Characterization of Calcium Phosphate Nanoparticles Based on a PEGylated Chelator for Gene Delivery
Bibliographic record
Abstract
Calcium phosphate (CaP) nanoparticles are promising gene delivery carriers due to their bioresorbability, ease of preparation, high gene loading efficacy, and endosomal escape properties. However, the rapid aggregation of the particles needs to be addressed in order to have potential in vivo . In addition, there is a need to better understand the relationship between CaP nanoparticle properties and their interactions with cells. Here, a new synthesis route involving click chemistry was developed to prepare the PEGylated chelator PEG-inositol 1,3,4,5,6-pentakisphosphate (PEG-IP5) that can coat and stabilize CaP nanoparticles. Two methods (1 and 2) differing on the time of addition of the PEGylated chelator were employed to produce stabilized particles. Method 1 yielded amorphous aggregated spheres with a particle size of about 200 nm, whereas method 2 yielded 40 nm amorphous loose aggregates of clusters, which were quickly turned into needle bundle-like crystals of about 80 nm in a few hours. Nanoparticles prepared by method 1 were internalized with significantly higher efficiency in HepG2 cells than those prepared by method 2, and the uptake was dramatically influenced by the reaction time of Ca 2+ and PO 4 3– and sedimentation of the particles. Interestingly, morphological transformations were observed for both types of particles after different storage times, but this barely influenced their in vitro cellular uptake. The transfection efficiency of the particles prepared by method 1 was significantly higher, and none of the formulations tested showed signs of cytotoxicity. This study provides a better understanding of the properties ( e.g., size, morphology, and crystallinity) of PEGylated CaP nanoparticles and how these influence the particles’ in vitro uptake and transfection efficiency.
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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 | 0.000 | 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 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".