Polymeric Versus Lipid Nanoparticles: Comparative Study of Nanoparticulate Systems as Indomethacin Carriers
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
Encapsulation of nonsteroidal or non-steroidal anti-inflammatory drugs (NSAID) in nanocarrier systems aims to enhance bioavailability and to decrease toxicity of these drugs and thus improve the efficacy of treatments. With this aim two types of nanoparticles were prepared and compared: lipid nanoparticles, made of cetyl palmitate and Miglyol 812 which were uncoated or coated with chitosan; or polymeric nanoparticles, made of poly (DL-lactic-co-glycolic acid) (PLGA) for which different emulsion stabilizers were also tested (poly (vinyl alcohol) (PVA), and Pluronic F68). Nanoparticles were characterized for drug content and for particle size, charge and morphology. The lipid matrix was analyzed regarding its crystallinity by differential scanning calorimetry (DSC). The size of the nanoparticles was measured by dynamic light scattering (DLS) which indicated a unimodal particle size distribution in all systems. Nanoparticles' stability was confirmed by their highly negative surface charge in the case of polymeric and uncoated lipid nanoparticles, as analyzed by zeta potential measurements using electrophoretic light scattering (ELS). Lipid chitosan coated nanoparticles have also shown to be stable presenting highly positive surface charge. Results have further demonstrated that indomethacin is highly encapsulated regardless the type of particles. Morphological analysis by scanning electron microscopy has shown that the nanoparticles were smooth and spherical. The results gathered within the current study point to the conclusion that the proposed formulations provide nanoparticles of satisfactory quality to encapsulate indomethacin, which might be used to improve bioavailability of other NSAID in the treatment of inflammation.
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 | 0.001 | 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