Multiple scattering limits the contrast in optical imaging of thick specimens. Here, we present gradient light interference microscopy (GLIM) to extract three-dimensional information from both thin and thick unlabeled specimens. GLIM exploits a special case of low-coherence interferometry to extract phase information from the specimen, which in turn can be used to measure cell mass, volume, surface area, and their evolutions in time. Because it combines multiple intensity images that correspond to controlled phase shifts between two interfering waves, gradient light interference microscopy is capable of suppressing the incoherent background due to multiple scattering. GLIM can potentially become a valuable tool for in vitro fertilization, where contrast agents and fluorophores may impact the viability of the embryo. Since GLIM is implemented as an add-on module to an existing inverted microscope, we anticipate that it will be adopted rapidly by the biological community.Challenges in biological imaging include labeling, photobleaching and phototoxicity, as well as light scattering. Here, Nguyen et al. develop a quantitative phase method that uses low-coherence interferometry for label-free 3D imaging in scattering tissue.
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http://dx.doi.org/10.1038/s41467-017-00190-7 | DOI Listing |
J Clin Med
December 2024
Department of Urology, Health Science University Eskisehir City Health Application and Research Center, 26080 Eskisehir, Turkey.
To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected.
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December 2024
Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China.
A high-quality optical path alignment is essential for achieving superior image quality in optical biological microscope (OBM) systems. The traditional automatic alignment methods for OBMs rely heavily on complex masker-detection techniques. This paper introduces an innovative, image-sensor-based optical path alignment approach designed for low-power objective (specifically 4×) automatic OBMs.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Department of Next Generation Information Center, Kangwon National University Hospital, Chuncheon 24289, Republic of Korea.
Gastric cancer is a leading cause of cancer-related mortality, particularly in East Asia, with a notable burden in Republic of Korea. This study aimed to construct and develop machine learning models for the prediction of gastric cancer mortality and the identification of risk factors. All data were acquired from the Korean Clinical Data Utilization for Research Excellence by multiple medical centers in South Korea.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method.
View Article and Find Full Text PDFNew Phytol
January 2025
Amsterdam Institute for Life and Environment (A-LIFE), Section Systems Ecology, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081HV, Amsterdam, the Netherlands.
Lichens play important roles in habitat formation and community succession in polar and alpine ecosystems. Despite their significance, the ecological effects of lichen traits remain poorly researched. We propose a trait trade-off for managing light exposure based on climatic harshness.
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