[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Gangadhar et al., 2022 - Google Patents

Staining-free, in-flow enumeration of tumor cells in blood using digital holographic microscopy and deep learning

Gangadhar et al., 2022

View PDF
Document ID
9102495325560770093
Author
Gangadhar A
Sari-Sarraf H
Vanapalli S
Publication year
Publication venue
bioRxiv

External Links

Snippet

Currently, detection of circulating tumor cells (CTCs) in cancer patient blood samples relies on immunostaining, which does not provide access to live CTCs, limiting the breadth of CTC- based applications. As a first step to address this limitation, here, we demonstrate staining …
Continue reading at www.biorxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay
    • G01N33/569—Immunoassay; Biospecific binding assay for micro-organisms, e.g. protozoa, bacteria, viruses
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N2015/0065—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials biological, e.g. blood
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/40—Concentrating samples
    • G01N1/4077—Concentrating samples by other techniques involving separation of suspended solids

Similar Documents

Publication Publication Date Title
US20230384208A1 (en) Image-based cell sorting systems and methods
Rees et al. Imaging flow cytometry
Singh et al. Label-free, high-throughput holographic screening and enumeration of tumor cells in blood
Heo et al. Real-time image processing for microscopy-based label-free imaging flow cytometry in a microfluidic chip
JP7361149B2 (en) High-precision 5-part Differential using digital holography microscopy and untouched peripheral blood leukocytes
Wang et al. Label-free detection of rare circulating tumor cells by image analysis and machine learning
Li et al. Cell morphology-based machine learning models for human cell state classification
Ayyappan et al. Identification and staging of B-cell acute lymphoblastic leukemia using quantitative phase imaging and machine learning
US11092532B2 (en) Method and device for label-free, high throughput holographic screening and enumeration of tumor cells in blood
KR20230156069A (en) Systems and methods for cell analysis
Gangadhar et al. Deep learning assisted holography microscopy for in-flow enumeration of tumor cells in blood
Zhang et al. Computational cytometer based on magnetically modulated coherent imaging and deep learning
Shu et al. Artificial‐intelligence‐enabled reagent‐free imaging hematology analyzer
Lin et al. Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology
Singh et al. Label-free fingerprinting of tumor cells in bulk flow using inline digital holographic microscopy
Otesteanu et al. A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics
Govindaraju et al. Machine learning enabled leukocyte quantification using smartphone coupled 3D printed microfluidic biosensor
Gangadhar et al. Staining-free, in-flow enumeration of tumor cells in blood using digital holographic microscopy and deep learning
Saeki et al. Digital cell counting device integrated with a single-cell array
Combs et al. Deep learning assisted mechanotyping of individual cells through repeated deformations and relaxations in undulating channels
Aslan et al. Smartphone imaging flow cytometry for high-throughput single-cell analysis
US20220180515A1 (en) Artificial intelligence enabled reagent-free imaging hematology analyzer
Kumar et al. Automated motion tracking and data extraction for red blood cell biomechanics
Liu et al. Deep Learning–Based 3D Single-Cell Imaging Analysis Pipeline Enables Quantification of Cell–Cell Interaction Dynamics in the Tumor Microenvironment
Han et al. Physicochemical profiling of macrophage heterogeneity using deep learning integrated nanosensor cytometry