A novel PGAM5 inhibitor LFHP-1c protects blood-brain barrier integrity in ischemic stroke

Blood-brain barrier (BBB) damage after ischemia significantly influences stroke outcome. Compound LFHP-1c was previously discovered with neuroprotective role in stroke model, but its mechanism of action on protection of BBB disruption after stroke remains unknown. Here, we show that LFHP-1c, as a direct PGAM5 inhibitor, prevented BBB disruption after transient middle cerebral artery occlusion (tMCAO) in rats. Mechanistically, LFHP-1c binding with endothelial PGAM5 not only inhibited the PGAM5 phosphatase activity, but also reduced the interaction of PGAM5 with NRF2, which facilitated nuclear translocation of NRF2 to prevent BBB disruption from ischemia. Furthermore, LFHP-1c administration by targeting PGAM5 shows a trend toward reduced infarct volume, brain edema and neurological deficits in nonhuman primate

A review of deep learning methods for the detection and classification of pulmonary nodules / 生物医学工程学杂志

Lung cancer has the highest mortality rate among all malignant tumors. The key to reducing lung cancer mortality is the accurate diagnosis of pulmonary nodules in early-stage lung cancer. Computer-aided diagnostic techniques are considered to have potential beyond human experts for accurate diagnosis of early pulmonary nodules. The detection and classification of pulmonary nodules based on deep learning technology can continuously improve the accuracy of diagnosis through self-learning, and is an important means to achieve computer-aided diagnosis. First, we systematically introduced the application of two dimension convolutional neural network (2D-CNN), three dimension convolutional neural network (3D-CNN) and faster regions convolutional neural network (Faster R-CNN) techniques in the detection of pulmonary nodules. Then we introduced the application of 2D-CNN, 3D-CNN, multi-stream multi-scale convolutional neural network (MMCNN), deep convolutional generative adversarial networks (DCGAN) and transfer learning technology in classification of pulmonary nodules. Finally, we conducted a comprehensive comparative analysis of different deep learning methods in the detection and classification of pulmonary nodules.