Evidence map›Paper›PMID 22449918›Full record

ArticleMethods in enzymology2012

Preparation, characterization, and cellular associations of silicon logic-embedded vectors.

Anne L van de Ven, Aaron Mack, Kenneth Dunner, Mauro Ferrari, Rita E Serda

Abstract read
In one paragraph

Article in Methods in enzymology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Silicon micro- and nanofabrication for medicine.Advanced healthcare materials · 2013
    Review
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

5 authors.

Anne L van de VenDepartment of Nanomedicine, The Methodist Hospital Research Institute, Houston, Texas, USA.
Aaron Mack
Kenneth Dunner
Mauro Ferrari
Rita E Serda

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
TRANS-NETWORK PROJECTSU54CA143837 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2009 to 2014
$10.8M
Subcellular localization of nanoparticlesRC2GM092599 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHIU, WAH, DECUZZI, PAOLO · 2009 to 2010
$3.0M
MOLECULAR MECHANISMS OF NEUROTRANSMITTER SECRETIONR01MH058920 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BEAN, ANDREW J · 1998 to 2010
$2.9M
Nanovectors for Characterization and Destruction of Breast Tumor VasculatureR01CA128797 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2007 to 2010
$1.7M
NCI NIH HHS CA016672NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA128797NCI NIH HHS R01CA128797NCI NIH HHS U54 CA143837NCI NIH HHS U54CA143837NIGMS NIH HHS RC2 GM092599NIGMS NIH HHS RC2GM092599NIMH NIH HHS MH58920NIMH NIH HHS R01 MH058920
6 · The paper itself

Abstract

Logic-embedded vectors (LEVs) have been introduced as a means to overcome sequential, biological barriers that prevent particle-based drug delivery systems from reaching their targets. In this chapter, we address the challenge of fabricating and optimizing LEVs to reach non-endosomal targets. We describe the general preparation, characterization, and cellular association of porous silicon-based LEVs. A specific example of LEV fabrication from start to finish, along with optimization and troubleshooting information, is presented to serve as a template for future designs.

Indexed as

Drug Delivery SystemsChitosanDrug CarriersMicroscopy, Electron, ScanningMicroscopy, Electron, TransmissionSiliconSpectroscopy, Fourier Transform InfraredSurface PropertiesChitosanDrug CarriersSilicon

Identifiers

PMID22449918
PMCPMC3763508

What OpenQuestion holds

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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.