Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections.

Background: Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia.

Automatic detection of hypoxia in renal tissue stained with HIF-1alpha.

Objective: The objective of this study was the identification of the stain HIF-alpha using the Image Cytometry, and to help to count the positive cells (with HIF-alpha) and the negative cells (without HIF-alpha) from the same sample.

Method: 17 images of renal tissues from male rats of Winstar lineage; overall, there were 12.587 objects (cells) in the images for analysis.

Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue.

Objective: The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation.

Methods: A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.

Replacement of specific markers for apoptosis and necrosis by nuclear morphology for affordable cytometry.

Objective: The objective of the present study was to employ high throughput image analysis to detect necrosis and apoptosis. Specific markers were replaced by morphological parameters of cells and nuclei.

Method: Fresh blood was taken from a healthy female and given a treatment to induce cell necrosis and apoptosis.