochemical Characterization of Chrysin‐Derivative‐Loaded Nanostructured Lipid Carriers with Special Reference to Anticancer Activity
Bibliographic record
Abstract
Abstract Homologues long‐chain chrysin derivatives (LCD, C n: 8–18) were synthesized and incorporated into nanostructured lipid carriers (NLC) with the aim to treat human neuroblastoma. Mutual miscibility and attractive interactions among the NLC components, namely tripalmitin (TP), cetyl palmitate (CP), oleic acid (OA), and the chrysin (CHR) derivatives (LCD) at the air–water interface were assessed by the Langmuir monolayer approach. Optimum combination for the NLC formulations was found to be 2:2:1 (M/M/M) for TP/CP/OA, respectively. NLC formulations, both in the absence and presence of LCD, were characterized by combined dynamic light scattering, electron microscopy, atomic force microscopy, and differential scanning calorimetry. The size and zeta potential of the NLC formulations were found in the range 200–350 nm and −12 to −18 mV, respectively. Encapsulation efficiency and release kinetics of CHR and LCD when loaded into NLC were also evaluated. LCD exhibited maximum incorporation, drug‐loading capacity, and sustained release because of its enhanced hydrophobicity. Superior incorporation efficiency and sustained‐release profile of LCD were able to enhance their anticancer activity against human neuroblastoma cell lines, compared to CHR, making them promising agents in combating cancer.
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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".