Deep-learning-driven quantification of interstitial fibrosis in digitized kidney biopsies

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.U01 DK085660 - NIDDK NIH HHS; RF1 AG062109 - NIA NIH HHS; R21 CA253498 - NCI NIH HHS; R21 DK119751 - NIDDK NIH HHS; R01 HL132325 - NHLBI NIH HHS; UL1 TR001430 - NCATS NIH HHS; R56 AG062109 - NIA NIH HHS; R21 DK119740 - NIDDK NIH HHShttps://www.medrxiv.org/content/10.1101/2021.01.03.21249179v1.full.pd

Water resource management in the context of a non-potable water reuse case study in arid climate

This study evaluates an existing non-potable water system serving outdoor services for a medical facility case study (MFCS) in Abu Dhabi (AD), United Arab Emirates, using mixed methods research to identify water demand and availability of non-potable water, and to optimize water reuse for reducing waste, energy consumption and greenhouse gas emissions (GHG). The MFCS footprint includes 50% landscaping. The water used for irrigation is from non-clinical/non-potable water, treated condensate water, a by-product of air conditioning. For 5 months per year, there is a predicted non-potable water deficit, so costly and non-sustainable desalinated potable water is required for irrigation. The findings include that there is a nonpotable water deficit due to an excessive consumption for landscape irrigation (LI) and water features (WF), and that 177,288 m3 of condensate and desalinated water was wasted (equivalent to 71 Olympic swimming pools). The contribution of this research is to demonstrate that water wastage, a contributor to GHG emissions, is due to inadequate field testing and verification, water tank storage problems and a lack of LI and WF water demand management. Strategies to address these issues are suggested and will be useful to building owners, operations and maintenance teams and facility managers to substantially decrease water consumption in any type of buildings with a non-potable water system, as well as helping AD to achieve its target of a 22% reduction in GHG emissions by 2030 (Environment AgencyAbu Dhabi (EAD 2017))

Inflammation-Induced Adverse Pregnancy and Neonatal Outcomes Can Be Improved by the Immunomodulatory Peptide Exendin-4

Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Inflammation is causally linked to preterm birth; therefore, finding an intervention that dampens maternal and fetal inflammatory responses may provide a new strategy to prevent adverse pregnancy and neonatal outcomes. Using animal models of systemic maternal inflammation [intraperitoneal injection of lipopolysaccharide (LPS)] and fetal inflammation (intra-amniotic administration of LPS), we found that (1) systemic inflammation induced adverse pregnancy and neonatal outcomes by causing a severe maternal cytokine storm and a mild fetal cytokine response; (2) fetal inflammation induced adverse pregnancy and neonatal outcomes by causing a mild maternal cytokine response and a severe fetal cytokine storm; (3) exendin-4 (Ex4) treatment of dams with systemic inflammation or fetal inflammation improved adverse pregnancy outcomes by modestly reducing the rate of preterm birth; (4) Ex4 treatment of dams with systemic, but not local, inflammation considerably improved neonatal outcomes, and such neonates continued to thrive; (5) systemic inflammation facilitated the diffusion of Ex4 through the uterus and the maternal–fetal interface; (6) neonates born to Ex4-treated dams with systemic inflammation displayed a similar cytokine profile to healthy control neonates; and (7) treatment with Ex4 had immunomodulatory effects by inducing an M2 macrophage polarization and increasing anti-inflammatory neutrophils, as well as suppressing the expansion of CD8+ regulatory T cells, in neonates born to dams with systemic inflammation. Collectively, these results provide evidence that dampening maternal systemic inflammation through novel interventions, such as Ex4, can improve the quality of life for neonates born to women with this clinical condition

High-order computation of burning propellant surface and simulation of fluid flow in solid rocket chamber

International audienceIn this paper, we present a numerical approach for predicting fluid flows in solid rocket motor (SRM) chambers. We use a novel high-order technique to track the burning grain surface. Spectral convergence toward the exact burning surface is achieved thanks to Fourier differentiation. In addition, we make use of a body-fitted mesh deforming with the burning surface and present a method to avoid manual remeshing. We describe several methods to deform the volume mesh and to keep good mesh element quality during the computation. We then couple the surface and volume approaches. The resulting coupled method is able to handle the formation of geometric singularities on the burning surface while keeping constant surface and volume mesh topology. This geometrical approach is integrated into a complex code for compressible, multi-species, turbulent flow simulations. Applications to the simulation of the internal flow in realistic solid rocket motors with complex grain geometry are then presented