Diffusion-weighted magnetic resonance imaging to assess diffuse renal pathology: a systematic review and statement paper.

Diffusion-weighted magnetic resonance imaging (DWI) is a non-invasive method sensitive to local water motion in the tissue. As a tool to probe the microstructure, including the presence and potentially the degree of renal fibrosis, DWI has the potential to become an effective imaging biomarker. The aim of this review is to discuss the current status of renal DWI in diffuse renal diseases. DWI biomarkers can be classified in the following three main categories: (i) the apparent diffusion coefficient-an overall measure of water diffusion and microcirculation in the tissue; (ii) true diffusion, pseudodiffusion and flowing fraction-providing separate information on diffusion and perfusion or tubular flow; and (iii) fractional anisotropy-measuring the microstructural orientation. An overview of human studies applying renal DWI in diffuse pathologies is given, demonstrating not only the feasibility and intra-study reproducibility of DWI but also highlighting the need for standardization of methods, additional validation and qualification. The current and future role of renal DWI in clinical practice is reviewed, emphasizing its potential as a surrogate and monitoring biomarker for interstitial fibrosis in chronic kidney disease, as well as a surrogate biomarker for the inflammation in acute kidney diseases that may impact patient selection for renal biopsy in acute graft rejection. As part of the international COST (European Cooperation in Science and Technology) action PARENCHIMA (Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease), aimed at eliminating the barriers to the clinical use of functional renal magnetic resonance imaging, this article provides practical recommendations for future design of clinical studies and the use of renal DWI in clinical practice.EU COST Programm

Periodic fever syndromes in Eastern and Central European countries: results of a pediatric multinational survey

<p>Abstract</p> <p>Objective</p> <p>To analyze the prevalence of diagnosed and suspected autoinflammatory diseases in Eastern and Central European (ECE) countries, with a particular interest on the diagnostic facilities in these countries.</p> <p>Methods</p> <p>Two different strategies were used to collect data on patients with periodic fever syndromes from ECE countries- the Eurofever survey and collection of data with the structured questionnaire.</p> <p>Results</p> <p>Data from 35 centers in 14 ECE countries were collected. All together there were 11 patients reported with genetically confirmed familial Mediterranean fever (FMF), 14 with mevalonate-kinase deficiency (MKD), 11 with tumor necrosis factor receptor associated periodic syndrome (TRAPS) and 4 with chronic infantile neurological cutaneous and articular syndrome (CINCA). Significantly higher numbers were reported for suspected cases which were not genetically tested. All together there were 49 suspected FMF patients reported, 24 MKD, 16 TRAPS, 7 CINCA and 2 suspected Muckle-Wells syndrome (MWS) patients.</p> <p>Conclusions</p> <p>The number of genetically confirmed patients with periodic fever syndromes in ECE countries is very low. In order to identify more patients in the future, it is important to organize educational programs for increasing the knowledge on these diseases and to establish a network for genetic testing of periodic fever syndromes in ECE countries.</p

Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European Cooperation in Science and Technology Action PARENCHIMA

Functional renal magnetic resonance imaging (MRI) has seen a number of recent advances, and techniques are now available that can generate quantitative imaging biomarkers with the potential to improve the management of kidney disease. Such biomarkers are sensitive to changes in renal blood flow, tissue perfusion, oxygenation and microstructure (including inflammation and fibrosis), processes that are important in a range of renal diseases including chronic kidney disease. However, several challenges remain to move these techniques towards clinical adoption, from technical validation through biological and clinical validation, to demonstration of cost-effectiveness and regulatory qualification. To address these challenges, the European Cooperation in Science and Technology Action PARENCHIMA was initiated in early 2017. PARENCHIMA is a multidisciplinary pan-European network with an overarching aim of eliminating the main barriers to the broader evaluation, commercial exploitation and clinical use of renal MRI biomarkers. This position paper lays out PARENCHIMA’s vision on key clinical questions that MRI must address to become more widely used in patients with kidney disease, first within research settings and ultimately in clinical practice. We then present a series of practical recommendations to accelerate the study and translation of these techniques

SME Performance, Innovation and Networking - Evidence on Complementarities for a Local Economic System

The paper addresses the relevancy of networking activities and R&D as main drivers of productivity performance and ouput innovation, for small and medium enterprises (SME) playing in a local economic system. Given the intangible nature of many techno organisational innovation and networking strategies, original recent survey data for manufacturing and services are exploited. The aim is to provide new evidence on the complementarity relationships concerning different networking activities and R&D in a local SME oriented system in Northern Italy. We first introduce a methodological framework to empirically test complementarity among R&D and networking, in a discrete setting. Secondly, we consequently present empirical evidence on productivity drivers and on complementarity between R&D and networking strategies, with respect to firm productivity and process/product output innovation. R&D is a main driver of innovation and productivity, even without networking. This may signify, in association with the evidence on complementarity, that firm expenditures on R&D are a primary driver for performance. The complementarity with networking is a consequential step. Networking by itself cannot thus play a role in stimulating productivity and innovation. It can be a complementary factor in situations where cooperation and networking are needed to achieve economies of scale and/or to merge and integrate diverse skills, technologies and competencies. This is compatible with a framework where networking is the public good part of an impure public good wherein R&D plays the part of the private-led driving force towards structural break from the business as usual scenario. Managers and policy makers should be aware that in order to exploit asset complementarity, possibly transformed into competitive advantages, both R&D and networking are to be sustained and favoured. our evidence suggests that R&D may be a single main driver of performance. Since R&D expenditures are associated with firm size, a policy sustain is to be directed towards firm enlargement. After a certain threshold firms have the force to increase expenditures. The size effect is nevertheless non monotonous. Then, but not least important, for the majority of firms still remaining under a critical size threshold, policy incentives should be directed to R&D in connection with networking, through which a virtuous circle may arise. It is worth noting that it is not networking as such the main engine. Networking elements are crucially linked to innovation dynamics; it is nevertheless innovation that explains and drives networking, and not the often claimed mere existence of local spillovers or of a civic associative culture in the territory. Such public good factors exist but are likely to evolve with and be sustained by firm innovative dynamics

