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E Brignardello
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M Gallo
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M Aragno
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R Manti
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E Tamagno
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O Danni
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G Boccuzzi
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The oxidative stress induced by high glucose concentration contributes to tissue damage associated with diabetes, including renal injury. Dehydroepiandrosterone (DHEA), the major secretory product of the human adrenal gland, has been shown to possess a multi-targeted antioxidant activity which is also effective against lipid peroxidation induced by high glucose. In this study we evaluated the effect of DHEA on the growth impairment which high glucose concentration induces in cultured rat mesangial cells. Primary cultures of rat mesangial cells were grown for 10 days in media containing either normal (i.e. 5.6 mmol/l) or high (i.e. 30 mmol/l) concentrations of glucose, without or with DHEA at different concentrations. The impairment of cell growth induced by high glucose was reversed by 100 nmol/l and 500 nmol/l DHEA, which had no effect on mesangial cells cultured in media containing glucose at the normal physiological concentration (5.6 mmol/l). In high-glucose cultured mesangial cells, DHEA also attenuated the lipid peroxidation, as measured by thiobarbituric acid reactive substances (TBARS) generation and 4-hydroxynonenal (HNE) concentration, and preserved the cellular content of reduced glutathione as well as the membrane Na+/K+ ATPase activity. The data further support the protective effect of DHEA against oxidative damage induced by high glucose concentrations, and bring into focus its possible effectiveness in preventing chronic complications of diabetes.

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L Strauss Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Turku Center for Disease Modeling, University of Turku, Turku, Finland

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A Junnila Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Turku Center for Disease Modeling, University of Turku, Turku, Finland

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A Wärri Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Turku Center for Disease Modeling, University of Turku, Turku, Finland

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M Manti Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden

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Y Jiang Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden

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E Löyttyniemi Department of Biostatistics, University of Turku, Turku, Finland

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E Stener-Victorin Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden

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M K Lagerquist Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden

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K Kukoricza Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Turku Center for Disease Modeling, University of Turku, Turku, Finland

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T Heinosalo Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Turku Center for Disease Modeling, University of Turku, Turku, Finland

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S Blom Aiforia Technologies Oyj, Pursimiehenkatu, Helsinki, Finland

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M Poutanen Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, and Turku Center for Disease Modeling, University of Turku, Turku, Finland
Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden

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The mouse estrous cycle is divided into four stages: proestrus (P), estrus (E), metestrus (M), and diestrus (D). The estrous cycle affects reproductive hormone levels in a wide variety of tissues. Therefore, to obtain reliable results from female mice, it is important to know the estrous cycle stage during sampling. The stage can be analyzed from a vaginal smear under a microscope. However, it is time-consuming, and the results vary between evaluators. Here, we present an accurate and reproducible method for staging the mouse estrous cycle in digital whole-slide images (WSIs) of vaginal smears. We developed a model using a deep convolutional neural network (CNN) in a cloud-based platform, Aiforia Create. The CNN was trained by supervised pixel-level multiclass semantic segmentation of image features from 171 hematoxylin-stained samples. The model was validated by comparing the results obtained by CNN with those of four independent researchers. The validation data included three separate studies comprising altogether 148 slides. The total agreement attested by the Fleiss kappa value between the validators and the CNN was excellent (0.75), and when D, E, and P were analyzed separately, the kappa values were 0.89, 0.79, and 0.74, respectively. The M stage is short and not well defined by the researchers. Thus, identification of the M stage by the CNN was challenging due to the lack of proper ground truth, and the kappa value was 0.26. We conclude that our model is reliable and effective for classifying the estrous cycle stages in female mice.

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