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Record W7077515999 · doi:10.1016/j.jddst.2025.107436

Preparation of PLGA nanoparticle/sodium alginate composites by pressurized Gas-eXpanded liquid technology for drug delivery applications

2025· article· en· W7077515999 on OpenAlexaff

Bibliographic record

VenueJournal of Drug Delivery Science and Technology · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsTECComposite numberPLGADrug deliveryDynamic light scatteringDextranAerogelNanoparticle

Abstract

fetched live from OpenAlex

A dried, homogeneous porous composite containing poly(lactic-co-glycolic acid) nanoparticles (PLGA-NP) entrapped in a sodium alginate (SA) aerogel matrix was developed using the Pressurized Gas-eXpanded liquid Technology (PGX TEC ) and compared to the same material dried by lyophilization. The size and stability of PLGA-NP in all stages of the processes were studied using dynamic light scattering and Field Emission Scanning Electron Microscopy. The morphology of the composites was investigated using helium ion microscopy, and fluorescence confocal microscopy. A fluorescently labeled dextran analogue (Tetramethylrhodamine Dextran, TMRD) as model for macromolecular drugs was loaded onto PLGA-NP and processed into PGX TEC -processed and freeze-dried composites. The release kinetics of TMRD from composites were measured and compared with unprocessed PLGA-NP. The results revealed that the processing of PLGA-NP using the PGX TEC expanded the particles leading to porous structures that facilitate the release of encapsulated macromolecules. Both composites prepared by PGX TEC or lyophilisation showed an initial burst release of TMRD in HEPES buffer solution in the first 8 h, reaching up to 8 % and 22 %, respectively. The PGX TEC processed composite outperformed the lyophilized composite for the total cumulative TMRD release after 400 h reaching 100 % release, while the lyophilized composite reached a plateau of about 32.7 %. The results suggest that PGX TEC -processed PLGA-NP entrapped in a SA matrix forming a PLGA-NP/SA composite have potential for delivery of macromolecular drugs such as peptides and proteins. • PLGA nanoparticles/sodium alginate aerogel (PLGA-NP/SA) was developed using Pressurized Gas-eXpanded liquid Technology (PGX TEC ). • PLGA-NP expanded by PGX TEC to a porous structure facilitating loading and release of bioactives of high molecular weight. • The PGX TEC generated PLGA-NP/SA composites have potential to deliver macromolecules, such as proteins or peptides.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.243
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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