Evidence map›Paper›PMID 19854924›Full record

ArticleApplied and environmental microbiology2009

Development of a fluorescent in situ method for visualization of enteric viruses.

Helen Rawsthorne, Trevor G Phister, Lee-Ann Jaykus

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Applied and environmental microbiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Helen RawsthorneDepartment of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, 400 Dan Allen Dr., Raleigh, NC 27695, USA. hrawsth@ncsu.edu
Trevor G Phister
Lee-Ann Jaykus
North Carolina State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studying the interactions between enteric pathogens and their environment is important to improving our understanding of their persistence and transmission. However, this remains challenging in large part because of difficulties associated with tracking pathogens in their natural environment(s). In this study, we report a fluorescent labeling strategy which was applied to murine norovirus (MNV-1), a human norovirus surrogate, and hepatitis A virus (HAV). Specifically, streptavidin-labeled Quantum dots (Q-Dots) were bound to biotinylated capsids of MNV-1 and HAV (bio-MNV-1 and bio-HAV); the process was confirmed by using a sandwich-type approach in which streptavidin-bound plates were reacted with biotinylated virus followed by a secondary binding to Q-Dots with an emission range of 635 to 675 nm (Q-Dots 655). The assay demonstrated a relative fluorescence of 528 +/- 48.1 and 112 +/- 8.6 for bio-MNV-1 and control MNV-1, respectively. The biotinylation process did not impact virus infectivity, nor did it interfere with the interactions between the virus and host cells or model produce items. Using fluorescent microscopy, it was possible to visualize both bio-HAV and bio-MNV-1 attached to the surfaces of permissive mammalian cells and green onion tissue. The method provides a powerful tool for the labeling and detection of enteric viruses (and their surrogates) which can be used to track virus behavior in situ.

Indexed as

AnimalsBiotinCell LineHepatitis A virusHumansIndicators and ReagentsMiceMicroscopy, ConfocalMicroscopy, FluorescenceNorovirusOnionsQuantum DotsStreptavidinBiotinIndicators and ReagentsStreptavidin

Identifiers

PMID19854924
PMCPMC2794100
OpenAlexW2078654368

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.