Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.

SummaryUsing both quantitative and qualitative methods, this study contrasted employees' job stress perceptions and their relationships to strains in China and the United States. Significant job stressor-strain correlations were found in both countries. However, hierarchical regression analyses revealed significant interactions of country by job stressors in predicting job strains, indicating the unique patterns of stressor-strain relationships in China and the United States. In the qualitative analyses, American employees reported significantly more incidents of lack of job control, direct interpersonal conflict, lack of team coordination, anger, frustration, feeling overwhelmed, and stomach problems than the Chinese. Chinese employees reported significantly more incidents of job evaluations, work mistakes, indirect conflict, employment conditions, lack of training, anxiety, helplessness, sleep problems, and feeling hot than the Americans. The qualitative approach contributed above and beyond the quantitative results in that it revealed culture-specific job stressors of job evaluations, work mistakes, and indirect conflict that had been overlooked in western-based stress research.

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Background: Post-stroke cognitive impairment (PSCI) occurs in approximately half of ischemic stroke survivors. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations is lacking. In this large-scale multicenter lesion-symptom mapping study, we aimed to identify infarct locations most strongly predictive of PSCI, and use this information to develop a prediction model. Methods:We harmonized individual patient data from twelve cohorts through the Meta-VCI-Map consortium. Patients with acute symptomatic infarcts on CT/MRI and cognitive assessment <1 year poststroke were eligible. PSCI was defined as impairment in ≥1 cognitive domains on neuropsychological assessment or impairment on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios for PSCI. For the prediction model, a "location impact score" on a five-point scale was derived from the VLSM results. Combined internal-external validation was performed using leave-one-cohort-out cross-validation for all twelve cohorts. Findings:In our combined sample of 2950 patients (age 67±12 years, 39% female), 44% had PSCI. We achieved almost complete lesion coverage of the brain in our analyses (87%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (False Discovery Rate corrected q<0•01; voxel-wise odds ratios >20). These strategic regions were mapped onto a three-dimensional brain template to visualize PSCI risk per brain region. The location impact score showed good correspondence between predicted and observed risk across cohorts after adjusting for cohortspecific PSCI occurrence. Interpretation:This study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts and can be applied by clinicians to identify individual patients at risk of PSCI.

Lesion location is an important determinant for post-stroke cognitive impairment. Although several 'strategic' brain regions have previously been identified, a comprehensive map of strategic brain regions for post-stroke cognitive impairment is lacking due to limitations in sample size and methodology. We aimed to determine strategic brain regions for post-stroke cognitive impairment by applying multivariate lesion-symptom mapping in a large cohort of 410 acute ischemic stroke patients. Montreal Cognitive Assessment at three to six months after stroke was used to assess global cognitive functioning and cognitive domains (memory, language, attention, executive and visuospatial function). The relation between infarct location and cognition was assessed in multivariate analyses at the voxel-level and the level of regions of interest using support vector regression. These two assumption-free analyses consistently identified the left angular gyrus, left basal ganglia structures and the white matter around the left basal ganglia as strategic structures for global cognitive impairment after stroke. A strategic network involving several overlapping and domain-specific cortical and subcortical structures was identified for each of the cognitive domains. Future studies should aim to develop even more comprehensive infarct location-based models for post-stroke cognitive impairment through multicenter studies including thousands of patients.

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of-the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.

AbstractMethyltransferase-like 3 (METTL3) was originally known to be responsible for N6-methyladenosine (m6A) modification of mRNA. Recent studies have found that METTL3 plays important roles in a variety of tumors by regulating the translation of oncogenes. However, the functional and regulating mechanisms of METTL3 in human gastric cancer have not yet been understood. Here we knocked down METTL3 in human gastric cancer cell lines, AGS and MKN45, by using shRNA transfection. RT-qPCR assay and western blotting verified the effectiveness of RNA interference on mRNA and protein levels, respectively. Then we found that METTL3 knockdown inhibited cell proliferation, migration and invasion in AGS and MKN45 cells. Moreover, METTL3 knockdown decreased Bcl2 and increased Bax and active Caspase-3 in gastric cancer cells, which suggested the apoptotic pathway was activated. Mechanistic investigation suggested that METTL3 led to inactivation of the AKT signaling pathway in human gastric cancer cells, including decreased phosphorylation levels of AKT and expression of down-stream effectors p70S6K and Cyclin D1. In conclusion, our study reveals that down-regulation of METTL3 inhibits the proliferation and mobility of human gastric cancer cells and leads to inactivation of the AKT signaling pathway, suggesting that METTL3 may be a potential target for the treatment of human gastric cancer.

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