Improvement of mammary gland health in 81 'sentinel herds' in England and Scotland between 2012 and 2021.

BACKGROUND: Achieving a reduction in mastitis in dairy cows is a common industry goal, but there is no recent peer-reviewed record of progress in the UK. METHODS: A convenience sample of 125 herds in England and Scotland was recruited based on the quality of records in 2016, willingness to participate and representative geographical distribution. Individual cow somatic cell counts and clinical mastitis data from 2012 to 2021 were summarised annually, and temporal changes were analysed. Eighty-one herds had sufficient data for comparison between 2012 and 2021, for one or more parameters. RESULTS: Over this period, the median incidence rate of clinical mastitis decreased from 40.0 to 21.0 cases per 100 cows per year (p < 0.001), with improvement in both lactation and dry period indicators. Lactation new infection rate calculated from individual cow somatic cell counts fell from 8.75% to 5.95% (p < 0.001), dry period new infection rate fell from 16.8% to 14.1% (p < 0.05) and proportion of cows over 200,000 cells/mL fell from 20.0% to 14.3% (p < 0.001). LIMITATIONS: Data were necessarily from herds with good records and do not provide absolute values for the industry. CONCLUSION: The findings reflect good progress over a 10-year period in a cohort of well-recorded herds and align with other national datasets.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Emerging Roles for Artificial Intelligence in Heart Failure Imaging.

Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.

A comprehensive study of skeletal muscle imaging in FHL1-related reducing body myopathy.

OBJECTIVE: FHL1-related reducing body myopathy is an ultra-rare, X-linked dominant myopathy. In this cross-sectional study, we characterize skeletal muscle ultrasound, muscle MRI, and cardiac MRI findings in FHL1-related reducing body myopathy patients. METHODS: Seventeen patients (11 male, mean age 35.4, range 12-76 years) from nine independent families with FHL1-related reducing body myopathy underwent clinical evaluation, muscle ultrasound (n = 11/17), and lower extremity muscle MRI (n = 14/17), including Dixon MRI (n = 6/17). Muscle ultrasound echogenicity was graded using a modified Heckmatt scale. T1 and STIR axial images of the lower extremity muscles were evaluated for pattern and distribution of abnormalities. Quantitative analysis of intramuscular fat fraction was performed using the Dixon MRI images. Cardiac studies included electrocardiogram (n = 15/17), echocardiogram (n = 17/17), and cardiac MRI (n = 6/17). Cardiac muscle function, T1 maps, T2-weighted black blood images, and late gadolinium enhancement patterns were analyzed. RESULTS: Muscle ultrasound showed a distinct pattern of increased echointensity in skeletal muscles with a nonuniform, multifocal, and "geographical" distribution, selectively involving the deeper fascicles of muscles such as biceps and tibialis anterior. Lower extremity muscle MRI showed relative sparing of gluteus maximus, rectus femoris, gracilis, and lateral gastrocnemius muscles and an asymmetric and multifocal, "geographical" pattern of T1 hyperintensity within affected muscles. Cardiac studies revealed mild and nonspecific abnormalities on electrocardiogram and echocardiogram with unremarkable cardiac MRI studies. INTERPRETATION: Skeletal muscle ultrasound and muscle MRI reflect the multifocal aggregate formation in muscle in FHL1-related reducing body myopathy and are practical and informative tools that can aid in diagnosis and monitoring of disease progression.

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.

AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Comparative performance of composite measures from two phase III clinical trials of ixekizumab in psoriatic arthritis.

BACKGROUND/OBJECTIVE: The aim of this study was to evaluate relative performance of composite measures in psoriatic arthritis and assess the impact of structural damage and functional disability on outcomes during ixekizumab treatment. METHODS: Data from SPIRIT-P1 and SPIRIT-P2 were analysed to evaluate the effect of ixekizumab on achievement of low disease activity (LDA) and remission with the minimal disease activity (MDA) and very low disease activity (VLDA) composite, Disease Activity index for Psoriatic Arthritis (DAPSA), Psoriatic Arthritis Disease Activity Score, GRAppa Composite ScorE and modified Composite Psoriatic Disease Activity Index (mCPDAI). Performance was compared by quantifying residual symptom burden and the impact of structural damage and functional disability. RESULTS: Significantly more ixekizumab-treated patients achieved treatment targets at week 24 versus placebo assessed with all composites. More patients achieved targets assessed by mCPDAI and DAPSA than other composites. Residual disease activity was similar between composites, but residual high patient-reported outcomes (PROs) and functional disability were more frequent when assessed with mCPDAI and DAPSA. Achievement of treatment targets was reduced by high baseline levels of structural damage and functional disability. CONCLUSION: Residual disease activity was similar in patients achieving treatment targets assessed with all composites, but residual high PROs and functional disability were more common when assessed with mCPDAI and DAPSA, most likely due to the absence/attenuated functional assessment in these composites. High baseline levels of structural damage and functional disability attenuated response rates with all composites, affecting MDA/VLDA most prominently; LDA may be the most appropriate target in these patients. TRIAL REGISTRATION NUMBER: NCT01695239.

