MétaCan
Menu
Back to cohort
Record W2105774848 · doi:10.1021/bc200233n

Mechanistic Insights into LDL Nanoparticle-Mediated siRNA Delivery

2011· article· en· W2105774848 on OpenAlexaff
Honglin Jin, Jonathan F. Lovell, Juan Chen, Qiaoya Lin, Lili Ding, Kenneth K. Ng, Rajendra K. Pandey, Muthiah Manoharan, Zhihong Zhang, Gang Zheng

Bibliographic record

VenueBioconjugate Chemistry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
Fundersnot available
KeywordsSmall interfering RNAGene silencingChemistryEndocytosisGene knockdownRNA interferenceInternalizationCell biologyGene deliveryLDL receptorBiophysicsReceptorMolecular biologyLipoproteinRNATransfectionGeneBiochemistryCholesterolBiology

Abstract

fetched live from OpenAlex

Although small interfering RNA (siRNA) can silence the expression of disease-related genes, delivery of these highly charged molecules is challenging. Delivery approaches for siRNAs are actively being pursued, and improved strategies are required for nontoxic and efficient delivery for gene knockdown. Low density lipoprotein (LDL) is a natural and endogenous nanoparticle that has a rich history as a delivery vehicle. Here, we examine purified LDL nanoparticles as carriers for siRNAs. When siRNA was covalently conjugated to cholesterol, over 25 chol-siRNA could be incorporated onto each LDL without changing nanoparticle morphology. The resulting LDL-chol-siRNA nanoparticles were selectively taken up into cells via LDL receptor mediated endocytosis, resulting in enhanced gene silencing compared to free chol-siRNA (38% gene knock down versus 0% knock down at 100 nM). However, silencing efficiency was limited by the receptor-mediated entrapment of the LDL-chol-siRNA nanoparticles in endolysosomes. Photochemical internalization demonstrated that endolysosome disruption strategies significantly enhance LDL-mediated gene silencing (78% at 100 nM).

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 categoriesMeta-epidemiology (narrow)
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 score1.000

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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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.

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

Citations52
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueBioconjugate ChemistrySame topicRNA Interference and Gene DeliveryFrench-language works237,207