English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Determination of fundamental mechanisms of disease often hinges onhistopathology visualization and quantitative image analysis. Currently, theanalysis of multi-channel fluorescence tissue images is primarily achieved bymanual measurements of tissue cellular content and sub-cellular compartments.Since the current manual methodology for image analysis is a tedious andsubjective approach, there is clearly a need for an automated analyticaltechnique to process large-scale image datasets. Here, we introduce Nuquantus(Nuclei quantification utility software) - a novel machine learning-basedanalytical method, which identifies, quantifies and classifies nuclei based oncells of interest in composite fluorescent tissue images, in which cell bordersare not visible. Nuquantus is an adaptive framework that learns themorphological attributes of intact tissue in the presence of anatomicalvariability and pathological processes. Nuquantus allowed us to robustlyperform quantitative image analysis on remodeling cardiac tissue aftermyocardial infarction. Nuquantus reliably classifies cardiomyocyte versusnon-cardiomyocyte nuclei and detects cell proliferation, as well as cell deathin different cell classes. Broadly, Nuquantus provides innovative computerizedmethodology to analyze complex tissue images that significantly facilitatesimage analysis and minimizes human bias.

Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images