19 research outputs found
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
Promysalin elicits species-selective inhibition of pseudomonas aeruginosa by targeting succinate sehydrogenase
Natural products have served as an inspiration to scientists both for their complex three-dimensional architecture and exquisite biological activity. Promysalin is one such Pseudomonad secondary metabolite that exhibits narrow-spectrum antibacterial activity, originally isolated from the rhizosphere. We herein utilize affinity-based protein profiling (AfBPP) to identify succinate dehydrogenase (Sdh) as the biological target of the natural product. The target was further validated in silico, in vitro, in vivo, and through the selection, and sequencing, of a resistant mutant. Succinate dehydrogenase plays an essential role in primary metabolism of Pseudomonas aeruginosa as the only enzyme that is involved both in the tricarboxylic acid cycle (TCA) and in respiration via the electron transport chain. These findings add credence to other studies that suggest that the TCA cycle is an understudied target in the development of novel therapeutics to combat P. aeruginosa, a significant pathogen in clinical settings