Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues

2021 ◽  
Author(s):  
Christopher M. Polleys ◽  
Panagiotis Lymperopoulos ◽  
Hong-Thao Thieu ◽  
Elizabeth Genega ◽  
Liping Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Restoration ◽  
Functional Characterization ◽  
Label Free ◽  
Two Photon

Real-Time Deformability Cytometry: Label-Free Functional Characterization of Cells

2017 ◽  
pp. 347-369 ◽  
Author(s):  
Maik Herbig ◽  
Martin Kräter ◽  
Katarzyna Plak ◽  
Paul Müller ◽  
Jochen Guck ◽  
...  
Keyword(s):  
Real Time ◽  
Functional Characterization ◽  
Label Free ◽  
Deformability Cytometry

scholarly journals Proteomic Characterization of Cerebrospinal Fluid from Ataxia-Telangiectasia (A-T) Patients Using a LC/MS-Based Label-Free Protein Quantification Technology

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Monika Dzieciatkowska ◽  
Guihong Qi ◽  
Jinsam You ◽  
Kerry G. Bemis ◽  
Heather Sahm ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Biomarker Discovery ◽  
Functional Characterization ◽  
Therapeutic Targets ◽  
Bioinformatic Analysis ◽  
Protein Quantification ◽  
Label Free ◽  
Free Protein ◽  
Potential Biomarker

Cerebrospinal fluid (CSF) has been used for biomarker discovery of neurodegenerative diseases in humans since biological changes in the brain can be seen in this biofluid. Inactivation of A-T-mutated protein (ATM), a multifunctional protein kinase, is responsible for A-T, yet biochemical studies have not succeeded in conclusively identifying the molecular mechanism(s) underlying the neurodegeneration seen in A-T patients or the proteins that can be used as biomarkers for neurologic assessment of A-T or as potential therapeutic targets. In this study, we applied a high-throughput LC/MS-based label-free protein quantification technology to quantitatively characterize the proteins in CSF samples in order to identify differentially expressed proteins that can serve as potential biomarker candidates for A-T. Among 204 identified CSF proteins with high peptide-identification confidence, thirteen showed significant protein expression changes. Bioinformatic analysis revealed that these 13 proteins are either involved in neurodegenerative disorders or cancer. Future molecular and functional characterization of these proteins would provide more insights into the potential therapeutic targets for the treatment of A-T and the biomarkers that can be used to monitor or predict A-T disease progression. Clinical validation studies are required before any of these proteins can be developed into clinically useful biomarkers.

Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Shu Wang ◽  
Bingbing Lin ◽  
Guimin Lin ◽  
Ruolan Lin ◽  
Feng Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Label Free ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Label Free Detection

scholarly journals Label-free assessment of red blood cell storage lesions by deep learning

2018 ◽  
Author(s):  
M. Doan ◽  
J. A. Sebastian ◽  
R. N. Pinto ◽  
C. McQuin ◽  
A. Goodman ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Morphological Changes ◽  
Dark Field ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
Life Saving ◽  
Laboratory Procedures ◽  
Stored Blood

AbstractBlood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.Stored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.We demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.Deep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

Functional characterization of human brown adipose tissue metabolism

2020 ◽  
Vol 477 (7) ◽  
pp. 1261-1286 ◽  
Author(s):  
Marie Anne Richard ◽  
Hannah Pallubinsky ◽  
Denis P. Blondin
Keyword(s):  
Adipose Tissue ◽  
Brown Adipose Tissue ◽  
Functional Characterization ◽  
Human Infants ◽  
Positron Emission ◽  
Brown Adipose ◽  
Treatment Of Obesity ◽  
And Function

Brown adipose tissue (BAT) has long been described according to its histological features as a multilocular, lipid-containing tissue, light brown in color, that is also responsive to the cold and found especially in hibernating mammals and human infants. Its presence in both hibernators and human infants, combined with its function as a heat-generating organ, raised many questions about its role in humans. Early characterizations of the tissue in humans focused on its progressive atrophy with age and its apparent importance for cold-exposed workers. However, the use of positron emission tomography (PET) with the glucose tracer [18F]fluorodeoxyglucose ([18F]FDG) made it possible to begin characterizing the possible function of BAT in adult humans, and whether it could play a role in the prevention or treatment of obesity and type 2 diabetes (T2D). This review focuses on the in vivo functional characterization of human BAT, the methodological approaches applied to examine these features and addresses critical gaps that remain in moving the field forward. Specifically, we describe the anatomical and biomolecular features of human BAT, the modalities and applications of non-invasive tools such as PET and magnetic resonance imaging coupled with spectroscopy (MRI/MRS) to study BAT morphology and function in vivo, and finally describe the functional characteristics of human BAT that have only been possible through the development and application of such tools.

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