A prospective cohort study of factors associated with the digital cushion thickness in dairy cattle.

Key factors such as stage of lactation, parity, and body fat reserves have been associated with the digital cushion thickness (DCT), however, there are discrepancies between the results of previously published studies. The objective of this study was to examine the association of stage of lactation, body fat reserves, parity, and lesion incidence with DCT in a large cohort of intensively monitored cows. Across 4 UK farms, 2,352 cows were prospectively enrolled and assessed at 4 time points: before calving (T1-Precalving), immediately after calving (T2-Calving), in early lactation (T3-Early), and in late lactation (T4-Late). At each time point, BCS was recorded, the presence of sole lesions (sole ulcers and sole hemorrhage) and white line lesions was assessed by veterinarians, and an ultrasound image was taken to retrospectively measure the backfat thickness (BFT) in the pelvic region and the digital cushion on the hind left lateral claw. Mixed effects multivariable linear regression models, with the cow as a random effect, were fit to examine the association between the explanatory variables and DCT. The explanatory variables tested were farm, parity, stage of lactation, BCS, BFT, height, the presence of a lesion at the time of measurement, the chronicity of a lesion during early lactation, the predicted maximum daily milk yield, and the rate of milk production rise in early lactation. Stage of lactation and farm were both associated with DCT; however, an interaction was present, and this DCT pattern of change was farm-dependent. Two distinct patterns emerged; one indicated the nadir to occur shortly after calving, the other indicated the nadir to occur during early lactation. Neither BFT nor BCS were significantly associated with DCT. Heifers displayed thinner digital cushions compared with multiparous cows; however, this effect was dependent on the stage of lactation, with heifers having a thinner digital cushion up until late lactation, by which time DCT was commensurate with multiparous animals. Sole lesions and white line lesions at the time of measurement were associated with DCT (sole lesion: estimate = -0.07 mm, 95% CI = -0.14-0.00; white line lesion: estimate = 0.28 mm, 95% CI = 0.15-0.42).

Setting the boundaries-an approach to estimate the Loss Gap in dairy cattle.

Dairy production in the UK has undergone substantial restructuring over the last few decades. Farming intensification has led to a reduction in the total numbers of farms and animals, while the average herd size per holding has increased. These ever-changing circumstances have important implications for the health and welfare of dairy cows, as well as the overall business performance of farms. For decision-making in dairy farming, it is essential to understand the underlying causes of the inefficiencies and their relative impact. The investigation of yield gaps regarding dairy cattle has been focused on specific causes. However, in addition to the risk of overestimating the impact of a specific ailment, this approach does not allow understanding of the relative contribution to the total, nor does it allow understanding of how well-described that gap is in terms of underlying causes. Using the English and Welsh dairy sectors as an example, this work estimates the Loss Gap-composed of yield losses and health expenditure - using a benchmarking approach and scenario analysis. The Loss Gap was estimated by comparing the current performance of dairy herds as a baseline with that of scenarios where assumptions were made about the milk production of cows, production costs, market prices, mortality, and expenditure related to health events. A deterministic model was developed, consisting of an enterprise budget, in which the cow was the unit, with milking herd and young stock treated separately. When constraining milk production, the model estimated an annual Loss Gap of £148 to £227 million for the whole sector. The reduction in costs of veterinary services and medicines, alongside herd replacement costs, were important contributors to the estimate with some variation between the scenarios. Milk price had a substantial impact in the estimate, with revenue from milk yield representing more than 30% of the Loss Gap, when milk price was benchmarked against that of the top performing farms. This framework provides the boundaries for understanding the relative burden from specific causes in English and Welsh dairy cattle, ensuring that the sum of the estimated losses due to particular problem does not exceed the losses from all-causes, health or non-health related.

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities.

Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.

Atrial Fibrillation After Patent Foramen Ovale Closure: Incidence, Pathophysiology, and Management.

This comprehensive review explores the incidence, pathophysiology, and management of atrial fibrillation (AF) following percutaneous closure of patent foramen ovale (PFO). Although AF is considered a common adverse event post PFO closure, its incidence, estimated at <5%, varies based on monitoring methods. The review delves into the challenging task of precisely estimating AF incidence, given subclinical AF and diverse diagnostic approaches. Notably, a temporal pattern emerges, with peak incidence around the 14th day after closure and a subsequent decline after the 45th day, mimicking general population AF trends. The pathophysiological mechanisms behind post PFO closure AF remain elusive, with proposed factors including local irritation, device-related interference, tissue stretch, and nickel hypersensitivity. Management considerations encompass rhythm control, with flecainide showing promise, and anticoagulation tailored to individual risk profiles. The authors advocate for a personalized approach, weighing factors like age, comorbidities, and device characteristics. Notably, postclosure AF is generally considered benign, often resolving spontaneously within 45 days, minimizing thromboembolic risks. Further studies are required to refine understanding and provide evidence-based guidelines.