Evidence map›Paper›PMID 21356167›Full record

ReviewDiscovery medicine2011

Optimizing siRNA delivery to the genital mucosa.

Joseph A Katakowski, Deborah Palliser

Open access · greenAbstract readReview
In one paragraph

Review in Discovery medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Polymer nanoparticles encapsulating siRNA for treatment of HSV-2 genital infection.Journal of controlled release : official journal of the Controlled Release Society · 2012
    Article
  4. Hydrogel-siRNA for cancer therapy.Cancer biology & therapy · 2011
    Article
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

2 authors at 1 institution in 1 country.

Joseph A KatakowskiDepartment of Microbiology and Immunology, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, New York 10461, USA.
Deborah Palliser
Albert Einstein College of Medicine · US

Funding

Dissecting HSV-2 immunity using cell-targeted siRNAsR21AI093539 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI PALLISER, DEBORAH · 2011 to 2012
$457k
NIAID NIH HHS R21 AI093539
6 · The paper itself

Abstract

RNA interference (RNAi) describes a highly conserved pathway, present in eukaryotic cells, for regulating gene expression. Small stretches of double-stranded RNA, termed small interfering RNAs (siRNAs), utilize this pathway to bind homologous mRNA, resulting in site-specific mRNA cleavage and subsequent protein degradation. The ubiquitous presence of the RNAi machinery, combined with its specificity and efficacy, makes it an attractive mechanism for reducing aberrant gene expression in therapeutic settings. However, a major obstacle to utilizing RNAi in the clinic is siRNA delivery. Administered siRNAs must make contact with the appropriate cell types and, following internalization, gain access to the cytosol where the RNAi machinery resides. This must be achieved so that silencing is maximized, whilst minimizing any undesirable off-target effects. Recently, the utility of siRNAs as a microbicide, usually applied to the genital mucosa for preventing transmission of sexually transmitted diseases including HIV-1 and HSV-2, has been investigated. In this review we will describe these studies and discuss potential strategies for improving gene silencing.

Indexed as

Gene Transfer TechniquesAnimalsAnti-Infective AgentsGenitaliaHumansMucous MembraneRNA, Small InterferingAnti-Infective AgentsRNA, Small Interfering

Identifiers

PMID21356167
PMCPMC3282624
OpenAlexW103823901

What OpenQuestion holds

Textmetadata
LicenceTDM
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.