Investigation and optimization of formulation parameters on preparation of targeted anti-CD205 tailored PLGA nanoparticles
Bibliographic record
Abstract
The purpose of this study was to assess the effect of various formulation parameters on anti-CD205 antibody decorated poly(d, l-lactide co-glycolide) (PLGA) nanoparticles (NPs) in terms of their ability to target dendritic cells (DCs). In brief, emulsification solvent evaporation technique was adapted to design NP formulations using two different viscosity grades (low and high) of both ester and carboxylic acid terminated PLGA. Incorporation of ligand was achieved following physical adsorption or chemical conjugation processes. The physicochemical characterizations of formulations were executed to assess the effects of different solvents (chloroform and ethyl acetate), stabilizer percentage, polymer types, polymer viscosities, ligand-NP bonding types, cross-linkers, and cryoprotectants (sucrose and trehalose). Modification of any of these parameters shows significant improvement of physicochemical properties of NPs. Ethyl acetate was the solvent of choice for the formulations to ensure better emulsion formation. Infrared spectroscopy confirmed the presence of anti-CD205 antibody in the NP formulation. Finally, cytotoxicity assay confirmed the safety profile of the NPs for DCs. Thus, ligand modified structurally concealed PLGA NPs is a promising delivery tool for targeting DCs in vivo.
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.
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 | 0.001 | 0.001 |
| 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".