Use of a hidden Markov model for interpretation of serial cow milk paratuberculosis antibody enzyme-linked immunosorbent assay results adjusted for milk yield and quality.

Paratuberculosis (Johne's disease), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a common, economically-important and potentially zoonotic contagious disease of cattle, with worldwide distribution. Disease management relies on identification of animals which are at high-risk of being infected or infectious.

Development and evaluation of predictive models for pregnancy risk in UK dairy cows.

One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated, and each herd was randomly allocated, at a ratio of 80:20, to either training or testing datasets.

Using Object-Oriented Simulation to Assess the Impact of the Frequency and Accuracy of Mobility Scoring on the Estimation of Epidemiological Parameters for Lameness in Dairy Herds.

Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios.

Quantification of the effect of in utero events on lifetime resilience in dairy cows.

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data.

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.

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.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC).

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.

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.

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.

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.

Quantifiable brain atrophy synthesis for benchmarking of cortical thickness estimation methods.

Cortical thickness (CTh) is routinely used to quantify grey matter atrophy as it is a significant biomarker in studying neurodegenerative and neurological conditions. Clinical studies commonly employ one of several available CTh estimation software tools to estimate CTh from brain MRI scans. In recent years, machine learning-based methods emerged as a faster alternative to the main-stream CTh estimation methods (e.

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.

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.

Dosimetry audit for megavoltage photon beams applied in non-reference conditions.

Purpose: We have conducted for the first time a Malaysian postal dosimetry audit of external beam under non-reference conditions by evaluating the output performance while screening for systematic errors within the dosimetry chain. The potential use from the choice of detector were investigated along with the search for other sources of discrepancies.

Methods: Ten radiotherapy centres were audited, encompassing 16 megavoltage photon beam arrangements, adopting the IAEA postal dosimetry protocol for non-reference conditions, with a holder modified to accommodate three TLD types: Ge-doped cylindrical silica fibres (CF), Ge-doped flat silica fibres (FF), and TLD-100 powder.

Identifying associations between management practices and antimicrobial resistances of sentinel bacteria recovered from bulk tank milk on dairy farms.

There is increasing emphasis on the need to reduce antimicrobial use (AMU) on dairy farms to reduce the emergence of resistant bacteria which could compromise animal health and impact human medicine. In addition to AMU, the role of farm management is an area of growing interest and represents an alternative route for possible interventions. The aim of this study was to evaluate the impact of farm management practices and AMU on resistances of sentinel bacteria in bulk milk.

Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study.

Background: Proximal femoral fractures are an important clinical and public health issue associated with substantial morbidity and early mortality. Artificial intelligence might offer improved diagnostic accuracy for these fractures, but typical approaches to testing of artificial intelligence models can underestimate the risks of artificial intelligence-based diagnostic systems.

Methods: We present a preclinical evaluation of a deep learning model intended to detect proximal femoral fractures in frontal x-ray films in emergency department patients, trained on films from the Royal Adelaide Hospital (Adelaide, SA, Australia).

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.

ROAD: The Road Event Awareness Dataset for Autonomous Driving.

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind.

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.

Deep learning in cancer diagnosis, prognosis and treatment selection.

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning.

Towards data-driven quantification of skin ageing using reflectance confocal microscopy.

Introduction: Evaluation of skin ageing is a non-standardized, subjective process, with typical measures relying coarse, qualitatively defined features. Reflectance confocal microscopy depth stacks contain indicators of both chrono-ageing and photo-ageing. We hypothesize that an ageing scale could be constructed using machine learning and image analysis, creating a data-driven quantification of skin ageing without human assessment.

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.