Review Article Socio-economic determinants of micronutrient intake and status in Europe: a systematic review

Objective To provide the evidence base for targeted nutrition policies to reduce the risk of micronutrient/diet-related diseases among disadvantaged populations in Europe, by focusing on: folate, vitamin B-12, Fe, Zn and iodine for intake and status; and vitamin C, vitamin D, Ca, Se and Cu for intake. Design MEDLINE and Embase databases were searched to collect original studies that: (i) were published from 1990 to 2011; (ii) involved gt 100 subjects; (iii) had assessed dietary intake at the individual level; and/or (iv) included best practice biomarkers reflecting micronutrient status. We estimated relative differences in mean micronutrient intake and/or status between the lowest and highest socio-economic groups to: (i) evaluate variation in intake and status between socio-economic groups; and (ii) report on data availability. Setting Europe. Subjects Children, adults and elderly. Results Data from eighteen publications originating primarily from Western Europe showed that there is a positive association between indicators of socio-economic status and micronutrient intake and/or status. The largest differences were observed for intake of vitamin C in eleven out of twelve studies (5-47 %) and for vitamin D in total of four studies (4-31 %). Conclusions The positive association observed between micronutrient intake and socio-economic status should complement existing evidence on socio-economic inequalities in diet-related diseases among disadvantaged populations in Europe. These findings could provide clues for further research and have implications for public health policy aimed at improving the intake of micronutrients and diet-related diseases

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption

Two novel cases expanding the phenotype of SETD2-related overgrowth syndrome

The SETD2-related overgrowth syndrome is also called "Luscan-Lumish syndrome" (OMIM 616831) with the clinical characteristics of intellectual disability, speech delay, macrocephaly, facial dysmorphism, and autism spectrum disorders. We report on two novel patients a 4.5-year-old boy and a 23-year-old female adolescent with a speech and language developmental delay, autism spectrum disorder and macrocephaly, who were both diagnosed with SETD2-related overgrowth syndrome due to de novo frameshift mutations in the SETD2 gene. Features not previously described which were present in either one of our patients were nasal polyps, a large tongue with creases, a high pain threshold, constipation, and undescended testicles. These features may be related to the syndrome and may need special attention in future patients. Additionally, prevention of obesity should be an important point of attention for patients diagnosed with a SETD2-related overgrowth syndrome

Gene Mosaicism Screening Using Single-Molecule Molecular Inversion Probes in Routine Diagnostics for Systemic Autoinflammatory Diseases

Diagnosis of systemic autoinflammatory diseases (SAIDs) is often difficult to achieve and can delay the start of proper treatments and result in irreversible organ damage. In several patients with dominantly inherited SAID, postzygotic mutations have been detected as the disease-causing gene defects. Mutations with allele frequencies <5% have been detected, even in patients with severe phenotypes. Next-generation sequencing techniques are currently used to detect mutations in SAID-associated genes. However, even if the genomic region is highly covered, this approach is usually not able to distinguish low-grade postzygotic variants from background noise. We, therefore, developed a sensitive deep sequencing assay for mosaicism detection in SAID-associated genes using single-molecule molecular inversion probes. Our results show the accurate detection of postzygotic variants with allele frequencies as low as 1%. The probability of calling mutations with allele frequencies ≥3% exceeds 99.9%. To date, we have detected three patients with mosaicism, two carrying likely pathogenic NLRP3 variants and one carrying a likely pathogenic TNFRSF1A variant with an allele frequency of 1.3%, confirming the relevance of the technology. The assay shown herein is a flexible, robust, fast, cost-effective, and highly reliable method for mosaicism detection; therefore, it is well suited for routine diagnostics