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Record W2564610278 · doi:10.1016/s1525-0016(16)33422-0

614. Microfluidic Manufacture of RNA-Lipid Nanoparticles Leads to Highly Efficient Delivery of Potent Nucleic Acid Therapeutics for Controlling Gene Expression

2016· article· en· W2564610278 on OpenAlexaff
Grace Tharmarajah, Éric Ouellet, Oscar Seira, Jie Liu, A.M. Thomas, Timothy Leaver, Andre Wild, Yuping Li, Yu Tian Wang, Wolfram Tetzlaff, Carl L. Hansen, Pieter R. Cullis, James R. Taylor, Euan Ramsay

Bibliographic record

VenueMolecular Therapy · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of British ColumbiaInternational Collaboration On Repair DiscoveriesPrecision Nanosystems (Canada)
Fundersnot available
KeywordsNucleic acidGene knockdownIn vivoSmall interfering RNARNATransfectionIn vitroRNA interferenceGene deliveryChemistryPTENCell biologyBiologyMolecular biologyBiochemistryGeneSignal transductionPI3K/AKT/mTOR pathwayBiotechnology

Abstract

fetched live from OpenAlex

Lipid nanoparticles (LNPs) have demonstrated efficient nucleic acid delivery in vitro and in vivo, as well as in clinical development. They exploit endogenous delivery pathways, by co-opting apolipoprotein E (apoE), to mediate effective delivery of the encapsulated nucleic acids into cells via the low-density lipoprotein receptor (LDLR). However, use of LNPs from the bench to the clinic has been considerably limited by challenges in manufacturing at both small and large scales. Here, we bridge that gap by describing the robust manufacture and use of clinical-grade lipid-based nanoparticles for highly efficient delivery of nucleic acids at scales suitable for both in vitro screening and in vivo applications.

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 score0.045
Threshold uncertainty score0.626

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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