Potential of ESBL-producing Escherichia coli selection in bovine feces after intramammary administration of first generation cephalosporins using in vitro experiments.

Selection and spread of Extended Spectrum Beta-Lactamase (ESBL) -producing Enterobacteriaceae within animal production systems and potential spillover to humans is a major concern. Intramammary treatment of dairy cows with first-generation cephalosporins is a common practice and potentially selects for ESBL-producing Enterobacteriaceae, although it is unknown whether this really occurs in the bovine fecal environment. We aimed to study the potential effects of intramammary application of cephapirin (CP) and cefalonium (CL) to select for ESBL-producing Escherichia coli in the intestinal content of treated dairy cows and in manure slurry, using in vitro competition experiments with ESBL and non-ESBL E. coli isolates. No selection of ESBL-producing E. coli was observed at or below concentrations of 0.8 µg/ml and 4.0 µg/ml in bovine feces for CP and CL, respectively, and at or below 8.0 µg/ml and 4.0 µg/ml, respectively, in manure slurry. We calculated that the maximum concentration of CP and CL after intramammary treatment with commercial products will not exceed 0.29 µg/ml in feces and 0.03 µg/ml in manure slurry. Therefore, the results of this study did not find evidence supporting the selection of ESBL-producing E. coli in bovine feces or in manure slurry after intramammary use of commercial CP or CL-containing products.

Cardiovascular Imaging in Pregnancy: Valvulopathy, Hypertrophic Cardiomyopathy, and Aortopathy.

Pregnancy is associated with profound hemodynamic changes that are particularly impactful in patients with underlying cardiovascular disease. Management of pregnant women with cardiovascular disease requires careful evaluation that considers the well-being of both the woman and the developing fetus. Clinical assessment begins before pregnancy and continues throughout gestation into the post-partum period and is supplemented by cardiac imaging. This review discusses the role of imaging, specifically echocardiography, cardiac MRI, and cardiac CT, in pregnant women with valvular diseases, hypertrophic cardiomyopathy, and aortic pathology.

Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis.

Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.

The relationship between physical activity, BMI, circadian rhythm, and sleep with cognition in bipolar disorder.

BACKGROUND: Cognitive deficits affect a significant proportion of patients with bipolar disorder (BD). Problems with sustained attention have been found independent of mood state and the causes are unclear. We aimed to investigate whether physical parameters such as activity levels, sleep, and body mass index (BMI) may be contributing factors. METHODS: Forty-six patients with BD and 42 controls completed a battery of neuropsychological tests and wore a triaxial accelerometer for 21 days which collected information on physical activity, sleep, and circadian rhythm. Ex-Gaussian analyses were used to characterise reaction time distributions. We used hierarchical regression analyses to examine whether physical activity, BMI, circadian rhythm, and sleep predicted variance in the performance of cognitive tasks. RESULTS: Neither physical activity, BMI, nor circadian rhythm predicted significant variance on any of the cognitive tasks. However, the presence of a sleep abnormality significantly predicted a higher intra-individual variability of the reaction time distributions on the Attention Network Task. CONCLUSIONS: This study suggests that there is an association between sleep abnormalities and cognition in BD, with little or no relationship with physical activity, BMI, and circadian rhythm.

Ixekizumab Demonstrates Consistent Efficacy Versus Adalimumab in Biologic Disease-Modifying Anti-rheumatic Drug-Naïve Psoriatic Arthritis Patients Regardless of Psoriasis Severity: 52-Week Post Hoc Results from SPIRIT-H2H.

