72 Evaluation of digital images of bovine embryos

Bovine embryo transfer is an assisted reproductive technology that enables cattle producers to reach their reproductive, genetic, and financial goals. Proper evaluation of embryo quality and developmental stage is key to the success of the embryo transfer industry and is often acquired “on the job” as there are few embryo evaluation training opportunities. The goals of this project were to develop a bank of digital images of invivo-produced bovine embryos, and to evaluate interrater reliability and variation of grade and stage scores and the assessment of the integrity of the zona pellucida (ZP) using an online platform. Frozen/thawed bovine embryos (Day 7) were placed in a commercially available holding medium, and images were captured using a high-resolution camera (112× magnification). Survey participants (n=89) responded to questions on demographic and embryo evaluation experience and assigned grades and stages and evaluated the integrity of the ZP in 58 embryo images (the IETS embryo evaluation rubric was provided). The variation among raters within and between demographic categories was analysed using the “kappaetc” package with weights for ordinal variables (grade and stage) in Stata version 14.2 (StataCorp). The Kruskal–Wallis test was used to assess whether variation [measured as interquartile range (IQR)] in grade and stage ratings within individual were significantly different (P=0.05) by demographic factors. Overall, interrater reliability (measured by Cohen’s Kappa, κ) for stage was moderate (κ=0.57), whereas identification of grade and the ZP was substantial (κ=0.69 and 0.73, respectively). Interrater reliability for grade was significantly lower among those with ≤2 years (n=17) of experience (κ=0.44) compared with those having 3–9 years (n=29; κ=0.57) and ≥10 years (n=43; κ=0.64). Similarly, interrater agreement for stage was significantly lower for those with ≤2 years of experience (κ=0.58) compared with those having 3–9 years (κ=0.68) and ≥10 years (κ=0.75). Interrater agreement for stage was higher for those who did not use the IETS rubric compared with those who did (κ=0.73 vs. κ=0.63, respectively), and the variability was highest for Stage 6 and lowest for Stage 4 embryos. No demographic variables were significantly associated with interrater reliability for the integrity of the ZP. The variability of stage ratings (IQR) within individual significantly increased with more years of experience, with greatest IQR among those with ≥10 years of experience. The IQR within individual for grade decreased with increasing length of time rating, with the lowest IQR for the longest duration spent on rating the embryos. Overall, there was a moderate to substantial interrater agreement in embryo evaluation, which increased with experience. Further, evaluation of bovine embryo digital images may be a useful training tool instead of or in conjunction with a “hands-on” experience. Future surveys will involve multiple images of the same embryo and video to enhance the evaluation process and increase interrater reliability.

Changes in bovine leukemia virus serological status and lymphocyte count between dry-off and early lactation in Michigan dairy cows

This study addresses how the serological status of bovine leukemia virus (BLV) and lymphocyte count fluctuate from dry-off to early lactation in dairy cattle. Very few studies have investigated how BLV antibody status and lymphocyte count of cows changes longitudinally during the lactation cycle. Blood samples were collected from dairy cattle (n = 149) on 5 commercial dairy herds in Michigan at dry-off, close-up, and 7 to 10 d after calving. Plasma was analyzed for anti-BLV antibodies using a BLV-ELISA and whole blood was analyzed for lymphocyte counts. We found that BLV seroprevalence increased from dry-off (38.9%) to close-up (43.6%), then slightly decreased from close-up to 7 to 10 d after calving (43.0%). However, the change in seroprevalence was only significant from dry-off to close-up. Cows of third or higher parity were more likely to seroconvert than cows of lower parity and had the highest ELISA-negative prevalence of BLV. Lymphocyte counts were significantly higher in ELISA-positive animals, but only among second and third or greater parity animals. These results indicate that the use of lymphocyte counts as a disease severity monitoring tool for BLV should differ by parity group. Future studies should investigate if changes in seroprevalence are due to new infections or natural changes in antibody concentrations as the cow prepares for colostrum production. More accurate lymphocyte guidelines to be used for monitoring the progression of BLV should be created that consider parity and lactation stage.

Serum vitamin D concentrations at dry-off and close-up predict increased postpartum urine ketone concentrations in dairy cattle

Vitamin D is commonly supplemented to dairy cows as vitamin D₃ to support calcium homeostasis and in times of low sunlight exposure. Vitamin D has beneficial immunomodulatory and anti-inflammatory properties. Serum 25-hydroxyvitamin D [25(OH)D] concentrations fluctuated during lactation, with the lowest concentrations measured in healthy cows within 7 d of calving. However, it is unknown if serum 25(OH)D concentrations measured during the previous lactation are associated with transition diseases or health risk factors in dairy cattle. We collected serum samples from 279 dairy cattle from 5 commercial dairy herds in Michigan at dry-off, close-up, and 2-10 d in milk (DIM). Vitamin D concentrations were determined by measuring serum 25(OH)D by radioimmunoassay. Total serum calcium was measured by colorimetric methods. Body condition scores (BCS) were assigned at the time of blood collection. Clinical disease incidence was monitored until 30 d postparturition. Separate bivariable logistic regression analyses were used to determine if serum 25(OH)D at dry-off, close-up, and 2-10 DIM was associated with various clinical diseases including mastitis, lameness, and uterine disorders (classified as metritis, retained placenta, or both) and increased urine ketone concentrations at P < 0.05. Among all significant bivariable analyses, multivariable logistic regression analyses were built to adjust for potential confounding variables including parity, BCS, season, and calcium. Receiver operator characteristic (ROC) curve analyses were used to determine optimal concentrations of serum 25(OH)D. We found that higher serum 25(OH)D concentrations at dry-off and close-up predicted increased urine ketone concentrations in early lactation, even after adjustment for confounders. Alternatively, we found that lower serum 25(OH)D at 2-10 DIM was associated with uterine diseases. Optimal concentrations for serum 25(OH)D at dry-off and close-up for lower risk of increased urine ketone concentrations were below 103.4 and 91.1 ng/mL, respectively. The optimal concentration for serum 25(OH)D at 2-10 DIM for uterine diseases was above 71.4 ng/mL. These results indicate that serum 25(OH)D at dry-off and close-up may be a novel predictive biomarker for increased urine ketone concentrations during early lactation. Increased urine ketone concentrations are not necessarily harmful or diagnostic for ketosis but do indicate development of negative energy balance, metabolic stress, and increased risk of early lactation diseases. Predicting that dairy cattle are at increased risk of disease facilitates implementation of intervention strategies that may lower disease incidence. Future studies should confirm these findings and determine the utility of serum 25(OH)D concentrations as a predictive biomarker for clinical and subclinical ketosis.

Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle

Dairy cattle experience metabolic stress during the transition from late gestation to early lactation resulting in higher risk for several economically important diseases (e.g. mastitis, metritis, and ketosis). Metabolic stress is described as a physiological state composed of 3 processes: nutrient metabolism, oxidative stress, and inflammation. Current strategies for monitoring transition cow nutrient metabolism include assessment of plasma non-esterified fatty acids and beta-hydroxybutyrate concentrations around the time of calving. Although this method is effective at identifying cows with higher disease risk, there is often not enough time to implement intervention strategies to prevent health disorders from occurring around the time of calving. Previously, we published predictive models for early lactation diseases at the individual cow level at dry-off. However, it is unknown if predictive probabilities from individual-level models can be aggregated to the cohort level to predict cohort-level incidence. Therefore, our objective was to test different data aggregation methods using previously published models that represented the 3 components of metabolic stress (nutrient metabolism, oxidative stress, and inflammation). We included 277 cows from five Michigan dairy herds for this prospective cohort study. On each farm, two to four calving cohorts were formed, totaling 18 cohorts. We measured biomarker data at dry-off and followed the cows until 30 days post-parturition for cohort disease incidence, which was defined as the number of cows: 1) having one or more clinical transition disease outcome, and/or 2) having an adverse health event (abortion or death of calf or cow) within each cohort. We tested three different aggregation methods that we refer to as the p-central, p-dispersion, and p-count methods. For the p-central method, we calculated the averaged predicted probability within each cohort. For the p-dispersion method, we calculated the standard deviation of the predicted probabilities within a cohort. For the p-count method, we counted the number of cows above a specified threshold of predicted probability within each cohort. We built four sets of models: one for each aggregation method and one that included all three aggregation methods (p-combined method). We found that the p-dispersion method was the only method that produced viable predictive models. However, these models tended to overestimate incidence in cohorts with low observed counts and underestimate risk in cohorts with high observed counts.

Predictive models for early lactation diseases in transition dairy cattle at dry-off

During the transition period, dairy cattle undergo tremendous metabolic and physiological changes to prepare for milk synthesis and secretion. Failure to sufficiently regulate these changes may lead to metabolic stress, which increases risk of transition diseases. Metabolic stress is defined as a physiological state consisting of 3 components: aberrant nutrient metabolism, oxidative stress, and inflammation. Current monitoring methods to detect cows experiencing metabolic stress involve measuring biomarkers for nutrient metabolism. However, these biomarkers, including non-esterified fatty acids, beta-hydroxybutyrate, and calcium are typically measured a few weeks before to a few days after calving. This is a retroactive approach, because there is little time to integrate interventions that remediate metabolic stress in the current cohort. Our objective was to determine if biomarkers of metabolic stress measured at dry-off are predictive of transition disease risk. We designed a prospective cohort study carried out on 5 Michigan dairy farms (N = 277 cows). We followed cows from dry-off to 30 days post-calving. Diseases and adverse outcomes were grouped in an aggregate outcome that included mastitis, metritis, retained placenta, ketosis, lameness, pneumonia, milk fever, displaced abomasum, abortion, and death of the calf or the cow. We used best subsets selection to select candidate models for four different sets of models: one set for each component of metabolic stress (nutrient metabolism, oxidative stress, and inflammation), and a combined model that included all 3 components. We used model averaging to obtain averaged predicted probabilities across each model set. We hypothesized that the averaged predictions from the combined model set with all 3 components of metabolic stress would be more effective at predicting disease than each individual component model set. The area under the curve estimated using receiver operator characteristic curves for the combined model set (0.93; 95% confidence interval [CI] = 0.90-0.96) was significantly higher compared with averaged predictions from the inflammation (0.87; 95% CI = 0.83-0.91), oxidative stress (0.78; 95% CI = 0.72-0.84), and nutrient metabolism (0.73; 95% CI = 0.67-0.79) model sets (p < 0.05). Our results indicate that it may be possible to detect cattle at risk for some transition diseases as early as dry-off. This has important implications for disease prevention, as earlier identification of cows at risk of health disorders will allow for earlier implementation of intervention strategies. A limitation of the current study is that we did not perform external validation. Future validation studies are needed to confirm our findings.