Evidence map›Paper›PMID 26372022›Full record

ReviewMolecular therapy. Nucleic acids2015

siRNA Versus miRNA as Therapeutics for Gene Silencing.

Jenny K W Lam, Michael Y T Chow, Yu Zhang, Susan W S Leung

Open access · goldAbstract readReview
In one paragraph

Review in Molecular therapy. Nucleic acids, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 523 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
523citing papers in PubMed, 2 pooled it
30.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

523 citing papers in PubMed, 2 syntheses or guidelines pooled it, 1,031 citations in OpenAlex.

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463 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Jenny K W LamDepartment of Pharmacology & Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Michael Y T ChowDepartment of Pharmacology & Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Yu ZhangDepartment of Pharmacology & Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
Susan W S LeungDepartment of Pharmacology & Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discovered a little over two decades ago, small interfering RNAs (siRNAs) and microRNAs (miRNAs) are noncoding RNAs with important roles in gene regulation. They have recently been investigated as novel classes of therapeutic agents for the treatment of a wide range of disorders including cancers and infections. Clinical trials of siRNA- and miRNA-based drugs have already been initiated. siRNAs and miRNAs share many similarities, both are short duplex RNA molecules that exert gene silencing effects at the post-transcriptional level by targeting messenger RNA (mRNA), yet their mechanisms of action and clinical applications are distinct. The major difference between siRNAs and miRNAs is that the former are highly specific with only one mRNA target, whereas the latter have multiple targets. The therapeutic approaches of siRNAs and miRNAs are therefore very different. Hence, this review provides a comparison between therapeutic siRNAs and miRNAs in terms of their mechanisms of action, physicochemical properties, delivery, and clinical applications. Moreover, the challenges in developing both classes of RNA as therapeutics are also discussed.

Identifiers

PMID26372022
PMCPMC4877448
OpenAlexW2216033157

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.