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Design and Development of Light-Sensitive Chitosan-Based Nanocarriers for Gene Delivery

2012· article· en· W2072327877 on OpenAlexafffund
Nicolas Duceppe, Maryam Tabrizian

Bibliographic record

VenueAdvances in science and technology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanocarriersTransfectionMaterials scienceChitosanGene deliveryZeta potentialBiophysicsNanoparticleNanotechnologyChemistryBiochemistryBiology

Abstract

fetched live from OpenAlex

In this work, we report on the development of a multifunctional and photo-inducible nanoplex made of chitosan (Ch) and hyaluronic acid (HA) for delivery of nucleic acid. Self-assembled Ch/HA nanoparticles were attached to ortho-nitrobenzyl (o-NB) photo-labile molecules (PL)-gold nanoparticles via thiol groups and to QDs-conjugate ssDNA through amide bond linkage to form nanoplexes (Ch:HA:AuPL:QD-DNA). The composition of DNA nanocarriers was validated by nuclear magnetic resonance, transmission electronic microscopy, energy dispersive x-ray spectroscopy, gel electrophoresis and spectrophotometry. The change in zeta potential (34 ± 11 to -26 ± 11 mV) and the loss of the o-NB characteristic peaks in nuclear magnetic resonance spectra, after the exposure of the PL molecule to ultraviolet light, both confirmed the photo-labile properties of the system. The potential of the nanoplexes to induce high cell transfection was assessed by flow cytometry and fluorescent microscopy imaging. Over 30% transfection of HEK-293 was obtained with the nanoplexes after a one-minute exposure of cells to UV light. This corresponds to a 15% increase in the transfection efficiency compared to unexposed Ch:HA:AuPL:QD-DNA nanocarriers. This high transfection efficiency was associated with the unique design of the carrier system and its photo-responsiveness feature for facilitating the DNA release.

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)0.000
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 score0.152
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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