Propidium monoazide (PMA) and ethidium bromide monoazide(EMA) improve DNA array and high-throughput sequencing ofporcine reproductive and respiratory syndrome virus identification

Pan-viral DNA array (PVDA) and high-throughput sequencing (HTS) are useful tools to identify novel viruses of emerging diseases. However, both techniques have difficulties to identify viruses in clinical samples because of the host genomic nucleic acid content (hg/cont). Both propidium monoazide (PMA) and ethidium bromide monoazide (EMA) have the capacity to bind free DNA/RNA, but are cell membrane-impermeable. Thus, both are unable to bind protected nucleic acid such as viral genomes within intact virions. However, EMA/PMA modified genetic material cannot be amplified by enzymes. In order to assess the potential of EMA/PMA to lower the presence of amplifiable hg/cont in samples and improve virus detection, serum and lung tissue homogenates were spiked with porcine reproductive and respiratory virus (PRRSV) and were processed with EMA/PMA. In addition, PRRSV RT-qPCR positive clinical samples were also tested. EMA/PMA treatments significantly decreased amplifiable hg/cont and significantly increased the number of PVDA positive probes and their signal intensity compared to untreated spiked lung samples. EMA/PMA treatments also increased the sensitivity of HTS by increasing the number of specific PRRSV reads and the PRRSV percentage of coverage. Interestingly, EMA/PMA treatments significantly increased the sensitivity of PVDA and HTS in two out of three clinical tissue samples. Thus, EMA/PMA treatments offer a new approach to lower the amplifiable hg/cont in clinical samples and increase the success of PVDA and HTS to identify viruses.

Patient-specific model of a scoliotic torso for surgical planning

A method for the construction of a patient-specific model of a scoliotic torso for surgical planning via inter- patient registration is presented. Magnetic Resonance Images (MRI) of a generic model are registered to surface topography (TP) and X-ray data of a test patient. A partial model is first obtained via thin-plate spline registration between TP and X-ray data of the test patient. The MRIs from the generic model are then fit into the test patient using articulated model registration between the vertebrae of the generic model’s MRIs in prone position and the test patient’s X-rays in standing position. A non-rigid deformation of the soft tissues is performed using a modified thin-plate spline constrained to maintain bone rigidity and to fit in the space between the vertebrae and the surface of the torso. Results show average Dice values of 0.975 ± 0.012 between the MRIs following inter-patient registration and the surface topography of the test patient, which is comparable to the average value of 0.976 ± 0.009 previously obtained following intra-patient registration. The results also show a significant improvement compared to rigid inter-patient registration. Future work includes validating the method on a larger cohort of patients and incorporating soft tissue stiffness constraints. The method developed can be used to obtain a geometric model of a patient including bone structures, soft tissues and the surface of the torso which can be incorporated in a surgical simulator in order to better predict the outcome of scoliosis surgery, even if MRI data cannot be acquired for the patient.

Multimodal image registration of the scoliotic torso for surgical planning

Background This paper presents a method that registers MRIs acquired in prone position, with surface topography (TP) and X-ray reconstructions acquired in standing position, in order to obtain a 3D representation of a human torso incorporating the external surface, bone structures, and soft tissues. Methods TP and X-ray data are registered using landmarks. Bone structures are used to register each MRI slice using an articulated model, and the soft tissue is confined to the volume delimited by the trunk and bone surfaces using a constrained thin-plate spline. Results The method is tested on 3 pre-surgical patients with scoliosis and shows a significant improvement, qualitatively and using the Dice similarity coefficient, in fitting the MRI into the standing patient model when compared to rigid and articulated model registration. The determinant of the Jacobian of the registration deformation shows higher variations in the deformation in areas closer to the surface of the torso. Conclusions The novel, resulting 3D full torso model can provide a more complete representation of patient geometry to be incorporated in surgical simulators under development that aim at predicting the effect of scoliosis surgery on the external appearance of the patient’s torso.