Evidence map›Paper›PMID 24039347›Full record

ArticleMeasurement science & technology2013

Dual channel detection of ultra low concentration of bacteria in real time by scanning FCS.

Ilaria Altamore, Luca Lanzano, Enrico Gratton

Open access · greenAbstract read
In one paragraph

Article in Measurement science & technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

16 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 2 institutions in 1 country.

Ilaria AltamoreLaboratory for Fluorescence Dynamics, Department of Biomedical Engineering, University of California, Irvine, California.
Luca Lanzano
Enrico Gratton
Fluor (United States) · USUniversity of California, Irvine · US

Funding

Uncover Strategies Used By Biological SystemsP50GM076516 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI LANDER, ARTHUR D · 2007 to 2016
$25.1M
YEAST RIBOSOMAL STALK COMPOSITION IN VIVO BY TWO-PHOTON FCS AND PCH ANALYSISP41RR003155 · NCRR · UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN · PI GRATTON, ENRICO · 1986 to 2011
$14.9M
NCRR NIH HHS P41 RR003155NIGMS NIH HHS P50 GM076516
6 · The paper itself

Abstract

We describe a novel method to detect very low concentrations of bacteria in water. Our device consists of a portable horizontal geometry small confocal microscope with large pinhole and a holder for cylindrical cuvettes containing the sample. Two motors provide a fast rotational and slow vertical motion of the cuvette so the device looks like a simplified flow cytometer without flow. To achieve high sensitivity the design has two detection channels. Bacteria are stained by two different nucleic acid dyes and excited with two different lasers. Data are analyzed with a correlation filter based on particle passage pattern recognition. The passage of a particle through the illumination volume is compared with a Gaussian pattern in both channels. The width of the Gaussian correlates with the time of passage of the particle so one particle is counted when the algorithm finds a match with a Gaussian in both channels. The concentration of particles in the sample is deduced from the total number of coincident hits and the total volume scanned. This portable setup provides higher sensitivity, low cost and it could have a wide use ranging from clinical applications to pollution monitors and water and air quality control.

Indexed as

Bacteria detectionpattern recognition algorithmultra-low concentration

Identifiers

PMID24039347
PMCPMC3770197
OpenAlexW1997337200

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.