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Record W1960475700 · doi:10.2147/ijn.s79306

Effective small interfering RNA delivery in vitro via a new stearylated cationic peptide

2015· article· en· W1960475700 on OpenAlexafffund
Pu Chen, Baoling Chen, Pan Ran, Diana Askhatova

Bibliographic record

VenueInternational Journal of Nanomedicine · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSmall interfering RNATransfectionLipofectamineRNARNA interferenceMolecular biologyCytotoxicityPeptideGene silencingBiochemistryCell biologyBiophysicsBiologyIn vitroChemistryGeneRecombinant DNA

Abstract

fetched live from OpenAlex

A crucial bottleneck in RNA interference-based gene therapy is the lack of safe and efficient delivery systems. Here, a novel small interfering RNA (siRNA) delivery peptide, STR-HK, was constructed by conjugating a stearyl end to the N-terminus of the peptide sequence HHHPKPKRKV, where PKPKRKV is an altered sequence of the nucleus localization signal (PKKKRKV) and contributes to the cytosol localization of STR-HK-siRNA complexes. Histidine is a linker and plays an important role in disrupting the endosomal membrane via the proton sponge effect. As expected, STR-HK formed complexes with siRNA with a particle size of 80-160 nm in diameter and efficiently delivered Cy3-labeled glyceraldehyde 3-phosphate dehydrogenase siRNA into PC-3 human prostate cancer cells. The transfection efficiency of STR-HK at molar ratio of 60/1 was comparable to that of Lipofectamine 2000, one of the most efficient commercially available transfection reagents. Furthermore, the STR-HK-siRNA complexes exhibited minimal cytotoxicity, which was significantly lower than that of Lipofectamine. Taken together, the strategy of conjugating the stearyl moiety with HHHPKPKRKV as a non-viral siRNA delivery system is advantageous.

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 score0.046
Threshold uncertainty score0.438

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 science0.0000.000
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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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