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

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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

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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

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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 4: Figure S1. The results of ICC estimation obtained by (a) varying the measurement error amount (x-axis of each graph); (b) by considering different strategies for handling limit of detection (LOD) issues; c) by considering three different LOD scenarios (12.5 %, 25% and 50% of values below LOD). The different strategies for handling LOD issues evaluated were: (1) sub W < LOD by E(W|W < LOD) = Richardson and Ciampi’s method; (2) sub W < LOD by E(W|W > LOD) = Schisterman’s method; (3) sub W < LOD by Zero and 4) sub W < LOD by LOD/2 (see Supplementary materials and methods for further details)

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Additional file 1: Materials and methods. Urine and serum samples preparation before MALDI-TOF/MS analyses; within and between subject variability of serum MALDI-TOF/MS peptidomic features and variability of MALDI-TOF/MS serum peptidomic features; Within- and between-subjects variability of urinary MALDI-TOF/MS peptidomic analysis; Spectra processing; sLOD estimation of MALDI-TOF/MS peptidomic features; Simulation analyses to examine the reliability of ICC for datasets with measurement error and LOD issues; LOD adjustment, data normalization and log2 transformation of MALDI-TOF/MS features; RCAL and SIMEX for logistic regression analyses

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Additional file 13: Table S4. The significant MS-MS fragmentation patterns of the serum features analyzed using MALDI-TOF/MS set at CID conditions