19 research outputs found
L’E-learning et les nouvelles technologies de l'image dans les travaux pratiques d‘Histologie en Médecine vétérinaire : Impacts sur la motivation et la maîtrise de l'apprentissage.
Additional file 8: Table S2. Free prostate specific antigen (fPSA), total PSA (tPSA), free to total PSA (f/tPSA) and prostate cancer antigen3 (PCA3) median and IQR values for the four classifications utilized in the PCa study
Additional file 1: of Low renal replacement therapy incidence among slowly progressing elderly chronic kidney disease patients referred to nephrology care: an observational study
STROBE_RRT incidence. Documentation of adherence to STROBE statement. (DOC 83 kb
Additional file 1 of Ideal cardiovascular health and risk of death in a large Swedish cohort
Supplementary Material
MOESM7 of MALDI-TOF peptidomic analysis of serum and post-prostatic massage urine specimens to identify prostate cancer biomarkers
Additional file 7: Figure S3. Scatterplots of the within-subject replicates vs mean values and a QQ plot of the differences of between-subjects replicates, Serum
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Additional file 11: Raw data 2. Raw data for estimating signal sLOD
MOESM5 of MALDI-TOF peptidomic analysis of serum and post-prostatic massage urine specimens to identify prostate cancer biomarkers
Additional file 5: Results. Monte Carlo simulations results confirmed that substituting the limit of detection (LOD) with LOD/2 does not affect the reliability of ICC estimation; The measurement error structure of peptidomi MALDI-TOF/MS-based analysis of the urinary and serum feature
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Additional file 2: Intermediate data results. Intermediate results generated step-by-step, following the manuscript details
MOESM3 of MALDI-TOF peptidomic analysis of serum and post-prostatic massage urine specimens to identify prostate cancer biomarkers
Additional file 3: Table S1. Monte Carlo simulation results. The ICC estimates were obtained by increasing the measurement error (σε) from 0.01 to 0.64 and considering three different limit of detection (LOD) conditions (12.5%, 25% and 50% of values set below LOD) using four different adjustment methods (Richardson and Ciampi’s method, Schisterman’s method, substitution of W < LOD by zeros and substitution of W < LOD by LOD/2). The mean ICCs and Monte Carlo standard errors are shown
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Additional file 14: MS-Tag search results.  MS-MS spectra, peptide lists and MS-Tag search results (including all the configuration parameter) for the fragmentation patters of the 12 MALDI-TOF/MS serum features
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Additional file 9: Table S3. A comparison of MALDI-TOF/MS serum and urinary features. Mean and standard error are reported in arbitrary units. Blank spaces are missing features