Uptake of New Lipid-coated Nanoparticles Containing Falcarindiol by Human Mesenchymal Stem Cells
Bibliographic record
Abstract
Nanoparticles are the focus of an increased interest in drug delivery systems for cancer therapy. Lipid-coated nanoparticles are inspired in structure and size by low-density lipoproteins (LDLs) because cancer cells have an increased need for cholesterol to proliferate, and this has been exploited as a mechanism for delivering anticancer drugs to cancer cells. Moreover, depending on drug chemistry, encapsulating the drug can be advantageous to avoid degradation of the drug during circulation in vivo. Therefore, in this study, this design is used to fabricate lipid-coated nanoparticles of the anticancer drug falcarindiol, providing a potential new delivery system of falcarindiol in order to stabilize its chemical structure against degradation and improve its uptake by tumors. Falcarindiol nanoparticles, with a phospholipid and cholesterol monolayer encapsulating the purified drug core of the particle, were designed. The lipid monolayer coating consists of 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol (Chol), and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] (DSPE PEG 2000) along with the fluorescent label DiI (molar ratios of 43:50:5:2). The nanoparticles are fabricated using the rapid injection method, which is a fast and simple technique to precipitate nanoparticles by good-solvent for anti-solvent exchange. It consists of a rapid injection of an ethanol solution containing the nanoparticle components into an aqueous phase. The size of the fluorescent nanoparticles is measured using dynamic light scattering (DLS) at 74.1 ± 6.7 nm. The uptake of the nanoparticles is tested in human mesenchymal stem cells (hMSCs) and imaged using fluorescence and confocal microscopy. The uptake of the nanoparticles is observed in hMSCs, suggesting the potential for such a stable drug delivery system for falcarindiol.
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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".