Emerging Links between Cerebral Blood Flow Regulation and Cognitive Decline: A Role for Brain Microvascular Pericytes

Cognitive impairment associated with vascular etiology has been of considerable interest in the development of dementia. Recent studies have started to uncover cerebral blood flow deficits in initiating cognitive deterioration. Brain microvascular pericytes, the only type of contractile cells in capillaries, are involved in the precise modulation of vascular hemodynamics due to their ability to regulate resistance in the capillaries. They exhibit potential in maintaining the capillary network geometry and basal vascular tone. In addition, pericytes can facilitate better blood flow supply in response to neurovascular coupling. Their dysfunction is thought to disturb cerebral blood flow causing metabolic imbalances or structural injuries, leading to consequent cognitive decline. In this review, we summarize the characteristics of microvascular pericytes in brain blood flow regulation and outline the framework of a two-hit hypothesis in cognitive decline, where we emphasize how pericytes serve as targets of cerebral blood flow dysregulation that occurs with neurological challenges, ranging from genetic factors, aging, and pathological proteins to ischemic stress.

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. In this study, we propose a multi-lead ensemble neural network (MENN) to distinguish anterior myocardial infarction (AMI) and inferior myocardial infarction (IMI) from healthy control (HC) respectively. In the study, three kinds of sub-networks and multi-lead ECG signals are combined, which fully explores the information of ECG signals and improves the classification performance. The algorithm is evaluated on the PTB database by 5-fold inter-subject cross-validation and the sensitivity (Se), specificity (Sp) and area under the curve (AUC) of AMI detection are 98.35%, 97.49%, 97.92%; The Se, Sp, and AUC of IMI detection are 93.17%, 92.02%, 92.60%. The proposed method achieves the state of the art results on both tasks and outperforms the baseline methods. Hence, the proposed method is potential for automatic MI diagnosis.