ReviewInternational journal of molecular sciences2020
Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.
Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed, 44 citations in OpenAlex.
- Tracking Oral Nanoparticle Uptake in Mouse Gastrointestinal Tract by Fluorescent Labeling and t-SNE Flow Cytometry.Bio-protocol · 2025Article
- Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders.Diagnostics (Basel, Switzerland) · 2024Article
- Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data.iScience · 2022Article
- The Influence of Magnetic Composite Capsule Structure and Size on Their Trapping Efficiency in the Flow.Molecules (Basel, Switzerland) · 2022Article
- Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.International journal of molecular sciences · 2022Article
- Classifying grains using behaviour-informed machine learning.Scientific reports · 2022Article
- Fluorescent Nanosystems for Drug Tracking and Theranostics: Recent Applications in the Ocular Field.Pharmaceutics · 2022Review
- Diagnosis of Acute Leukemia by Multiparameter Flow Cytometry with the Assistance of Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Article
- Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.Frontiers in genetics · 2022Article
- Inertial cell sorting of microparticle-laden flows: An innovative OpenFOAM-based arbitrary Lagrangian-Eulerian numerical approach.Biomicrofluidics · 2021Article
- Changes in Autofluorescence Level of Live and Dead Cells for Mouse Cell Lines.Journal of fluorescence · 2020Article
- Cell Reprogramming With CRISPR/Cas9 Based Transcriptional Regulation Systems.Frontiers in bioengineering and biotechnology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient's life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.
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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.