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Background/Objectives The paediatric reference range of fecal calprotectin (FC) has not been decisively established and previous studies show a wide within-age variability, suggesting that other factors like anthropometric data or type of feeding can influence FC. Our aims were to establish the normal levels of FC in healthy children grouped by age and analyze whether sex, gestational age, birth weight, type of delivery, type of feeding, or anthropometric data influence FC values. Methods This multicentre, cross-sectional, and observational study enrolled healthy donors under 18 years of age who attended their Primary Health Care Centre for their routine Healthy Child Program visits. The exclusion criteria were: (i) immunodeficiency, (ii) autoimmune or (iii) gastrointestinal disease; (iv) medication usage; (v) gastrointestinal symptoms; or (vi) positive finding in the microbiological study. Results We enrolled 395 subjects, mean age was 4.2 years (range 3 days to 16.9 years), and 204 were male. The median FC was 77.0 mcg/g (interquartile range 246). A negative correlation between age and FC was observed (Spearman's rho = −0.603, P<0.01), and none of the other factors analyzed were found to influence FC levels. Conclusions Normal FC values in healthy children (particularly in infants) are higher than those considered to be altered in adults and show a negative correlation with age. It is necessary to reconsider the upper limits of FC levels for paediatric patients according to age, with further studies required to determine other factors that influence FC during infancy. [ABSTRACT FROM AUTHOR]

Sparse matrix matrix (SpMM) multiplication is involved in a wide range of scientific and technical applications. The computational requirements for this kind of operation are enormous, especially for large matrices. This paper analyzes and evaluates a method to efficiently compute the SpMM product in a computing environment that includes graphics processing units (GPUs). Some libraries to compute this matricial operation can be found in the literature. However, our strategy (FastSpMM) outperforms the existing approaches because it combines the use of the ELLPACK-R storage format with the exploitation of the high ratio computation/memory access of the SpMM operation and the overlapping of CPU–GPU communications/computations by Compute Unified Device Architecture streaming computation. In this work, FastSpMM is described and its performance evaluated with regard to the CUSPARSE library (supplied by NVIDIA), which also includes routines to compute SpMM on GPUs. Experimental evaluations based on a representative set of test matrices show that, in terms of performance, FastSpMM outperforms the CUSPARSE routine as well as the implementation of the SpMM as a set of sparse matrix vector products. [ABSTRACT FROM AUTHOR]