ArticleBiomedical optics express2013
Label-free high-throughput cell screening in flow.
Article in Biomedical optics express, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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.
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.
Who cites it
24 citing papers in PubMed.
- Dual-view transport of intensity phase imaging flow cytometry.Biomedical optics express · 2023Article
- Differential phase-diversity electrooptic modulator for cancellation of fiber dispersion and laser noise.Nature communications · 2023Article
- Label-free macrophage phenotype classification using machine learning methods.Scientific reports · 2023Article
- Article
- Light sheet based volume flow cytometry (VFC) for rapid volume reconstruction and parameter estimation on the go.Scientific reports · 2022Article
- The palette of techniques for cell cycle analysis.FEBS letters · 2020Review
- Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing.Biomedical optics express · 2020Article
- Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis.Light, science & applications · 2020Article
- Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry.Scientific reports · 2019Article
- Label-free classification of cells based on supervised machine learning of subcellular structures.PloS one · 2019Article
- Invited Article: Digital refocusing in quantitative phase imaging for flowing red blood cells.APL photonics · 2018Article
- High-throughput imaging flow cytometry by optofluidic time-stretch microscopy.Nature protocols · 2018Article
- Matrix Analysis of Warped Stretch Imaging.Scientific reports · 2017Article
- Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM).Journal of visualized experiments : JoVE · 2017Article
- Large population cell characterization using quantitative phase cytometer.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- All-passive pixel super-resolution of time-stretch imaging.Scientific reports · 2017Article
- Ultrafast laser-scanning time-stretch imaging at visible wavelengths.Light, science & applications · 2017Article
- Review: imaging technologies for flow cytometry.Lab on a chip · 2016Review
- Deep Learning in Label-free Cell Classification.Scientific reports · 2016Article
- Design of Warped Stretch Transform.Scientific reports · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Flow cytometry is a powerful tool for cell counting and biomarker detection in biotechnology and medicine especially with regards to blood analysis. Standard flow cytometers perform cell type classification both by estimating size and granularity of cells using forward- and side-scattered light signals and through the collection of emission spectra of fluorescently-labeled cells. However, cell surface labeling as a means of marking cells is often undesirable as many reagents negatively impact cellular viability or provide activating/inhibitory signals, which can alter the behavior of the desired cellular subtypes for downstream applications or analysis. To eliminate the need for labeling, we introduce a label-free imaging-based flow cytometer that measures size and cell protein concentration simultaneously either as a stand-alone instrument or as an add-on to conventional flow cytometers. Cell protein concentration adds a parameter to cell classification, which improves the specificity and sensitivity of flow cytometers without the requirement of cell labeling. This system uses coherent dispersive Fourier transform to perform phase imaging at flow speeds as high as a few meters per second.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.