MétaCan
Menu
Back to cohort
Record W2398495292 · doi:10.1039/c6bm00204h

Powering up the molecular therapy of RNA interference by novel nanoparticles

2016· review· en· W2398495292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomaterials Science · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsSaskatoon Medical ImagingUniversity of Saskatchewan
Fundersnot available
KeywordsBiocompatibilityNanoparticleRNA interferenceChemistryDrug deliveryRNAToxicityNanotechnologyDrugPharmacologyBiophysicsBiochemistryMaterials scienceMedicineBiologyGene

Abstract

fetched live from OpenAlex

RNA interference technology has been widely applied in biomedical therapy in recent years. A type of small RNA molecule - siRNA could regulate the expression of disease related genes by breaking down the integrity of mRNA with high specificity. However, the low efficiency of siRNA delivery to its target seriously hampered the RNAi therapy. Compared with viral-based delivery systems, non-viral-based nanoparticles are more suitable for disease treatment due to reduced cellular toxicity, higher loading capacity, and better biocompatibility. This review article highlights several nanoparticle-based siRNA delivery systems, including liposomes, cationic solid lipid nanoparticles, reconstituted high density lipoprotein, polymeric nanoparticles, cationic cell penetrating peptides, and inorganic nanoparticles. The molecular mechanism of gene silencing, clinical examples, and the limitations of current technology related to nanomaterial sciences, are also discussed.

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.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.255
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.040
GPT teacher head0.328
Teacher spread0.288 · 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