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Record W1984698021 · doi:10.1021/acs.biomac.5b00221

Tailoring the Surface of a Gene Delivery Vector with Carboxymethylated Dextran: A Systematic Analysis

2015· article· en· W1984698021 on OpenAlexafffund
Charles Fortier, Elodie Louvier, Yves Durocher, Grégory De Crescenzo

Bibliographic record

VenueBiomacromolecules · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité de MontréalPolytechnique MontréalNational Research Council Canada
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNanocarriersGene deliveryChemistryCoatingBiophysicsNanotechnologyIn vivoDrug deliveryMaterials scienceGenetic enhancementBiochemistryGeneBiologyBiotechnology

Abstract

fetched live from OpenAlex

Polymeric nanocarriers are attractive nonviral vectors for gene delivery purposes in vivo. For such applications, numerous physiological and subcellular bottlenecks have to be overcome. In that endeavor, each structural feature of nanocarriers can be optimized with respect to its corresponding challenges. Here, we focused on the interface between a model gene delivery nanocarrier and relevant constituents of the physiological environment. We screened a library of carboxymethylated dextrans (CMD) for the electrostatic coating of positively charged nanocarriers. We evaluated the jointed influence of the CMD molecular weight and charge density upon nanocarrier coating with respect to DNase, small ions, plasma proteins, red blood cells, and target cells. A total of 4 out of 26 CMD coated nanocarriers successfully passed every screening assay, but did not yield increased reporter gene expression in target cells compared to uncoated nanocarriers. The fine-tuning of CMD for nanocarrier coating yielded a relevant shortlist of candidates that will be further tested 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 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 score0.008
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.235
Teacher spread0.218 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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