Genetic Reconstruction of Protozoan rRNA Decoding Sites Provides a Rationale for Paromomycin Activity against Leishmania and Trypanosoma

Aminoglycoside antibiotics target the ribosomal decoding A-site and are active against a broad spectrum of bacteria. These compounds bind to a highly conserved stem-loop-stem structure in helix 44 of bacterial 16S rRNA. One particular aminoglycoside, paromomycin, also shows potent antiprotozoal activity and is used for the treatment of parasitic infections, e.g. by Leishmania spp. The precise drug target is, however, unclear; in particular whether aminoglycoside antibiotics target the cytosolic and/or the mitochondrial protozoan ribosome. To establish an experimental model for the study of protozoan decoding-site function, we constructed bacterial chimeric ribosomes where the central part of bacterial 16S rRNA helix 44 has been replaced by the corresponding Leishmania and Trypanosoma rRNA sequences. Relating the results from in-vitro ribosomal assays to that of in-vivo aminoglycoside activity against Trypanosoma brucei, as assessed in cell cultures and in a mouse model of infection, we conclude that aminoglycosides affect cytosolic translation while the mitochondrial ribosome of trypanosomes is not a target for aminoglycoside antibiotics

Malaria vaccines and their potential role in the elimination of malaria

Research on malaria vaccines is currently directed primarily towards the development of vaccines that prevent clinical malaria. Malaria elimination, now being considered seriously in some epidemiological situations, requires a different vaccine strategy, since success will depend on killing all parasites in the community in order to stop transmission completely

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