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Papers
61,005 resultsShowing papers similar to Learning Single‐Cell Distances from Cytometry Data
ClearMultidimensional analysis methods for flow cytometry : Pushing the boundaries
This thesis developed new methods for analyzing multidimensional flow cytometry data to better identify cell populations. While a bioinformatics and immunology paper, flow cytometry is also used in cutting-edge research to detect and quantify micro- and nanoplastics in biological fluids.
Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning
Researchers developed a stain-free flow cytometry method combined with machine learning to rapidly distinguish microplastics from natural particles like algae and sediment in water samples. The approach achieved identification accuracies over 93% and was validated in freshwater environmental samples, offering a time-efficient screening tool for microplastic monitoring.
Flow cytometry as new promising detection tool for micro and submicron plastic particles
Researchers evaluated flow cytometry as a tool for detecting and counting micro- and submicron plastic particles in environmental and biological samples. The method offered rapid throughput and the ability to distinguish plastic particles from biological material, but required careful optimization for complex matrices.
Flow cytometry as new promising detection tool for micro and submicron plastic particles
Researchers evaluated flow cytometry as a detection tool for micro- and nanoplastics, testing its ability to rapidly identify and count plastic particles in environmental and biological samples. Results demonstrated that flow cytometry offers a promising high-throughput approach for microplastic detection compared to more time-intensive conventional methods.
Machine learning enhanced machine vision system for micro-plastics particles classification
Researchers developed a machine learning-based classification system using fluorescence microscopy with Nile Red staining to identify and categorize microplastic types in environmental samples, aiming to provide a faster and more automated alternative to labor-intensive manual identification methods.
Microwave Cytometry with Machine Learning for Shape-Resolved Microplastic Detection
Researchers developed a microwave cytometry platform paired with a random forest model trained on microscopy-derived shape data to electronically determine the major and minor axes of ellipsoidal microplastic particles with less than 8% average error, removing the spherical-particle assumption that limits existing flow-through sensors.
Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry
Researchers developed a label-free method using holographic flow cytometry and artificial intelligence to identify and classify neuroblastoma cancer cells without the need for traditional staining. The approach analyzes cell shape and structure to distinguish between different cancer cell subtypes. While not directly related to microplastics, the technique advances rapid screening capabilities for bioparticle analysis in fluid samples.
Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics
Researchers developed a stain-free flow cytometry method combined with viSNE dimensionality reduction for analyzing stream biofilms at the individual cell level, demonstrating its ability to track community structure, phototrophic decay, and microplastic contamination in both laboratory experiments and field samples.
Label-free ghost cytometry for manufacturing of cell therapy products
This paper is not about microplastics — it describes a machine-learning-based flow cytometry technique for quality control in cell therapy manufacturing.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Connected Component Labelling in the determination of morphometric features of microplastic particles in samples of different matrices
Researchers developed a Connected Component Labeling (CCL) method using the Union-Find algorithm in C++ to automate morphometric analysis of microplastic particles in Baltic Sea fish tissues, enabling automatic measurement of particle area, size, and shape without manual microscope software input.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Automatic Cell Counting With YOLOv5: A Fluorescence Microscopy Approach.
This paper is not about microplastics; it is a study of automatic cell counting using the YOLOv5 deep learning model applied to fluorescence microscopy images, achieving high accuracy for laboratory cell detection.
A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
An open-source computer vision application was developed to automatically count and classify microplastics in microscopy images, achieving accuracy comparable to manual counting while processing samples orders of magnitude faster, offering the scientific community a free tool to reduce the bottleneck of tedious visual microplastic enumeration.
Can flow cytometry emerge as a high-throughput technique for micro- and nanoplastics analysis in complex environmental aqueous matrices?
Researchers reviewed the potential of flow cytometry — a technique that rapidly analyzes individual particles — as a high-throughput tool for detecting micro- and nanoplastics in water samples, finding it excels at measuring particles smaller than 20 micrometers that other methods struggle to detect. Using fluorescent dyes to tag plastics, the approach could enable near-real-time environmental monitoring at a scale no other current technique can match.
Novel methodology for identification and quantification of microplastics in biological samples
Researchers validated a protocol for identifying and quantifying polyethylene microplastics in biological samples, finding that membrane filtration caused particle retention problems and that flow cytometry offered a more reliable alternative for analysis of biological digests.
Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification
Not relevant to microplastics — this study compares machine learning and chemometric methods (including PCA, SVM, and neural networks) for classifying plastic waste types using a handheld near-infrared spectrometer, focused on improving plastic recycling sorting rather than microplastic detection.
A novel high-throughput analytical method to quantify microplastics in water by flow cytometry
Researchers developed a faster, high-throughput method using flow cytometry — a technology that rapidly counts and characterizes particles in liquid — to measure microplastics in water, achieving about 97% accuracy across multiple plastic types and sizes and offering a practical alternative to slow, labor-intensive microscopy-based counting.
Preliminary Results From Detection of Microplastics in Liquid Samples Using Flow Cytometry
Researchers developed a novel flow cytometry approach for in-situ detection and quantification of microplastics in liquid samples using fluorescent staining, testing nine polymer types under controlled laboratory conditions. The method offers a high-throughput alternative to traditional time-consuming microplastic detection protocols that risk sample contamination.
Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data
This paper presents a new computational method for analyzing single-cell genomic data by clustering cells based on both their gene expression and chromatin accessibility patterns. The technique uses a skip aggregation network to better integrate information from multiple data types. While not related to microplastics, this type of advanced analytical tool could potentially be applied to study how microplastic exposure affects gene expression at the single-cell level in human tissues.
Effect of medium refractive index on microparticle characterization by optical scattering
Researchers investigated how the refractive index of the medium affects optical scattering measurements used to characterize microplastic particles, finding that medium choice significantly influences size estimation accuracy. Machine learning was applied to improve classification of particles under varying optical conditions.
Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study
Researchers developed a label-free method to identify microplastics inside living human cells using enhanced dark-field microscopy combined with deep learning, achieving high classification accuracy for polystyrene microparticles differing only in pigmentation.
Flow Cytometry as a Rapid Alternative to Quantify Small Microplastics in Environmental Water Samples
Researchers developed a flow cytometry method using fluorescent staining to rapidly detect and quantify small microplastics (1-50 micrometers) in environmental water samples, achieving over 80% recovery rates and significantly reducing analysis time compared to traditional microscopy.