Anomalías dentarias de número Supernumerarios

2.3.1 Samples

A total of 250 serum samples from Holstein-Friesian calves (n = 208, eight of which were prior to colostrum ingestion) and adult dairy cows (n = 42) were used in the study. Samples had been collected for companion studies (Fecteau et al., 1997a; Fecteau et al., 1997b) and were stored frozen at -80 °C at the University of Prince Edward Island

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before use. All samples were tested in parallel using a radial immunodiffusion assay (RID) and by FTIR spectroscopy.

2.3.2 Radial immunodiffusion assay

Serum samples were thawed at room temperature (20–24 °C) and vortexed for 10 s. Subsequently, IgG was measured using the Bovine IgG RID Kit (Triple J Farms; working range of 196 to 2.748 mg/dL), except for eight samples that were collected prior to colostrum ingestion, which were measured using the Bovine Ultra Low Level IgG RID Kit (Triple J Farms; working range of 10 to 100 mg/dL). The RID assays were performed according to manufacturer’s recommendations, using 5 µL of undiluted serum in each well, with the manufacturer’s standard samples used on all RID assays.

The diameters of precipitated rings were measured after 18–24 h of incubation at room temperature, using a handheld caliper. Each of the serum samples and assay standards was tested in replicates of five and mean values calculated. The assay standards were used to construct a calibration curve that was used to determine IgG concentrations for the test samples. Serum samples with IgG concentrations outside the manufacturer stated performance range for the assay (above) were excluded from further analysis (n = 50; 28 calves; 22 cows).

2.3.3 Fourier-transform infrared (FTIR) spectroscopy

Thawed serum samples were diluted (1:1) with deionized sterile water and vortexed at a maximum of 2700 rpm for 10 s to homogenize the samples. After dilution, 10 µL aliquots were spread evenly onto 5 mm diameter wells within an adhesive-masked,

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96-well silicon microplate (Riley et al., 2007). For each sample, six replicate spectra were recorded to assess precision. The loaded microplates were allowed to dry at room temperature (20–24 °C) for 2 hours to evaporate water, resulting in thin dried films. For collection of FTIR spectra, the microplates were inserted into a multisampler (HTS-XT Autosampler, Bruker Optics) interfaced with a Bruker FTIR spectrometer (Tensor 37, Bruker Optics) equipped with a deuterium tryglycine sulfate detector and controlled by proprietary software (OPUS version 6.5, Bruker Optics).

Absorbance spectra were collected at wavenumbers between 4000–400 cm-1 _with

a nominal resolution of 4 cm-1 _{with 512 scans collected for data acquisition. An empty}

well served as the background reference for each microplate. A total of 1.500 (250 × 6) spectra were collected and converted into a printable format (PRN) (GRAMS/AI version 7.02, Thermo Fisher Scientific). The PRN format spectral data were imported into MATLAB version R2012b (MathWorks) and further data analysis was performed using scripts written by the authors.

2.3.4 Spectra pre-processing

The infrared spectra were pre-processed using the Savitzky-Golay method (2nd order polynomial function with nine points) to generate first order derivative spectra (Savitzky and Golay, 1964). The derivative spectra then were normalized using a vector normalization procedure, in which the square root of the sum of squared intensities of each spectrum over the wavenumber range of 1600–1800 cm-1 _{were calculated, and then}

the intensity at each wavenumber across the full measured spectral range was divided by this factor. Spectra were then cut and truncated to include only the 3700–2600 cm-1 _and

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1800–1300 cm-1 _{wavenumber regions, which exhibited the strongest absorptions in the}

original spectra. Subsequently, spectrum outlier detection was performed using Dixons Q-test (Dean and Dixon, 1951; Rorabacher, 1991) at each wavenumber. Spectra were designated as outliers if more than 50% of absorbance values were outside the 95% confidence level; these outliers were excluded from further analysis. The average spectrum of each sample (after removal of outlier spectra if applicable) was used for subsequent analysis.

2.3.5 Calibration model building

The sample IgG concentrations obtained by the RID method that were within the reference range (n = 200) were linked to their corresponding pre-processed infrared spectra and the samples divided into two sets. The test set (n = 67) were identified by ordering all of the spectra according to their corresponding RID IgG levels (minimum to maximum) and selecting every third spectrum. Thus, the test set encompassed the full range of IgG values as determined by RID. The remaining ‘combined’ set (n = 133) was used for model development, once randomly split into a training (n = 67) and a validation data set (n = 66).

PLS regression was applied to the training set to develop trial calibration models with up to 30 PLS factors, and the analytical accuracy of each trial model was estimated as the sum of squares of the errors for the validation set. This procedure was repeated 10,000 times (including new randomly assigned splits of the combined data set into new training and validation sets). The root mean squared error for the Monte Carlo cross-

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validation value (RMMCCV) (Richard R. Picard and R. Dennis Cook, 1984; Xu and Liang, 2001) was calculated by the following equation:

2 1 v 1 RMMCCV N i i i Nn  



where N denotes the number of repeated procedures (N = 10,000), 𝑛v is the

number of samples in the validation set (𝑛_v = 66), and y_i / y_i represent the IgG

concentrations for the validation set samples as obtained from RID experiments and predicted from the IR spectroscopic data, respectively. The notation  denotes the operator of Euclidean norm. The optimal number of PLS factors was chosen as the one giving the lowest RMMCCV value. Once the number of PLS factors had been determined, the training set and validation set were combined to build the calibration model.

2.3.6 Performance of the FTIR assay

The test set was used to evaluate the predictive performance of the optimized model. Scatter plot, Pearson correlation coefficient, and concordance correlation coefficient (Lin, 1989) were used. The three criteria were also applied to assess the combined set to examine model fitting. A Bland-Altman plot was used to examine the difference between RID and FTIR values for the test set and thereby assess the interchangeability of the two assays (Altman and Bland, 1983; Martin Bland and Altman, 1986; Bland and Altman, 1995). A paired Student’s t-test was performed to evaluate

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differences between the RID and FTIR methods, with a significant difference recognized at P < 0.05. The model’s potential utility was further examined using the ratio of predictive deviation (RPD: ratio of the standard deviation of the IgG concentration measured by RID to RMSEP) and the range error ratio (RER: ratio of the range of the IgG concentration measured by RID to RMSEP).

The precision of the new analytical method was also assessed using the test set data. The relative standard deviation (coefficient of variation) was calculated for the IgG concentrations predicted from replicate spectra for each sample, and used as an indicator of precision. For comparison, the relative standard deviation of each sample tested by RID assay was also calculated. These calculations were performed with Stata version 13.0 statistical software (StataCorp).

To evaluate the clinical applicability of FTIR spectroscopy for diagnosis of FTPI, the sensitivity (Se), specificity (Sp) and accuracy were evaluated at a RID cut-off of 1,000 mg/dL (Godden, 2008; Fecteau et al., 2013). The FTPI prevalence (P) for test and entire data were calculated using the same criteria.