Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis.
TLDR
Current instrumentation and analytical procedures will likely be overcome by the unceasing progress in new devices for AF detection and data interpretation, while a progress is expected in the search and characterization of endogenous fluorophores and their roles as intrinsic biomarkers.Abstract:
Native fluorescence, or autofluorescence (AF), consists in the emission of light in the UV-visible, near-IR spectral range when biological substrates are excited with light at suitable wavelength. This is a well-known phenomenon, and the strict relationship of many endogenous fluorophores with morphofunctional properties of the living systems, influencing their AF emission features, offers an extremely powerful resource for directly monitoring the biological substrate condition. Starting from the last century, the technological progresses in microscopy and spectrofluorometry were convoying attention of the scientific community to this phenomenon. In the future, the interest in the autofluorescence will certainly continue. Current instrumentation and analytical procedures will likely be overcome by the unceasing progress in new devices for AF detection and data interpretation, while a progress is expected in the search and characterization of endogenous fluorophores and their roles as intrinsic biomarkers.read more
Citations
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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.
Yair Rivenson,Hongda Wang,Zhensong Wei,Kevin de Haan,Yibo Zhang,Yichen Wu,Harun Gunaydin,Jonathan E. Zuckerman,Thomas Chong,Anthony Sisk,Lindsey M Westbrook,W. Dean Wallace,Aydogan Ozcan +12 more
TL;DR: It is shown that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples.
Journal ArticleDOI
The Striatum Organizes 3D Behavior via Moment-to-Moment Action Selection
Jeffrey E. Markowitz,Jeffrey E. Markowitz,Winthrop F. Gillis,Celia Beron,Celia Beron,Shay Q. Neufeld,Shay Q. Neufeld,Keiramarie Robertson,Keiramarie Robertson,Neha D. Bhagat,Ralph E. Peterson,Emalee Peterson,Minsuk Hyun,Minsuk Hyun,Scott W. Linderman,Bernardo L. Sabatini,Bernardo L. Sabatini,Sandeep Robert Datta +17 more
TL;DR: These experiments reveal that DLS neurons systematically encode information about the identity and ordering of sub-second 3D behavioral motifs; this encoding is facilitated by fast-timescale decorrelations between the direct and indirect pathways.
Journal ArticleDOI
Redox Signaling by Reactive Electrophiles and Oxidants
TL;DR: This Review reflects on five aspects critical for understanding how nature harnesses these noncanonical post-translational modifications to coordinate distinct cellular activities: specific players and their generation, physicochemical properties, mechanisms of action, methods of interrogation, and functional roles in health and disease.
Journal ArticleDOI
Fluorescence Assay in Biology and Medicine
TL;DR: This volume is a remarkably complete summary of a rapidly advancing field of fluorescent measurement, with methods for assay of antibiotics such as griseofulvin and the tetracyclines, catecholamines, plus fluorescent microscopy and specific applications.
Journal ArticleDOI
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
TL;DR: In this paper, a convolutional neural network trained using a generative adversarial network model was used to transform an auto-fluorescence image of an unlabeled tissue section into an image that is equivalent to the bright-field image of the stained-version of the same sample.
References
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