INTRODUCTION: Ixekizumab, a selective interleukin-17A antagonist, was compared with adalimumab in the SPIRIT-H2H study (NCT03151551) in patients with psoriatic arthritis (PsA) and concomitant psoriasis. This post hoc analysis reports outcomes to week 52 in patients from SPIRIT-H2H, stratified by baseline psoriasis severity. METHODS: SPIRIT-H2H was a 52-week, multicenter, randomized, open-label, rater-blinded, parallel-group study of biologic disease-modifying antirheumatic drug (DMARD)-naïve patients (N = 566) with PsA and active psoriasis (≥ 3% body surface area involvement). Patients were randomized to ixekizumab or adalimumab (1:1) with stratification by baseline concomitant use of conventional synthetic DMARDs and psoriasis severity (with/without moderate-to-severe psoriasis). Patients received on-label dosing according to psoriasis severity. The primary endpoint was the proportion of patients simultaneously achieving ≥ 50% improvement in American College of Rheumatology criteria (ACR50) and 100% improvement in Psoriasis Area Severity Index (PASI100) at week 24. Secondary endpoints included musculoskeletal, disease activity (defined by composite indices), skin and nail, quality of life and safety outcomes. In this post hoc analysis, primary and secondary endpoints of SPIRIT-H2H were analyzed by baseline psoriasis severity. RESULTS: A greater proportion of patients achieved the combined endpoint of ACR50 + PASI100 and PASI100 with ixekizumab compared with adalimumab at weeks 24 and 52, regardless of baseline psoriasis severity. ACR response rates were similar for ixekizumab and adalimumab across both patient subgroups. For musculoskeletal outcomes, similar efficacy was seen for ixekizumab and adalimumab, but ixekizumab showed greater responses for skin outcomes regardless of psoriasis severity. The safety profiles of ixekizumab and adalimumab were consistent between subgroups. CONCLUSIONS: Regardless of baseline psoriasis severity, ixekizumab demonstrated greater efficacy than adalimumab with respect to simultaneous achievement of ACR50 + PASI100, and showed consistent and sustained efficacy across PsA-related domains. It also demonstrated higher response rates for skin outcomes. These subgroup analyses highlight the efficacy of ixekizumab in patients with PsA irrespective of the severity of concomitant psoriasis.

Bedding system influences lying behaviour in dairy cows.

BACKGROUND: The nature and depth of bedding material have an important influence on cow lying behaviour and comfort. Increasing use of recycled manure solids (RMS) as bedding led to an investigation of the influence of this material on cow lying behaviour. METHODS: Leg mounted accelerometers were used to estimate daily lying time and number and duration of lying bouts in four groups of 40 cows. Each group spent two 2-week periods on each of four bedding systems: deep sand, deep RMS, sawdust on mattresses and RMS on mattresses. RESULTS: Total daily lying times were significantly shorter on both RMS treatments than on sawdust. Number of lying bouts per day was greater on sawdust than any other treatment, while lying bouts were 2.6 min longer on deep RMS and 9.3 min longer on sand, than on sawdust. CONCLUSIONS: Greater depth and apparent softness of bedding material does not necessarily result in longer total daily lying times. RMS may have some characteristics that reduce its attraction as a bedding material for cows. The influence of bedding system on number and duration of lying bouts and the resulting total lying time appear complex.

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis.

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.

Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen's kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen's kappa to 92.2% and 84.1% respectively. A computational framework integrating protein-protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.

Cardiothoracic imaging findings of Proteus syndrome.

In this work, we sought to delineate the prevalence of cardiothoracic imaging findings of Proteus syndrome in a large cohort at our institution. Of 53 individuals with a confirmed diagnosis of Proteus syndrome at our institution from 10/2001 to 10/2019, 38 individuals (men, n = 23; average age = 24 years) underwent cardiothoracic imaging (routine chest CT, CT pulmonary angiography and/or cardiac MRI). All studies were retrospectively and independently reviewed by two fellowship-trained cardiothoracic readers. Disagreements were resolved by consensus. Differences between variables were analyzed via parametric and nonparametric tests based on the normality of the distribution. The cardiothoracic findings of Proteus syndrome were diverse, but several were much more common and included: scoliosis from bony overgrowth (94%), pulmonary venous dilation (62%), band-like areas of lung scarring (56%), and hyperlucent lung parenchyma (50%). In addition, of 20 individuals who underwent cardiac MRI, 9/20 (45%) had intramyocardial fat, mostly involving the endocardial surface of the left ventricular septal wall. There was no statistically significant difference among the functional cardiac parameters between individuals with and without intramyocardial fat. Only one individual with intramyocardial fat had mildly decreased function (LVEF = 53%), while all others had normal ejection fraction.

Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating "dry" period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a "positive" diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a "positive" diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use.

Discrimination of contagious and environmental strains of Streptococcus uberis in dairy herds by means of mass spectrometry and machine-learning.

Streptococcus uberis is one of the most common pathogens of clinical mastitis in the dairy industry. Knowledge of pathogen transmission route is essential for the selection of the most suitable intervention. Here we show that spectral profiles acquired from clinical isolates using matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) can be used to implement diagnostic classifiers based on machine learning for the successful discrimination of environmental and contagious S. uberis strains. Classifiers dedicated to individual farms achieved up to 97.81% accuracy at cross-validation when using a genetic algorithm, with Cohen's kappa coefficient of 0.94. This indicates the potential of the proposed methodology to successfully support screening at the herd level. A global classifier developed on merged data from 19 farms achieved 95.88% accuracy at cross-validation (kappa 0.93) and 70.67% accuracy at external validation (kappa 0.34), using data from another 10 farms left as holdout. This indicates that more work is needed to develop a screening solution successful at the population level. Significant MALDI-TOF spectral peaks were extracted from the trained classifiers. The peaks were found to correspond to bacteriocin and ribosomal proteins, suggesting that immunity, growth and competition over nutrients may be correlated to the different transmission routes.