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Record W2743141745 · doi:10.1039/c7tb01833a

Biomaterials for polynucleotide delivery to anchorage-independent cells

2017· article· en· W2743141745 on OpenAlexafffund
Aysha Ansari, Paul Santerre, Hasan Uludağ

Bibliographic record

VenueJournal of Materials Chemistry B · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of TorontoToronto Public HealthAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPolynucleotideTransfectionEndocytosisNucleic acidBiomaterialCellNanotechnologyMaterials scienceCell biologyChemistryBiophysicsBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

Anchorage-independent cells possess morphological features and cell membrane compositions that are distinct from adherent cells. They display minimal surface area, have a low rate of endocytosis and generally possess few proteoglycans which make it a challenge to deliver nucleic acids into them. Wide ranges of methods and materials have been developed to tackle the delivery obstacles for the polynucleotide-based therapeutics in modifying non-adherent cells. This article summarizes the techniques and biomaterials that have been utilized for transfection of anchorage-independent cells. First, physical techniques are briefly described along with particular applications for which they are well-suited. The structure-activity relationship of various biomaterial carriers of polynucleotides are then discussed with strategies employed to enhance their capability to transfect anchorage-independent cells. In conclusion, the authors' perspectives on different methods for polynucleotide delivery to primary human cells are compared, along with a discussion of their progression towards clinical trials.

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 score0.003
Threshold uncertainty score0.691

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 studies0.0000.000
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 head0.015
GPT teacher head0.271
Teacher spread0.257 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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