scholarly journals Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination

2021 ◽  
Vol 11 (13) ◽  
pp. 6142
Author(s):  
José Luis Ganoza-Quintana ◽  
Félix Fanjul-Vélez ◽  
José Luis Arce-Diego
Keyword(s):  
Fractal Dimension ◽  
Phase Contrast ◽  
Tissue Type ◽  
P Value ◽  
Phase Imaging ◽  
The Novel ◽  
Label Free ◽  
External Scale ◽  
Tissue Slices ◽  
Fresh Tissue

Histology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel use of phase contrast in clinical digital histology to improve diagnosis. The use of label-free fresh tissue slices prevents processing artifacts and reduces processing time. Phase contrast parameters are implemented and calculated: the external scale, the fractal dimension, the anisotropy factor, the scattering coefficient, and the refractive index variance. Images of healthy and tumoral samples of liver, colon, and kidney are employed. A total of 252 images with 10×, 20×, and 40× magnifications are measured. Discrimination significance between healthy and tumoral tissues is assessed statistically with ANOVA (p-value < 0.005). The analysis is made for each tissue type and for different magnifications. It shows a dependence on tissue type and image magnification. The p-value of the most significant parameters is below 10−5. Liver and colon tissues present a great overlap in significant phase contrast parameters. The 10× fractal dimension is significant for all tissue types under analysis. These results are promising for the use of phase contrast in digital histology clinical praxis.

scholarly journals Real-time halo correction in phase contrast imaging

2017 ◽  
Author(s):  
Mikhail E. Kandel ◽  
Michael Fanous ◽  
Catherine Best-Popescu ◽  
Gabriel Popescu
Keyword(s):  
Real Time ◽  
Phase Contrast ◽  
A Priori ◽  
Spatial Coherence ◽  
Low Frequency ◽  
Nondestructive Method ◽  
Label Free ◽  
Contrast Imaging ◽  
Tissue Slices ◽  
Time Operation

AbstractAs a label-free, nondestructive method, phase contrast is by far the most popular microscopy technique for routine inspection of cell cultures. Yet, features of interest such as extensions near cell bodies are often obscured by a glow, which came to be known as the halo. Advances in modeling image formation have shown that this artifact is due to the limited spatial coherence of the illumination. Yet, the same incoherent illumination is responsible for superior sensitivity to fine details in the phase contrast geometry. Thus, there exists a trade-off between high-detail (incoherent) and low-detail (coherent) imaging systems. In this work, we propose a method to break this dichotomy, by carefully mixing corrected low-frequency and high-frequency data in a way that eliminates the edge effect. Specifically, our technique is able to remove halo artifacts at video rates, requiring no manual interaction ora prioripoint spread function measurements. To validate our approach, we imaged standard spherical beads, sperm cells, tissue slices, and red blood cells. We demonstrate the real-time operation with a time evolution study of adherent neuron cultures whose neurites are revealed by our halo correction. We show that with our novel technique, we can quantify cell growth in large populations, without the need for thresholds and calibration.

scholarly journals Label-free imaging of collagen fibers in tissue slices using phase imaging with computational specificity

2021 ◽  
Author(s):  
Masayoshi Sakakura ◽  
Gabriel Popescu ◽  
Andre Kajdacsy-Balla ◽  
Virgilia Macias
Keyword(s):  
Second Harmonic ◽  
Imaging Modality ◽  
High Specificity ◽  
Ground Truth ◽  
Collagen Fibers ◽  
Nonlinear Susceptibility ◽  
Phase Imaging ◽  
Label Free ◽  
Tissue Slices ◽  
Disease Stratification

Evaluating the tissue collagen content in addition to the epithelial morphology has been proven to offer complementary information in histopathology, especially in disease stratification and patient survivability prediction. One imaging modality widely used for this purpose is second harmonic generation microscopy (SHGM), which reports on the nonlinear susceptibility associated with the collagen fibers. Another method is polarization light microscopy (PLM) combined with picrosirius-red (PSR) tissue staining. However, SHGM requires expensive equipment and provides limited throughput, while PLM and PSR staining are not part of the routine pathology workflow. Here, we advance phase imaging with computational specificity (PICS) to computationally infer the collagen distribution of unlabeled tissue, with high specificity. PICS utilizes deep learning to translate quantitative phase images (QPI) into corresponding PSR images with high accuracy and speed. Our results indicate that the distributions of collagen fiber orientation, length, and straightness reported by PICS closely match the ones from ground truth.

scholarly journals Stain-free nucleus identification in holographic learning flow cyto-tomography

2021 ◽  
Author(s):  
Daniele Pirone ◽  
Joowon Lim ◽  
Francesco Merola ◽  
Lisa Miccio ◽  
Martina Mugnano ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Three Dimensional ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Segmentation Method ◽  
Free Cells ◽  
Significant Step ◽  
Learning Flow ◽  
Free Nucleus

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

Label-Free Mass Spectrometry-Based Plasma Proteomics Identified LY6D, DSC3, CDSN, SERPINB12, and SLURP1,as Novel Protein Biomarkers For Pulmonary Tuberculosis