An artificial intelligence generated automated algorithm to measure total kidney volume in ADPKD

Introduction Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI) generated method for routinely measuring total kidney volume (TKV). Methods An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T MRI data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium which was first manually segmented by a single human operator. As an independent validation cohort, we utilised 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single centre. The tool was then implemented for clinical use and its performance analysed. Results The training / internal validation cohort was younger (mean age 44.0 vs 51.5 years) and the female-male ratio higher (1.2 v 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging Class 1, 86%). The median DICE score on the clinical validation dataset between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic dataset was 56 (±28) min whereas manual corrections of the algorithm output took 8.5 (±9.2) min per scan. Conclusions Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application

Environmental Efficiency, Emission Trends and Labour Productivity: Trade-Off or Joint Dynamics? Empirical Evidence Using NAMEA Panel Data

The paper provides new empirical evidence on the relationship between environmental efficiency and labour productivity using industry level data. We first provide a critical and extensive discussion around the interconnected issues of environmental efficiency and performance, firm performances and labour productivity, and environmental and non-environmental innovation dynamics. The most recent literature dealing with environmental innovation, environmental regulations and economic performances is taken as reference. We then test a newly adapted EKC hypothesis, by verifying the correlation between the two trends of environmental efficiency (productivity, namely sector emission on added value) and labour productivity (added value on employees) over a dynamic path. We exploit official NAMEA data sources for Italy over 1990-2002 for 29 sectoral branches. The period is crucial since environmental issues and then environmental policies came into the arena, and a restructuring of the economy occurred. It is thus interesting to assess the extent to which capital investments for the economy as a whole are associated with a positive or negative correlation between environmental efficiency of productive branches and labour productivity, often claimed by mainstream theory dealing with innovation in environmental economics. We believe that on the basis of the theoretical and empirical analyses focusing on innovation paths, firm performances and environmental externalities, there are good reasons to expect a positive correlation between environmental and labour productivities, or in alternative terms a negative correlation between mission intensity of production and labour productivity. The tested hypothesis is crucial within the long standing discussion over the potential trade-off or complementarity between environmental and labour productivity, strictly associated with sectoral and national technological innovation paths. The main added value of the paper is the analysis of the aforementioned hypothesis by exploiting a panel data set based on official NAMEA sectoral disaggregated accounting data, providing both cross section heterogeneity and a sufficient time span. We find that for most emissions, if not all, a negative correlation emerges between labour productivity and environmental productivity. Though this trend appears driven by the macro sectors services, manufacturing and industry, this evidence is not homogenous across emissions. In some cases U-shapes arise, mainly for services, and the assessment of Turning Points is crucial. Manufacturing and industry, all in all, seem to have a stronger weight. Overall, then, labour productivity dynamics seem to be complementary to a decreasing emission intensity of productive processes. The extent to which this evidence derives from endogenous market forces, industrial restructuring and/or from policy effects is scope for further research. The relative role of manufacturing and services in explaining this pattern is also to be analysed in future empirical analyses. In addition, the role of capital stocks and trade openness are extensions which may add value to future analyses carried out on the same NAMEA dataset

Short communication: Monitoring nutritional quality of Amiata donkey milk: Effects of lactation and productive season

Milk nutritional characteristics are especially interesting when donkey milk is aimed at consumption by children and the elderly. The aim of this study was to monitor the nutritional quality of Amiata donkey milk during lactation and productive season to provide information on the milk characteristics and to study action plans to improve milk yield and quality. Thirty-one pluriparous jennies belonging to the same farm were selected. Individual samples of milk from the morning milking were taken once per month starting from the d 30 of lactation until d 300. Milk yield and dry matter, fat, and ash content were constant throughout the experimental period. Milk total protein content showed a progressive decrease during the first 6 mo of lactation; after this period, the protein percentages remained constant (1.50%). Caseins and lactose were lower until d 60 of lactation and remained constant thereafter. During summer and autumn, milk yield and casein and lactose contents were higher, whereas during the spring season, higher protein and ash contents were found. The percentages of fat and dry matter were stable as were most of the minerals in the milk, except for calcium, which was higher in the spring. In conclusion, Amiata donkey milk was found to be relatively stable during lactation. This is an advantage in terms of the production and trade of a food product with consistent characteristics. The different milk yield and quality during the productive seasons were probably related to better adaptability of the animals to warm and temperate periods