Visualization Method for the Cell-Level Vesicle Transport Using Optical Flow and a Diverging Colormap.

Seohyun Lee, Hyuno Kim, Hideo Higuchi, Masatoshi Ishikawa

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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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

1 citing paper in PubMed.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022
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

4 authors.

Seohyun LeeInformation Technology Center, Data Science Research Division, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan.ORCID 0000-0002-0952-9547

Hyuno KimInformation Technology Center, Data Science Research Division, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan.

Hideo HiguchiDepartment of Physics, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.

Masatoshi IshikawaInformation Technology Center, Data Science Research Division, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan.

Funding

Japan Society for the Promotion of Science 19K23717Japan Society for the Promotion of Science 20K15753

6 · The paper itself

Abstract

Elucidation of cell-level transport mediated by vesicles within a living cell provides key information regarding viral infection processes and also drug delivery mechanisms. Although the single-particle tracking method has enabled clear analysis of individual vesicle trajectories, information regarding the entire cell-level intracellular transport is hardly obtainable, due to the difficulty in collecting a large dataset with current methods. In this paper, we propose a visualization method of vesicle transport using optical flow, based on geometric cell center estimation and vector analysis, for measuring the trafficking directions. As a quantitative visualization method for determining the intracellular transport status, the proposed method is expected to be universally exploited in various biomedical cell image analyses.

Indexed as

Optic FlowImage Processing, Computer-Assistedoptical flowvesicle transportvisualization

Identifiers

PMID33450927

DOI10.3390/s21020522

PMCPMC7828387

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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.