2019 ◽  
Vol 17 ◽  
Author(s):  
Xiaoli Yu ◽  
Lu Zhang ◽  
Na Li ◽  
Peng Hu ◽  
Zhaoqin Zhu ◽  
...  
Keyword(s):  
Pulmonary Tuberculosis ◽  
Search Algorithm ◽  
Differentially Expressed ◽  
P Value ◽  
Plasma Samples ◽  
Label Free ◽  
Protein Biomarkers ◽  
Protein Database ◽  
Leave One Out ◽  
Potential Biomarkers

Aim: We aimed to identify new plasma biomarkers for the diagnosis of Pulmonary tuberculosis. Background: Tuberculosis is an ancient infectious disease that remains one of the major global health problems. Until now, effective, convenient, and affordable methods for diagnosis of Pulmonary tuberculosis were still lacked. Objective: This study focused on construct a label-free LC-MS/MS based comparative proteomics between six tuberculosis patients and six healthy controls to identify differentially expressed proteins (DEPs) in plasma. Method: To reduce the influences of high-abundant proteins, albumin and globulin were removed from plasma samples using affinity gels. Then DEPs from the plasma samples were identified using a label-free Quadrupole-Orbitrap LC-MS/MS system. The results were analyzed by the protein database search algorithm SEQUEST-HT to identify mass spectra to peptides. The predictive abilities of combinations of host markers were investigated by general discriminant analysis (GDA), with leave-one-out cross-validation. Results: A total of 572 proteins were identified and 549 proteins were quantified. The threshold for differentially expressed protein was set as adjusted p-value < 0.05 and fold change ≥1.5 or ≤0.6667, 32 DEPs were found. ClusterVis, TBtools, and STRING were used to find new potential biomarkers of PTB. Six proteins, LY6D, DSC3, CDSN, FABP5, SERPINB12, and SLURP1, which performed well in the LOOCV method validation, were termed as potential biomarkers. The percentage of cross-validated grouped cases correctly classified and original grouped cases correctly classified is greater than or equal to 91.7%. Conclusion: We successfully identified five candidate biomarkers for immunodiagnosis of PTB in plasma, LY6D, DSC3, CDSN, SERPINB12, and SLURP1. Our work supported this group of proteins as potential biomarkers for pulmonary tuberculosis, and be worthy of further validation.

Label-free second-harmonic phase imaging of biological specimen by digital holographic microscopy

2010 ◽  
Vol 35 (24) ◽  
pp. 4102 ◽  
Author(s):  
Etienne Shaffer ◽  
Corinne Moratal ◽  
Pierre Magistretti ◽  
Pierre Marquet ◽  
Christian Depeursinge
Keyword(s):  
Second Harmonic ◽  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Label Free ◽  
Biological Specimen ◽  
Harmonic Phase ◽  
Holographic Microscopy ◽  
Harmonic Phase Imaging

scholarly journals Label-free cell viability assay using phase imaging with computational specificity

2020 ◽  
Author(s):  
Chenfei Hu ◽  
Shenghua He ◽  
Young Jae Lee ◽  
Yuchen He ◽  
Edward M. Kong ◽  
...  
Keyword(s):  
Cell Viability ◽  
Hela Cells ◽  
Free Cell ◽  
Phase Imaging ◽  
Label Free ◽  
Cell Dynamics ◽  
Cell Viability Assay ◽  
Chemical Reagents ◽  
Viability Assay ◽  
Cell Staining

AbstractExisting approaches to evaluate cell viability involve cell staining with chemical reagents. However, this step of exogenous staining makes these methods undesirable for rapid, nondestructive and long term investigation. Here, we present instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity (PICS). This new concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by quantitative phase imaging. Demonstrated on HeLa cells culture, the proposed method reports approximately 95% accuracy in identifying injured and dead cells. Further comparison of cell morphology with labeled HeLa cells suggests that potential adverse effect on cell dynamics introduced by the viability reagents can be avoided using the label-free investigation method, which would be valuable for a broad range of biomedical applications.

scholarly journals Tensile and transverse strength of novel copolymers for denture base.

2019 ◽  
Vol S (1) ◽  
pp. 7-10
Author(s):  
Ahmed Asim Saeed Al-Ali ◽  
◽  
Ammar k. Al-Noori ◽  
Amer A. Taqa ◽  
◽  
...  
Keyword(s):  
Acrylic Resin ◽  
P Value ◽  
Transverse Strain ◽  
The Novel ◽  
Denture Base ◽  
Transverse Strength ◽  
Base Materials ◽  
Propenoic Acid ◽  
Significant Difference ◽  
Transverse Strains

Objectives: Compare tensile and transverse strength of new copolymers for denture base. Materials and methods: The specimens were prepared from heat cured acrylic resin with three types of additives: Acryester B, Ethoxycarbonylethylene, and Propenoic acid at a percentage of 5% and 10%. The tensile and transverse strains were tested, recorded and compared. Results: The analysis of variance display statistically significant difference. The p-value was 0.001 for each of tensile and transverse strain tests. Conclusions: The tensile strength of the novel copolymers increased. The transverse strength of some of the novel copolymers increased.

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