title: Automatic detection of hypoxia in renal tissue stained with HIF-1alpha.
abstract: 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.
  
  
  
  17 images of renal tissues from male rats of Winstar lineage; overall, there were 12.587 objects (cells) in the images for analysis. The acquired images were then analyzed through the free softwares CellProfiler (version 2.1.1) and CellProfiler Analyst (version 2.0). In the software CellProfiler Anlyst, there was a separation with the classes of the object, using a classifier, and the classes were: 1) class with HIF-alpha and 2) class without HIF-alpha.
  
  
  
  With the data obtained through Score All, it was possible to calculate the percentage of cells that had HIF-alpha; out of 12.587 objects of the sample, 6.773 (54%) had HIF-alpha and 5.814 (46%) did not have HIF-alpha. Data of sensibility 0.90, specificity 0.84 and standard deviation 0.10 and 0.12.
  
  
  
  The research shows that the free software CellProfiler, through the light microscope, was able to identify the stains, perform the machine's learning, and subsequently count and separate cells from distinct classes (with and without the stain of HIF-alpha).
authors.affiliation: Pharmaceutical Sciences, University of Vila Velha, Brazil.
authors.forename: Aline Rodrigues
authors.initials: AR
authors.lastname: Buzin
authors.affiliation: Pharmaceutical Sciences, University of Vila Velha, Brazil.
authors.forename: Nayana Damiani
authors.initials: ND
authors.lastname: Macedo
authors.affiliation: Department of Morphology, Federal University of Espirito Santo, Brazil; Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden (TUD), Dresden, Germany.
authors.forename: Isabela Bastos Binotti Abreu
authors.initials: IB
authors.lastname: De Araujo
authors.affiliation: Department of Morphology, Federal University of Espirito Santo, Brazil.
authors.forename: Breno Valentim
authors.initials: BV
authors.lastname: Nogueira
authors.affiliation: Pharmaceutical Sciences, University of Vila Velha, Brazil.
authors.forename: Tadeu Uggere
authors.initials: TU
authors.lastname: de Andrade
authors.affiliation: Pharmaceutical Sciences, University of Vila Velha, Brazil.
authors.forename: Denise Coutinho
authors.initials: DC
authors.lastname: Endringer
authors.affiliation: Pharmaceutical Sciences, University of Vila Velha, Brazil. Electronic address: dominik.lenz@gmail.com.
authors.forename: Dominik
authors.initials: D
authors.lastname: Lenz
mesh_terms: D000818:Animals; D057205:Automation, Laboratory; D015415:Biomarkers / Q000032:analysis; D015687:Cell Hypoxia; D051795:Hypoxia-Inducible Factor 1, alpha Subunit / Q000032:analysis*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry / Q000295:instrumentation*; D007668:Kidney / Q000737:chemistry*; D000069550:Machine Learning; D008297:Male; D017208:Rats, Wistar; D012984:Software
chemical_list: D015415:Biomarkers; C497444:Hif1a protein, rat; D051795:Hypoxia-Inducible Factor 1, alpha Subunit
keywords: HIF 1α; Hypoxia; Image analysis; Machine learning