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RNA Interference: A Potent Tool for Gene-Specific Therapeutics

2004· review· en· W2155612281 on OpenAlexafffund
Thomas E. Ichim, Mu Li, Hua Qian, Igor A. Popov, Katarzyna Rycerz, Xiufen Zheng, David White, Robert Zhong, Wei‐Ping Min

Bibliographic record

VenueAmerican Journal of Transplantation · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsRobarts Clinical TrialsLawson Health Research InstituteLondon Health Sciences CentreWestern University
FundersRoche Organ Transplant Research FoundationKidney Foundation of CanadaLondon Health Sciences CentreLawson Health Research InstituteHeart and Stroke Foundation of Canada
KeywordsRNA interferenceSmall interfering RNARNA silencingGene silencingDNA-directed RNA interferenceRNAComputational biologySmall hairpin RNAMedicineGeneBiologyGenetics

Abstract

fetched live from OpenAlex

RNA interference (RNAi) is a process through which double-stranded RNA induces the activation of cellular pathways, leading to potent and selective silencing of genes with homology to the double strand. Much excitement surrounding small interfering RNA (siRNA)-mediated therapeutics arises from the fact that this approach overcomes many of the shortcomings previously experienced with approaches such as antibodies, antisense oligonucleotides and pharmacological inhibitors. Induction of RNAi through administration of siRNA has been successfully used in treatment of hepatitis, viral infections, and cancer. In this review we will present a brief history of RNAi, methods of inducing RNAi, application of RNAi in the therapeutic setting, and the possibilities of using this highly promising approach in the context of transplantation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.004

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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations106
Published2004
Admission routes2
Has abstractno

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