Formulation, Characterization and Cytotoxicity Effects of Novel Thymoquinone-PLGA-PF68 Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thymoquinone has anti-cancer properties. However, its application for clinical use is limited due to its volatile characteristics. The current study aims to develop a polymeric nanoformulation with PLGA-PEG and Pluronics F68 as encapsulants to conserve thymoquinone’s (TQ) biological activity before reaching the target sites. Synthesis of nanoparticles was successfully completed by encapsulating TQ with polymeric poly (D, L-lactide-co-glycolide)-block-poly (ethylene glycol) and Pluronics F68 (TQ-PLGA-PF68) using an emulsion–solvent evaporation technique. The size and encapsulation efficiency of TQ-PLGA-PF68 nanoparticles were 76.92 ± 27.38 nm and 94%, respectively. TQ released from these encapsulants showed a biphasic released pattern. Cytotoxicity activity showed that tamoxifen-resistant (TamR) MCF-7 breast cancer cells required a higher concentration of TQ-PLGA-PF68 nanoparticles than the parental MCF-7 cells to achieve IC50 (p < 0.05). The other two resistant subtypes (TamR UACC732 inflammatory breast carcinoma and paclitaxel-resistant (PacR) MDA-MB 231 triple-negative breast cell line) required a lower concentration of TQ-PLGA-PF68 nanoparticles compared to their respective parental cell lines (p < 0.05). These findings suggest that TQ encapsulation with PLGA-PEG and Pluronics F68 is a promising anti-cancer agent in mitigating breast cancer resistance to chemotherapeutics. In future studies, the anti-cancer activity of TQ-PLGA-PF68 with the standard chemotherapeutic drugs used for breast cancer treatment is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it