An Investigation of the Suitability of Viscosity Detection in Estimating IgG Content in Mare Colostrum.

An adequate supply of colostrum is important for the prevention of hypogammaglobulinaemia in foals. In addition to the quantity of colostrum consumed and the time of consumption, the quality of the colostrum, the immunoglobulin (Ig) G concentration, is crucial. The aim of this study was to determine whether the viscosity of equine colostrum was a suitable estimate of IgG concentration. IgG content of colostrum was measured by ELISA and viscosity directly measured with a cone plate viscometer and indirectly assessed with a funnel. Analysis of 56 colostrum samples obtained from 40 mares at different postpartum time points was conducted to assess colostrum samples with varying levels of quality. The range of IgG concentrations determined by ELISA was 0.83 to 245.5 mg/mL (30.69 ± 41.92 mg/mL). The range of viscosity values determined by the cone plate method was 1.84 to 110.00 cP (7.86 ± 17.48 cP) at a shear rate of 3 rpm. Colostrum drainage from the funnel (drainage time), varied between 7.9 and 30.0 s, with an average of 9.96 ± 4.48 s. As the data were not normally distributed, Spearman's rank correlation analyses were calculated and significant correlation found between viscosity and IgG content (ρ = 0.71, P < .001), as well as between drainage time and IgG content (ρ = 0.75, P < .001). These correlations indicate that determining the viscosity of equine colostrum by cone plate or drainage time, may be an effective proxy measurement of IgG content.

Approaches to standardising the magnetic resonance image analysis of equine tendon lesions.

Background: Low-field magnetic resonance imaging (MRI) has gained increasing importance to monitor equine tendon lesions. Comparing results between studies and cases is hampered, because image analysis approaches vary strongly. This study aimed to improve reliability, comparability and time efficiency of quantitative MRI image analysis. Methods: Induced tendon lesions were studied over a 24-week period with 10 follow-up MRI examinations. Signal intensities (SIs) of tendons, tendon lesions, cortical bone and background, as well as lesion cross-sectional areas (CSAs) were measured. Lesion SI standardisation with different formulas was evaluated, using histological findings as reference. Different types of region of interest (ROI) for lesion SI measurement were compared. Lesion CSA measurement at different levels was evaluated, using the calculated total lesion volume as reference. Subjective lesion identification and manual CSA and SI measurements were compared to an automated, algorithm-based approach. Results: Lesion SI standardised using a quotient of lesion and background or cortical bone SI, correlated best with histologically determined lesion severity. Lesion SI in circular ROIs correlated strongly with lesion SI in free-hand whole-lesion ROIs. The level of the maximum lesion CSA shifted over time; the CSA maximum correlated strongly with lesion volume. In sequences with short acquisition time, algorithm-based automated lesion detection showed almost perfect agreement with subjective lesion identification. Automated measurement of CSA and SI was also feasible, with stronger correlation and better agreement with the manually obtained data for the SI than for the CSA. Conclusion: Our study may provide guidance for MRI image analysis of tendon healing. Reliable image analysis can be performed time-efficiently, particularly regarding lesion SI quantification.