AT1 Receptors: Their Actions from Hypertension to Cognitive Impairment.

Hypertension is one of the most prevalent cardiovascular disorders worldwide, affecting 1.13 billion people, or 14% of the global population. Hypertension is the single biggest risk factor for cerebrovascular dysfunction. According to the American Heart Association, high blood pressure (BP), especially in middle-aged individuals (~ 40 to 60 years old), is associated with an increased risk of dementia, later in life. Alzheimer's disease and cerebrovascular disease are the two leading causes of dementia, accounting for around 80% of the total cases and usually combining mixed pathologies from both. Little is known regarding how hypertension affects cognitive function, so the impact of its treatment on cognitive impairment has been difficult to assess. The brain renin-angiotensin system (RAS) is essential for BP regulation and overactivity of this system has been established to precede the development and maintenance of hypertension. Angiotensin II (Ang-II), the main peptide within this system, induces vasoconstriction and impairs neuro-vascular coupling by acting on brain Ang-II type 1 receptors (AT1R). In this review, we systemically analyzed the association between RAS and biological mechanisms of cognitive impairment, from the perspective of AT1R located in the central nervous system. Additionally, the possible contribution of brain AT1R to global cognition decline in COVID-19 cases will be discussed as well.

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier.

MOTIVATION: Multi-label protein subcellular localization (SCL) is an indispensable way to study protein function. It can locate a certain protein (such as the human transmembrane protein that promotes the invasion of the SARS-CoV-2) or expression product at a specific location in a cell, which can provide a reference for clinical treatment of diseases such as COVID-19. RESULTS: The paper proposes a novel method named ML-locMLFE. First of all, six feature extraction methods are adopted to obtain protein effective information. These methods include pseudo amino acid composition (PseAAC), encoding based on grouped weight (EBGW), gene ontology (GO), multi-scale continuous and discontinuous (MCD), residue probing transformation (RPT) and evolutionary distance transformation (EDT). In the next part, we utilize the multi-label information latent semantic index (MLSI) method to avoid the interference of redundant information. In the end, multi-label learning with feature induced labeling information enrichment (MLFE) is adopted to predict the multi-label protein SCL. The Gram-positive bacteria dataset is chosen as a training set, while the Gram-negative bacteria dataset, virus dataset, newPlant dataset and SARS-CoV-2 dataset as the test sets. The overall actual accuracy (OAA) of the first four datasets is 99.23%, 93.82%, 93.24%, and 96.72% by the leave-one-out cross validation (LOOCV). It is worth mentioning that the OAA prediction result of our predictor on the SARS-CoV-2 dataset is 72.73%. The results indicate that the ML-locMLFE method has obvious advantages in predicting the SCL of multi-label protein, which provides new ideas for further research on the SCL of multi-label protein. AVAILABILITY AND IMPLEMENTATION: The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/ML-locMLFE/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.