Evidence map›Paper›PMID 41387929›Full record

ReviewInflammation and regeneration2025

Single-pixel imaging flow cytometry for biomedical research.

Hiroshi Kanno, Zhiying Liu, Rio Sato, Hidenori Endo, Kuniyasu Niizuma, Keisuke Goda

Abstract readReview
In one paragraph

Review in Inflammation and regeneration, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hiroshi KannoDepartment of Chemistry, The University of Tokyo, Tokyo, 113-0033, Japan. hkanno@g.ecc.u-tokyo.ac.jp.ORCID http://orcid.org/0000-0003-3340-9462
Zhiying LiuDepartment of Chemistry, The University of Tokyo, Tokyo, 113-0033, Japan.
Rio SatoTokyo Metropolitan Kokusai High School, Tokyo, 153-0041, Japan.
Hidenori EndoDepartment of Neurosurgery, Tohoku University Graduate School of Medicine, Miyagi, 980-8575, Japan.
Kuniyasu NiizumaDepartment of Translational Neuroscience, Tohoku University Graduate School of Medicine, Miyagi, 980-8575, Japan.
Keisuke GodaDepartment of Chemistry, The University of Tokyo, Tokyo, 113-0033, Japan. goda@chem.s.u-tokyo.ac.jp.ORCID http://orcid.org/0000-0001-6302-6038

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput single-cell analysis and screening have become essential tools in life science research. Imaging flow cytometry, in particular, enables large-scale image-based profiling of heterogeneous cell populations, allowing statistical analysis of cellular morphology, subcellular features, and functional responses. However, its analytical capability is often limited by the use of conventional two-dimensional (2D) image sensors. In this review, we highlight recent advances in single-pixel imaging flow cytometry, which replaces 2D image sensors with single-pixel photodetectors. This approach offers advantages in sensitivity, flexibility, and speed in imaging system design and has been implemented in various optical configurations to achieve high-throughput single-cell imaging. We first introduce its key techniques, then outline representative biomedical applications, including cancer and COVID-19 research, and finally discuss current limitations and prospects for future developments. Single-pixel imaging flow cytometry is expected to serve as a versatile platform supporting both basic and translational studies in diverse biomedical applications.

Indexed as

CancerCoronary artery diseaseCOVID-19Imaging flow cytometrySingle-pixel imagingThrombosis

Identifiers

PMID41387929
PMCPMC12699816

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.