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Record W2034139519 · doi:10.3390/pharmaceutics5030498

Advances in Lipid Nanoparticles for siRNA Delivery

2013· article· en· W2034139519 on OpenAlexafffund
Yuen Yi C. Tam, Sam Chen, Pieter R. Cullis

Bibliographic record

VenuePharmaceutics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchAlnylam Pharmaceuticals
KeywordsSmall interfering RNAGene silencingRNA interferenceDrug deliveryRNACytoplasmComputational biologyChemistryNanotechnologyCell biologyGeneBiologyBiochemistryMaterials science

Abstract

fetched live from OpenAlex

Technological advances in both siRNA (small interfering RNA) and whole genome sequencing have demonstrated great potential in translating genetic information into siRNA-based drugs to halt the synthesis of most disease-causing proteins. Despite its powerful promises as a drug, siRNA requires a sophisticated delivery vehicle because of its rapid degradation in the circulation, inefficient accumulation in target tissues and inability to cross cell membranes to access the cytoplasm where it functions. Lipid nanoparticle (LNP) containing ionizable amino lipids is the leading delivery technology for siRNA, with five products in clinical trials and more in the pipeline. Here, we focus on the technological advances behind these potent systems for siRNA-mediated gene silencing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.319
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations222
Published2013
Admission routes2
Has abstractyes

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