Sudden cardiac death (SCD) is a major health challenge. The records of 769 autopsy cases of SCD examined at Tongji Medicolegal Expertise Center from January 2006 to December 2015 were retrospectively reviewed. The mean age of the cases was 46 years, excluding 27 victims in whom the exact age could not be confirmed. The highest incidence of SCD occurred among the 40- to 60-year-old group (45.0%). Male preponderance was observed in SCD cases (male: female ratio: 5.0:1), and this preponderance was even higher (8.0:1) in the 10- to 20-year-old and 60- to 70-year-old groups. Death predominantly occurred in hospitals (37.4%) and outdoors (32.5%). The incidence of SCD did not differ significantly between the seasons. Coronary atherosclerotic disease (CAD) was the main cause of SCD (67.9%), followed by unexplained SCD (6.1%), myocarditis (5.7%), cardiomyopathy (4.7%), rupture of aortic dissection (3.9%), and cardiac conduction system disease (3.9%). In terms of the CAD cases, the mean age was 52.0 years and coronary artery stenosis exceeding 75% accounted for 73.6% of cases. The left anterior descending branch was involved with atherosclerosis in 92.0% of cases. In conclusion, detailed autopsy and forensic pathology examination is key to diagnosing SCD. Making an early diagnosis and performing early intervention of CAD may reduce the mortality of SCD. Additionally, the use of molecular genetic tests plus forensic pathology diagnosis will help further determine the underlying cause of death in individuals with SCD.

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A method to determine postmortem interval (PMI) based on environmental temperature and the concentrations of vitreous humor (VH) molecules were explored. Rabbit carcasses were placed in a chamber at 5, 15, 25, or 35°C, and 80-100 μL of VH was collected with the double-eye alternating micro-sampling method every 12 h. A Roche DPPI biochemical analyzer was used to measure the concentrations of six substances in VH samples. The interpolation function model and mixed-effect model were employed for data fitting to establish equations for PMI estimation. The concentrations of K , P, Mg , creatinine (CRE), and urea nitrogen (UN) exhibited an upward trend with increasing PMI in all temperature groups, while the concentration of Ca showed a downward trend. Validation results using K and Mg ions revealed that the mixed-effect model provided a better estimation than the interpolation function model using the data from our experiment. However, both models were able to estimate PMI using temperature and VH molecule concentrations.

For learning graph representations, not all detailed structures within a graph are relevant to the given graph tasks. Task-relevant structures can be 𝑙𝑜𝑐𝑎𝑙𝑖𝑧𝑒𝑑 or 𝑠𝑝𝑎𝑟𝑠𝑒 which are only involved in subgraphs or characterized by the interactions of subgraphs (a hierarchical perspective). A graph neural network should be able to efficiently extract task-relevant structures and be invariant to irrelevant parts, which is challenging for general message passing GNNs. In this work, we propose to learn graph representations from a sequence of subgraphs of the original graph to better capture task-relevant substructures or hierarchical structures and skip 𝑛𝑜𝑖𝑠𝑦 parts. To this end, we design soft-mask GNN layer to extract desired subgraphs through the mask mechanism. The soft-mask is defined in a continuous space to maintain the differentiability and characterize the weights of different parts. Compared with existing subgraph or hierarchical representation learning methods and graph pooling operations, the soft-mask GNN layer is not limited by the fixed sample or drop ratio, and therefore is more flexible to extract subgraphs with arbitrary sizes. Extensive experiments on public graph benchmarks show that soft-mask mechanism brings performance improvements. And it also provides interpretability where visualizing the values of masks in each layer allows us to have an insight into the structures learned by the model. CCS CONCEPTS• Computing methodologies → Neural networks; Supervised learning by classification.

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