Comparative study of enteric viruses, coliphages and indicator bacteria for evaluating water quality in a tropical high-altitude system

<p>Abstract</p> <p>Background</p> <p>Bacteria used as indicators for pathogenic microorganisms in water are not considered adequate as enteric virus indicators. Surface water from a tropical high-altitude system located in Mexico City that receives rainwater, treated and non-treated wastewater used for irrigation, and groundwater used for drinking, was studied.</p> <p>Methods</p> <p>The presence of enterovirus, rotavirus, astrovirus, coliphage, coliform bacteria, and enterococci was determined during annual cycles in 2001 and 2002. Enteric viruses in concentrated water samples were detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Coliphages were detected using the double agar layer method. Bacteria analyses of the water samples were carried out by membrane filtration.</p> <p>Results</p> <p>The presence of viruses and bacteria in the water used for irrigation showed no relationship between current bacterial indicator detection and viral presence. Coliphages showed strong association with indicator bacteria and enterovirus, but weak association with other enteric viruses. Enterovirus and rotavirus showed significant seasonal differences in water used for irrigation, although this was not clear for astrovirus.</p> <p>Conclusion</p> <p>Coliphages proved to be adequate faecal pollution indicators for the irrigation water studied. Viral presence in this tropical high-altitude system showed a similar trend to data previously reported for temperate zones.</p

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