Worldwide distribution of glomerular diseases: the role of renal biopsy registries

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Genome-wide association studies in kidney diseases: Quo Vadis

A genome-wide association (GWA) study is a genetic epidemiology approach designed to scan genetic variation across the entire human genome in order to identify genetic associations with phenotypic traits as well as the presence or absence of a disease. Hundreds of thousands of single-nucleotide polymorphisms (SNPs), the most common form of genetic variant, serve as markers. SNPs are assayed and related to diseases or health-related conditions applying bioinformatics algorithms. This has become feasible thanks to the recent technological improvements in the so-called high-throughput technologies. The analysis identifies regions (loci) with statistically significant differences in allele or genotype frequencies between cases and controls and so the variations are said to be ‘associated’ with the diseas

A Narrative Review on C3 Glomerulopathy: A Rare Renal Disease

In April 2012, a group of nephrologists organized a consensus conference in Cambridge (UK) on type II membranoproliferative glomerulonephritis and decided to use a new terminology, "C3 glomerulopathy" (C3 GP). Further knowledge on the complement system and on kidney biopsy contributed toward distinguishing this disease into three subgroups: dense deposit disease (DDD), C3 glomerulonephritis (C3 GN), and the CFHR5 nephropathy. The persistent presence of microhematuria with or without light or heavy proteinuria after an infection episode suggests the potential onset of C3 GP. These nephritides are characterized by abnormal activation of the complement alternative pathway, abnormal deposition of C3 in the glomeruli, and progression of renal damage to end-stage kidney disease. The diagnosis is based on studying the complement system, relative genetics, and kidney biopsies. The treatment gap derives from the absence of a robust understanding of their natural outcome. Therefore, a specific treatment for the different types of C3 GP has not been established. Recommendations have been obtained from case series and observational studies because no randomized clinical trials have been conducted. Current treatment is based on corticosteroids and antiproliferative drugs (cyclophosphamide, mycophenolate mofetil), monoclonal antibodies (rituximab) or complement inhibitors (eculizumab). In some cases, it is suggested to include sessions of plasma exchange

Kaposi's sarcoma in renal transplant recipients--the impact of proliferation signal inhibitors

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Dzyaloshinskii-Moriya interaction and Hall effects in the skyrmion phase of MnFeGe alloys

We carry out density functional theory calculations which demonstrate that the electron dynamics in the skyrmion phase of Fe-rich Mn$_{1-x}$Fe$_x$Ge alloys is governed by Berry phase physics. We observe that the magnitude of the Dzyaloshinskii-Moriya interaction, directly related to the mixed space-momentum Berry phases, changes sign and magnitude with concentration $x$ in direct correlation with the data of Shibata {\it et al.}, Nature Nanotech. {\bf 8}, 723 (2013). The computed anomalous and topological Hall effects in FeGe are also in good agreement with available experiments. We further develop a simple tight-binding model able to explain these findings. Finally, we show that the adiabatic Berry phase picture is violated in the Mn-rich limit of the alloys.Comment: 5 page

Oleogelation of extra virgin olive oil by different gelators affects lipid digestion and polyphenol bioaccessibility

The possibility to steer extra virgin olive oil (EVOO) digestion and polyphenol bioaccessibility through oleogelation was investigated. EVOO was converted into oleogels using lipophilic (monoglycerides, rice wax, sunflower wax, phytosterols) or hydrophilic (whey protein aerogel particles, WP) gelators. In-vitro digestion demonstrated that the oleogelator nature influenced both lipid digestion and polyphenol bioaccessibility. WP-based oleogels presented ∼100% free fatty acid release compared to ∼64% for unstructured EVOO and ∼40 to ∼55% for lipophilic-based oleogels. This behavior was attributed to the ability of WP to promote micelle formation through oleogel destructuring. Contrarily, the lower lipolysis of EVOO gelled with lipophilic gelators compared to unstructured EVOO suggested that the gelator obstructed lipase accessibility. Tyrosol and hydroxytyrosol bioaccessibility increased for WP oleogels (∼27%), while liposoluble-based oleogels reduced it by 7 to 13%. These findings highlight the deep effect of the gelator choice on the digestion fate of EVOO components in the human body

Exercise and physical performance in older adults with sarcopenic obesity: a systematic review

Sarcopenic obesity is characterized by low muscle mass and high body fat; prevalence increases with age, particularly after age 65 years. For this systematic literature review we searched scientific databases for studies on exercise interventions for improving physical performance in adults with sarcopenic obesity; also, we identified potential gaps in clinical practice guidelines that need to be addressed

New perspectives in the prediction of postoperative complications for high-risk ulcerative colitis patients: machine learning preliminary approach

OBJECTIVE: Patients with acute severe and medical refractory ulcerative colitis have a high risk of postoperative complications after total abdominal colectomy (TAC). The objective of this retrospective study is to use machine learning to analyze and predict short-term outcomes. PATIENTS AND METHODS: 32 patients with ulcerative colitis were treated with total abdominal colectomy between 2011 and 2017. Biographical data, preoperative therapy, blood chemistry, nutritional status, surgical technique, blood transfusion and preoperative length of stay were the features selected for the statistical analyses and were used as input for the machine learning algorithms to predict the rate of complications. RESULTS: Traditional statistical analysis showed an overall postoperative morbidity rate of 34% and a mortality rate of 3%. Preoperative low serum albumin levels (4 days), blood transfusions (≥1 unit) and body temperature (≥37.5°C) demonstrated a major impact on infectious morbidity with statistical significance (p<0.05). Patients treated with steroids and rescue therapy presented a higher risk of minor infectious complications (p<0.05). Evaluating only preoperative features, machine learning algorithms were able to predict minor postoperative complications with a high strike rate (84.3%), high sensitivity (87.5%) and high specificity (83.3%) during the testing phase. CONCLUSIONS: Machine learning is demonstrated to be useful in predicting the rate of minor postoperative complications in high-risk ulcerative colitis patients, despite the small sample size. It represents a major step forward in data analysis by implementing a retrospective study from a prospective point of view

SIR-C/X-SAR data calibration and ground truth campaign over the NASA-CB1 test-site

During the Space Shuttle Endeavour mission in October 1994, a remote-sensing campaign was carried out with the objectives of both radiometric and polarimetric calibration and ground truth data acquisition of bare soils. This paper presents the results obtained in the experiment. Polarimetric cross-talk and channel imbalance values, as well as radiometric calibration parameters, have been found to be within the science requirements for SAR images. Regarding ground truth measurements, a wide spread in the height rms values and correlation lengths has been observed, which has motivated a critical revisiting of surface parameters descriptors

A comparative study of covariance selection models for the inference of gene regulatory networks

Display Omitted Three different models for inferring gene networks from microarray data are proposed.The most sensitive approach is selected by an exhaustive simulation study.The method reveals a cross-talk between the isoprenoid biosynthesis pathways in Arabidopsis thaliana.The method highlights 9 genes in HRAS signature regulated by the transcription factor RREB1. MotivationThe inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. ResultsIn this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) '?2C' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ?2C outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. AvailabilitySoftware implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip