Evidence map›Paper›PMID 24080766›Full record

ArticleLab on a chip2013

Giga-pixel fluorescent imaging over an ultra-large field-of-view using a flatbed scanner.

Zoltán Göröcs, Yuye Ling, Meng Dai Yu, Dimitri Karahalios, Kian Mogharabi, Kenny Lu, Qingshan Wei, Aydogan Ozcan

Abstract read
In one paragraph

Article in Lab on a chip, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Technologies for Frugal and Sensitive Point-of-Care Immunoassays.Annual review of analytical chemistry (Palo Alto, Calif.) · 2022
    Review
  3. Article
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  10. Review
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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

8 authors.

Zoltán GöröcsElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA. ozcan@ucla.edu.
Yuye Ling
Meng Dai Yu
Dimitri Karahalios
Kian Mogharabi
Kenny Lu
Qingshan Wei
Aydogan Ozcan

Funding

Towards Mega-Throughput, Label-free Genomics and Proteomics: Revolutionizing MicrDP2OD006427 · OD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI OZCAN, AYDOGAN · 2009 to 2009
$2.3M
NIH HHS DP2 OD006427NIH HHS DP2OD006427
6 · The paper itself

Abstract

We demonstrate a new fluorescent imaging technique that can screen for fluorescent micro-objects over an ultra-wide field-of-view (FOV) of ~532 cm(2), i.e., 19 cm × 28 cm, reaching a space-bandwidth product of more than 2 billion. For achieving such a large FOV, we modified the hardware and software of a commercially available flatbed scanner, and added a custom-designed absorbing fluorescent filter, a two-dimensional array of external light sources for computer-controlled and high-angle fluorescent excitation. We also re-programmed the driver of the scanner to take full control of the scanner hardware and achieve the highest possible exposure time, gain and sensitivity for detection of fluorescent micro-objects through the gradient index self-focusing lens array that is positioned in front of the scanner sensor chip. For example, this large FOV of our imaging platform allows us to screen more than 2.2 mL of undiluted whole blood for detection of fluorescent micro-objects within <5 minutes. This high-throughput fluorescent imaging platform could be useful for rare cell research and cytometry applications by enabling rapid screening of large volumes of optically dense media. Our results constitute the first time that a flatbed scanner has been converted to a fluorescent imaging system, achieving a record large FOV.

Indexed as

Fluorescent DyesHumansImage EnhancementMicrofluidic Analytical TechniquesMicroscopy, FluorescenceSoftwareFluorescent Dyes

Identifiers

PMID24080766
PMCPMC3837097

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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.