Detection and molecular identification of human adenoviruses and enteroviruses in wastewater from Morocco.

AIMS: Reclaimed wastewater is a considerable water resource in Morocco. Its agricultural reuse requires an assessment of viral contamination. The aim of this study was to detect both infectious and noninfectious human adenoviruses (HAdV) and enteroviruses (EV) in raw wastewater and treat effluent from wastewater treatment plants (WWTPs) and domestic sewage in Morocco. METHODS AND RESULTS: A total of 22 samples were analysed. A polyethylene glycol (PEG) precipitation method was used, followed by integrated cell culture-PCR (ICC-PCR) using two cell lines: human rhabdomyosarcoma tumour tissue and laryngeal carcinoma cells (RD and Hep2 cells). Furthermore, viral genome amplification was confirmed by sequencing. HAdV were detected in 10 (45·5%) of the 22 samples involving two species: HAdV-B and HAdV-D. EV was detected in 5 (23%) samples belonging to Coxsackievirus B5 and poliovirus vaccine strain (Sabin 2). CONCLUSIONS: Human adenoviruses and EV were detected in the analysed samples from two WWTPs and HAdV in domestic sewage. SIGNIFICANCE AND IMPACT OF THE STUDY: This work is the first study in Morocco using cell culture, PCR and sequencing of enteric viruses from wastewater. The presence of infectious HAdV and EV in treated effluent emphasizes the need of wastewater treatment surveillance.

A novel method for planning liver resections using deformable Bézier surfaces and distance maps.

BACKGROUND AND OBJECTIVE: For more than a decade, computer-assisted surgical systems have been helping surgeons to plan liver resections. The most widespread strategies to plan liver resections are: drawing traces in individual 2D slices, and using a 3D deformable plane. In this work, we propose a novel method which requires low level of user interaction while keeping high flexibility to specify resections. METHODS: Our method is based on the use of Bézier surfaces, which can be deformed using a grid of control points, and distance maps as a base to compute and visualize resection margins (indicators of safety) in real-time. Projection of resections in 2D slices, as well as computation of resection volume statistics are also detailed. RESULTS: The method was evaluated and compared with state-of-the-art methods by a group of surgeons (n=5, 5-31 years of experience). Our results show that theproposed method presents planning times as low as state-of-the-art methods (174 s median time) with high reproducibility of results in terms of resected volume. In addition, our method not only leads to smooth virtual resections easier to perform surgically compared to other state-of-the-art methods, but also shows superior preservation of resection margins. CONCLUSIONS: Our method provides clinicians with a robust and easy-to-use method for planning liver resections with high reproducibility, smoothness of resection and preservation of resection margin. Our results indicate the ability of the method to represent any type of resection and being integrated in real clinical work-flows.

Surface reconstruction for planning and navigation of liver resections.

Computer-assisted systems for planning and navigation of liver resection procedures rely on the use of patient-specific 3D geometric models obtained from computed tomography. In this work, we propose the application of Poisson surface reconstruction (PSR) to obtain 3D models of the liver surface with applications to planning and navigation of liver surgery. In order to apply PSR, the introduction of an efficient transformation of the segmentation data, based on computation of gradient fields, is proposed. One of the advantages of PSR is that it requires only one control parameter, allowing the process to be fully automatic once the optimal value is estimated. Validation of our results is performed via comparison with 3D models obtained by state-of-art Marching Cubes incorporating Laplacian smoothing and decimation (MCSD). Our results show that PSR provides smooth liver models with better accuracy/complexity trade-off than those obtained by MCSD. After estimating the optimal parameter, automatic reconstruction of liver surfaces using PSR is achieved keeping similar processing time as MCSD. Models from this automatic approach show an average reduction of 79.59% of the polygons compared to the MCSD models presenting similar smoothness properties. Concerning visual quality, on one hand, and despite this reduction in polygons, clinicians perceive the quality of automatic PSR models to be the same as complex MCSD models. On the other hand, clinicians perceive a significant improvement on visual quality for automatic PSR models compared to optimal (obtained in terms of accuracy/complexity) MCSD models. The median reconstruction error using automatic PSR was as low as 1.03±0.23mm, which makes the method suitable for clinical applications. Automatic PSR is currently employed at Oslo University Hospital to obtain patient-specific liver models in selected patients undergoing laparoscopic liver resection.