Evidence map›Paper›PMID 29403088›Full record

ArticleScientific reports2018

Poisson statistics-mediated particle/cell counting in microwell arrays.

Christian D Ahrberg, Jong Min Lee, Bong Geun Chung

Abstract read
In one paragraph

Article in Scientific reports, 2018. 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
–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

7 citing papers in PubMed.

  1. Article
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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.

Christian D AhrbergDepartment of Mechanical Engineering, Sogang University, Seoul, Republic of Korea.
Jong Min LeeDepartment of Mechanical Engineering, Sogang University, Seoul, Republic of Korea.
Bong Geun ChungDepartment of Mechanical Engineering, Sogang University, Seoul, Republic of Korea. bchung@sogang.ac.kr.ORCID http://orcid.org/0000-0002-6838-3218

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise determination of particle or cell numbers is of importance for a wide array of applications in environmental studies, medical and biological applications, or manufacturing and monitoring applications in industrial production processes. A number of techniques ranging from manual counting to sophisticated equipment (e.g., flow cytometry) are available for this task. However, these methods are either labour intensive, prone to error, or require expensive equipment. Here, we present a fast, simple method for determining the number density of cells or microparticles using a microwell array. We analyze the light transmission of the microwells and categorize the microwells into two groups. As particles/cells contained in a microwell locally reduce the light transmission, these wells displayed a lower average transmission compared to unoccupied microwells. The number density of particles/cells can be calculated by Poisson statistics from the ratio of occupied to unoccupied microwells. Following this approach, the number densities of two different types of microparticles, as well as HeLa and E. Coli cells, ranging over four orders of magnitude were determined. Through the microwell array defined by microfabrication, a simple image recognition algorithm can be used with the formation of aggregates or irregular shaped samples providing no additional difficulty to the microwell recognition. Additionally, this method can be carried out using only simple equipment and data analysis automated by a computer program.

Indexed as

Lab-On-A-Chip DevicesCell CountCell-Derived MicroparticlesEscherichia coliGreen Fluorescent ProteinsHeLa CellsHumansMicrofluidic Analytical TechniquesPoisson DistributionStaining and LabelingGreen Fluorescent Proteins

Identifiers

PMID29403088
PMCPMC5799205

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Registered trials

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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.