Engineered CaCO<sub>3</sub> nanoparticles with targeting activity: A simple approach for a vascular intended drug delivery system
Bibliographic record
Abstract
Abstract During the last decade, great attention has been paid to drug delivery systems that are able to induce a site‐specific and tunable release of biomolecules, attenuating the drawbacks of common therapy. The encapsulation of bioactive compounds, such as proteins and drugs, offers the advantage of enhancing the pharmacokinetics and bioavaliability of the entrapped molecules. The encapsulating technique and coating agents are chosen on the basis of the desired functionality of the final product, release kinetics, and fabrication costs. Calcium carbonate (CaCO3) is considered an ideal substrate to fabricate particles at the nanoscale level. In this work, CaCO3 nanoparticles were synthesized through a two‐step protocol which comprised complex coacervation and mineralization, and they were loaded with bovine serum albumin (BSA) as a model protein. The synthesized nanoparticles were then functionalized with the layer‐by‐layer (LbL) electrostatic self‐assembling technique using chitosan as a polycation and dextran sulphate as a polyanion. The multilayered architecture that covers CaCO3 nanoparticles prevented a burst release of BSA, resulting in 77.34 ± 1.22 % of the released protein, after 72 h of incubation. A horseradish peroxidase‐linked IgG was immobilized at the outer particle layer and its presence was detected via chemiluminescence. Multilayered and non‐multilayered nanoparticles were biocompatible until 7 days using EA. hy926 endothelial cells. Here, we report a cost‐effective protocol to obtain protein‐loaded CaCO3 nanoparticles (diameter < 155 nm) using a coacervate‐based synthesis system. Furthermore, with a very simple technique these nanoparticles were functionalized with antibodies, developing immuno‐nanoparticles to apply in the drug delivery field.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".