Emergence and transmission pathways of rapidly evolving evolutionary branch C4a strains of human enterovirus 71 in the Central Plain of China.

BACKGROUND: Large-scale outbreaks of hand, foot, and mouth disease (HFMD) occurred repeatedly in the Central Plain of China (Shandong, Anhui, and Henan provinces) from 2007 until now. These epidemics have increased in size and severity each year and are a major public health concern in mainland China. PRINCIPAL FINDINGS: Phylogenetic analysis was performed and a Bayesian Markov chain Monte Carlo tree was constructed based on the complete VP1 sequences of HEV71 isolates. These analyses showed that the HFMD epidemic in the Central Plain of China was caused by at least 5 chains of HEV71 transmission and that the virus continued to circulate and evolve over the winter seasons between outbreaks. Between 1998 and 2010, there were 2 stages of HEV71 circulation in mainland China, with a shift from evolutionary branch C4b to C4a in 2003-2004. The evolution rate of C4a HEV71 was 4.99×10(-3) substitutions per site per year, faster than the mean of all HEV71 genotypes. The most recent common ancestor estimates for the Chinese clusters dated to October 1994 and November 1993 for the C4a and C4b evolutionary branches, respectively. Compared with all C4a HEV71 strains, a nucleotide substitution in all C4b HEV71 genome (A to C reversion at nt2503 in the VP1 coding region, which caused amino acid substitution of VP1-10: Gln to His) had reverted. CONCLUSIONS: The data suggest that C4a HEV71 strains introduced into the Central Plain of China are responsible for the recent outbreaks. The relationships among HEV71 isolates determined from the combined sequence and epidemiological data reveal the underlying seasonal dynamics of HEV71 circulation. At least 5 HEV71 lineages circulated in the Central Plain of China from 2007 to 2009, and the Shandong and Anhui lineages were found to have passed through a genetic bottleneck during the low-transmission winter season

Fast and low-GPU-memory abdomen CT organ segmentation: The FLARE challenge

International audienceAutomatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/

Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge

Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset.A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics.In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i.e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https://adam.isi.uu.nl/