Establishment and partial characterisation of a new cell line derived from adult tissues of the tsetse fly Glossina morsitans morsitans.

BACKGROUND: Insect cell lines play a vital role in many aspects of research on disease vectors and agricultural pests. The tsetse fly Glossina morsitans morsitans is an important vector of salivarian trypanosomes in sub-Saharan Africa and, as such, is a major constraint on human health and agricultural development in the region. METHODS: Here, we report establishment and partial characterisation of a cell line, GMA/LULS61, derived from tissues of adult female G. m. morsitans. GMA/LULS61 cells, grown at 28 °C in L-15 (Leibovitz) medium supplemented with foetal bovine serum and tryptose phosphate broth, have been taken through 23 passages to date and can be split 1:1 at 2-week intervals. Karyotyping at passage 17 revealed a predominantly haploid chromosome complement. Species origin and absence of contaminating bacteria were confirmed by PCR amplification and sequencing of fragments of the COI gene and pan-bacterial 16S rRNA gene respectively. However, PCR screening of RNA extracted from GMA/LULS61 cells confirmed presence of the recently described Glossina morsitans morsitans iflavirus and Glossina morsitans morsitans negevirus, but absence of Glossina pallipides salivary gland hypertrophy virus. GMA/LULS61 cells supported infection and growth of 6/7 different insect-derived strains of the intracellular bacterial symbiont Wolbachia. CONCLUSIONS: The GMA/LULS61 cell line has potential for application in a variety of studies investigating the biology of G. m. morsitans and its associated pathogenic and symbiotic microorganisms.

Parasitism and host dispersal plasticity in an aquatic model system.

Dispersal is a central determinant of spatial dynamics in communities and ecosystems, and various ecological factors can shape the evolution of constitutive and plastic dispersal behaviours. One important driver of dispersal plasticity is the biotic environment. Parasites, for example, influence the internal condition of infected hosts and define external patch quality. Thus, state-dependent dispersal may be determined by infection status and context-dependent dispersal by the abundance of infected hosts in the population. A prerequisite for such dispersal plasticity to evolve is a genetic basis on which natural selection can act. Using interconnected microcosms, we investigated dispersal in experimental populations of the freshwater protist Paramecium caudatum in response to the bacterial parasite Holospora undulata. For a collection of 20 natural host strains, we found substantial variation in constitutive dispersal and to a lesser degree in dispersal plasticity. First, infection tended to increase or decrease dispersal relative to uninfected controls, depending on strain identity, indicative of state-dependent dispersal plasticity. Infection additionally decreased host swimming speed compared to the uninfected counterparts. Second, for certain strains, there was a weak negative association between dispersal and infection prevalence, such that uninfected hosts dispersed less when infection was more frequent in the population, indicating context-dependent dispersal plasticity. Future experiments may test whether the observed differences in dispersal plasticity are sufficiently strong to be picked up by natural selection. The evolution of dispersal plasticity as a strategy to mitigate parasite effects spatially may have important implications for epidemiological dynamics.

Deep learning approach for the segmentation of aneurysmal ascending aorta.

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans.

Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ±â€¯2.94%, 85.98 ±â€¯3.40%, 88.02 ±â€¯2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R2 = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.