Evidence map›Paper›PMID 24446580›Full record

ArticleAnalytical chemistry2014

Antibody-free detection of Mycobacterium tuberculosis antigen using customized nanotraps.

Hung-Jen Wu, Yaojun Li, Jia Fan, Zaian Deng, Zhao Hu, Xuewu Liu, Edward A Graviss, Mauro Ferrari, Xin Ma, Ye Hu

Open access · bronzeAbstract read
In one paragraph

Article in Analytical chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

7 citing papers in PubMed, 16 citations in OpenAlex.

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  7. Predictive value of serum bradykinin and desArgTuberculosis (Edinburgh, Scotland) · 2016
    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

10 authors at 1 institution in 1 country.

Hung-Jen WuDepartment of Nanomedicine, Houston Methodist Research Institute , 6670 Bertner Avenue R8-213, Houston, TX 77030, United States.
Yaojun Li
Jia Fan
Zaian Deng
Zhao Hu
Xuewu Liu
Edward A Graviss
Mauro Ferrari
Xin Ma
Ye Hu
Houston Methodist · US

Funding

Texas Center for Cancer NanomedicineU54CA151668 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2010 to 2014
$12.4M
NCI NIH HHS U54 CA151668NCI NIH HHS U54CA151668
6 · The paper itself

Abstract

Rapid screening and diagnosis of tuberculosis disease (TB) is still challenging and critically needed for global TB control efforts. In this study, we present a rapid and streamlined technology, using precisely engineered silica nanopore thin films, which are optimized for pore size, structure, capillary force, and film thickness, to isolate Mycobacterium tuberculosis (MTB) antigens in laboratory and clinical samples for rapid TB screening. This technology, referred to here as on-chip fractionation, is integrated with high-throughput matrix-assisted laser desorption/ionization time-of flight mass spectrometry to screen and identify fragments of the MTB antigen, CFP-10, from complex biological samples, without use of immunoaffinity agents. With the use of this comprehensive approach, we were able to clearly distinguish a clinical isolate of MTB from a nonTB species of the genus Mycobacterium avium grown in liquid culture media. This assay can reach a detection limit of 10 fmol and an isolation rate of 90% for the antigen CFP-10. Our strategy has significant potential to fill the conceptual and technical gaps in rapid diagnosis of active TB disease.

Indexed as

Amino Acid SequenceAntigens, BacterialMass SpectrometryMolecular Sequence DataNanotechnologyPhotoelectron SpectroscopySpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationAntigens, BacterialMycobacterium tuberculosis antigens

Identifiers

PMID24446580
PMCPMC3983014
OpenAlexW1967966968

What OpenQuestion holds

Textmetadata
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.