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Record W1985866231 · doi:10.1002/app.32508

Controlled size chitosan nanoparticles as an efficient, biocompatible oligonucleotides delivery system

2010· article· en· W1985866231 on OpenAlexaff
Romila Manchanda, Surendra Nimesh

Bibliographic record

VenueJournal of Applied Polymer Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsMontreal Clinical Research Institute
Fundersnot available
KeywordsDynamic light scatteringNanoparticleGlutaraldehydeChitosanMaterials scienceChemical engineeringOligonucleotideLipofectamineTransmission electron microscopyAqueous solutionNanotechnologyNuclear chemistryChemistryChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Polymeric nanoparticles of chitosan crosslinked with glutaraldehyde have been prepared using reverse micellar system. An optically clear solution was obtained on redispersing these nanoparticles in aqueous buffer. The nanoparticles were characterized for their size and surface morphology employing dynamic laser scattering (DLS) and transmission electron microscopy (TEM). The TEM images showed spherical particles with smooth surface and narrow size distribution of about 90 nm, which was also supported by DLS data. Size and morphology of the particles remains the same on redispersing the lyophilized powder of these nanoparticles in aqueous buffer. Further, these nanoparticles were loaded with different synthetic oligonucleotides (ODNs). In vitro pH dependent release of the adsorbed oligonucleotides from these nanoparticles was also studied. At basic pH the release of oligonucleotides was found higher as compared with neutral and acidic medium. Cytotoxicity studies done on HEK 293 cells reveals that oligonucleotide loaded nanoparticles have high cell viability of nearly 76–88% whereas those of lipofectamine was about 35%. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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 score0.004
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.235
Teacher spread0.230 · 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

Citations41
Published2010
Admission routes1
Has abstractyes

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