Bacteriological etiology and treatment of mastitis in Finnish dairy herds

Background: The Finnish dairy herd recording system maintains production and health records of cows and herds. Veterinarians and farmers register veterinary treatments in the system. Milk samples for microbiological analysis are routinely taken from mastitic cows. The laboratory of the largest dairy company in Finland, Valio Ltd., analyzes most samples using real-time PCR. This study addressed pathogen-specific microbiological data and treatment and culling records, in combination with cow and herd characteristics, from the Finnish dairy herd recording system during 2010-2012. Results: The data derived from 240,067 quarter milk samples from 93,529 dairy cows with mastitis; 238,235 cows from the same herds served as the control group. No target pathogen DNA was detected in 12% of the samples. In 49% of the positive samples, only one target species and in 19%, two species with one dominant species were present. The most common species in the samples with a single species only were coagulase-negative staphylococci (CNS) (43%), followed by Staphylococcus aureus (21%), Streptococcus uberis (9%), Streptococcus dysgalactiae (8%), Corynebacterium bovis (7%), and Escherichia coli (5%). On average, 36% of the study cows and 6% of the control cows had recorded mastitis treatments during lactation. The corresponding proportions were 16 and 6% at drying-off. For more than 75% of the treatments during lactation, diagnosis was acute clinical mastitis. In the milk samples from cows with a recorded mastitis treatment during lactation, CNS and S. aureus were most common, followed by streptococci. Altogether, 48% of the cows were culled during the study. Mastitis was reported as the most common reason to cull; 49% of study cows and 18% of control cows were culled because of mastitis. Culling was most likely if S. aureus was detected in the milk sample submitted during the culling year. Conclusions: The PCR test has proven to be an applicable method also for large-scale use in bacterial diagnostics. In the present study, microbiological diagnosis was unequivocal in the great majority of samples where a single species or two species with one dominating were detected. Coagulase-negative staphylococci and S. aureus were the most common species. S. aureus was also the most common pathogen among the culled cows, which emphasizes the importance of preventive measures.Peer reviewe

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging