Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
36,167
result(s) for
"staining"
Sort by:
Virtual staining for histology by deep learning
Deep learning-based virtual staining has the potential to replace chemical staining in histology and to provide more sustainable, rapid, and cost-effective pipelines for histopathology.Virtual staining will allow multiple outputs of stained images from a single input image of a tissue section, hence enabling virtual multiplexing and single-cell resolution between histological assessments that currently require separate tissue sections.Deep learning-based virtual staining models require large amounts of training data, and quantitative computational and rigorous histological validation is required for each intended use.
In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.
In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.
Journal Article
Detection of large extracellular silver nanoparticle rings observed during mitosis using darkfield microscopy
During studies on the absorption and interactions between silver nanoparticles and mammalian cells grown in vitro it was observed that large extracellular rings of silver nanoparticles were deposited on the microscope slide, many located near post-mitotic cells. Silver nanoparticles (AgNP, 80nm), coated with citrate, were incubated at concentrations of 0.3 to 30 μg/ml with a human-derived culture of retinal pigment epithelial cells (ARPE-19) and observed using darkfield and fluorescent microscopy, 24 h after treatment. Approximately cell-sized extracellular rings of deposited AgNP were observed on the slides among a field of dispersed individual AgNP. The mean diameter of 45 nanoparticles circles was 62.5 +/-12 microns. Ring structures were frequently observed near what appeared to be post-mitotic daughter cells, giving rise to the possibility that cell membrane fragments were deposited on the slide during mitosis, and those fragments selectively attracted and retained silver nanoparticles from suspension in the cell culture medium. These circular structures were observable for the following technical reasons: 1) darkfield microscope could observe single nanoparticles below 100 nm in size, 2) a large concentration (10 8 and 10 9 ) of nanoparticles was used in these experiments 3) negatively charged nanoparticles were attracted to adhesion membrane proteins remaining on the slide from mitosis. The observation of silver nanoparticles attracted to apparent remnants of cellular mitosis could be a useful tool for the study of normal and abnormal mitosis.
Journal Article
Principles and approaches for reproducible scoring of tissue stains in research
Evaluation of tissues is a common and important aspect of translational research studies. Labeling techniques such as immunohistochemistry can stain cells/tissues to enhance identification of specific cell types, cellular activation states, and protein expression. While qualitative evaluation of labeled tissues has merit, use of semiquantitative and quantitative scoring approaches can greatly enhance the rigor of the tissue data. Adhering to key principles for reproducible scoring can enhance the quality and reproducibility of the tissue data so as to maximize its biological relevance and scientific impact.
Journal Article
Observation and quantification of the morphological effect of trypan blue rupturing dead or dying cells
Trypan blue has long been the gold standard for staining dead cell to determine cell viability. The dye is excluded from membrane-intact live cells, but can enter and concentrate in membrane-compromised dead cells, rendering the cells dark blue. Over the years, there has been an understanding that trypan blue is inaccurate for cell viability under 80% without scientific support. We previously showed that trypan blue can alter the morphology of dead cells to a diffuse shape, which can lead to over-estimation of viability. Here, we investigate the origin of the dim and diffuse objects after trypan blue staining. Utilizing image and video acquisition, we show real-time transformation of cells into diffuse objects when stained with trypan blue. The same phenomenon was not observed when staining cells with propidium iodide. We also demonstrate the co-localization of trypan blue and propidium iodide, confirming these diffuse objects as cells that contain nuclei. The videos clearly show immediate cell rupturing after trypan blue contact. The formation of these diffuse objects was monitored and counted over time as cells die outside of the incubator. We hypothesize and demonstrate that rapid water influx may have caused the cells to rupture and disappear. Since some dead cells disappear after trypan blue staining, the total can be under-counted, leading to over-estimation of cell viability. This inaccuracy could affect the outcomes of cellular therapies, which require accurate measurements of immune cells that will be infused back into patients.
Journal Article
Arts & crafts stained glass
This \"is the first study of how the late-19th-century arts and crafts movement transformed the aesthetics and production of stained glass in Britain and America. A progressive school of artists, committed to direct involvement both in making and designing windows, emerged in the 1880s and 1890s, reinventing stained glass as a modern, expressive art form. Using innovative materials and techniques, they rejected formulaic Gothic Revivalism while seeking authentic, creative inspiration in medieval traditions. This new approach was pioneered by Christopher Whall (1849-1924), whose charismatic teaching educated a generation of talented pupils--both men and women--who produced intensely colorful and inventive stained glass, using dramatic, lyrical, and often powerfully moving design and symbolism\"-- Provided by publisher.
Book
Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show 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. A blind comparison, by board-certified pathologists, of this virtual staining method and standard histological staining using microscopic images of human tissue sections of the salivary gland, thyroid, kidney, liver and lung, and involving different types of stain, showed no major discordances. The virtual-staining method bypasses the typically labour-intensive and costly histological staining procedures, and could be used as a blueprint for the virtual staining of tissue images acquired with other label-free imaging modalities.
Deep learning can be used to virtually stain autofluorescence images of unlabelled tissue sections, generating images that are equivalent to the histologically stained versions.
Journal Article