Pterostilbene is a natural 3,5-dimethoxy analog of resveratrol. This stilbene compound has a strong bioactivity and exists widely in Dalbergia and Vaccinium spp. Besides natural extraction, pterostilbene can be obtained by biosynthesis. Pterostilbene has become popular because of its remarkable pharmacological activities, such as anti-tumor, anti-oxidation, anti-inflammation, and neuroprotection. Pterostilbene can be rapidly absorbed and is widely distributed in tissues, but it does not seriously accumulate in the body. Pterostilbene can easily pass through the blood-brain barrier because of its low molecular weight and good liposolubility. In this review, the studies performed in the last three years on resources, synthesis, bioactivity, and pharmacokinetics of pterostilbene are summarized. This review focuses on the effects of pterostilbene on certain diseases to explore its targets, explain the possible mechanism, and look for potential therapeutic applications.

In this narrative review article, we discuss the role of sleep deprivation (SD) in memory processing in rodent models. Numerous studies have examined the effects of SD on memory, with the majority showing that sleep disorders negatively affect memory. Currently, a consensus has not been established on which damage mechanism is the most appropriate. This critical issue in the neuroscience of sleep remains largely unknown. This review article aims to elucidate the mechanisms that underlie the damaging effects of SD on memory. It also proposes a scientific solution that might explain some findings. We have chosen to summarize literature that is both representative and comprehensive, as well as innovative in its approach. We examined the effects of SD on memory, including synaptic plasticity, neuritis, oxidative stress, and neurotransmitters. Results provide valuable insights into the mechanisms by which SD impairs memory function.

Pterostilbene, an important analogue of the star molecule resveratrol and a novel compound naturally occurring in blueberries and grapes, exerts a significant neuroprotective effect on cerebral ischemia/reperfusion (I/R), but its mechanism is still unclear. This study aimed to follow the molecular mechanisms behind the potential protective effect of pterostilbene against I/R induced injury. For fulfilment of our aim, we investigated the protective effects of pterostilbene on I/R injury caused by middle cerebral artery occlusion (MCAO) in vivo and oxygen-glucose deprivation (OGD) in vitro. Machine learning models and molecular docking were used for target exploration and validated by western blotting. Pterostilbene significantly reduced the cerebral infarction volume, improved neurological deficits, increased cerebral microcirculation and improved blood–brain barrier (BBB) leakage. Machine learning models confirmed that the stroke target MMP-9 bound to pterostilbene, and molecular docking demonstrated the strong binding activity. We further found that pterostilbene could depolymerize stress fibers and maintain the cytoskeleton by effectively increasing the expression of the non-phosphorylated actin depolymerizing factor (ADF) in the early stage of I/R. In the late stage of I/R, pterostilbene could activate the Wnt pathway and inhibit the expression of MMP-9 to decrease the degradation of the extracellular basement membrane (BM) and increase the expression of junction proteins. In this study, we explored the protective mechanisms of pterostilbene in terms of both endothelial cytoskeleton and extracellular matrix. The early and late protective effects jointly maintain BBB stability and attenuate I/R injury, showing its potential to be a promising drug candidate for the treatment of ischemic stroke.

Pterostilbene is a promising molecule with superior pharmacological activities and pharmacokinetic characteristics compared to its structural analogue resveratrol, which could be used to treat ischemic stroke. However, its mechanism is still unclear. The cutting-edge air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and spatial metabolomics analysis were applied to investigate the distribution of pterostilbene in ischemic rat brain and the changes of related small molecule metabolic pathways to further explore the potential mechanisms of pterostilbene against cerebral ischemia-reperfusion injury. This research found that pterostilbene could significantly restore cerebral microcirculation blood flow, reduce infarct volume, improve neurological function and ameliorate neuronal damage in ischemic rats. Moreover, pterostilbene was widely and abundantly distributed in ischemic brain tissue, laying a solid foundation for the rescue of ischemic penumbra. Further study revealed that pterostilbene played a therapeutic role in restoring energy supply, rebalancing neurotransmitters, reducing abnormal polyamine accumulation and phospholipid metabolism. These findings offer an opportunity to illustrate novel mechanisms of pterostilbene in the treatment of cerebral ischemia/reperfusion injury resulting from ischemic stroke.

Sleep disorders are the most widespread mental disorders after stroke and hurt survivors’ functional prognosis, response to restoration, and quality of life. This review will address an overview of the progress of research on the biological mechanisms associated with stroke-complicating sleep disorders. Extensive research has investigated the negative impact of stroke on sleep. However, a bidirectional association between sleep disorders and stroke exists; while stroke elevates the risk of sleep disorders, these disorders also independently contribute as a risk factor for stroke. This review aims to elucidate the mechanisms of stroke-induced sleep disorders. Possible influences were examined, including functional changes in brain regions, cerebrovascular hemodynamics, neurological deficits, sleep ion regulation, neurotransmitters, and inflammation. The results provide valuable insights into the mechanisms of stroke complicating sleep disorders.

Sleep staging by highly trained specialists is laborious. Although automatic sleep staging with supervised learning methods has been implemented for almost a decade, it requires lots of manually annotated data. Self-supervised learning methods have recently been gaining attention. They can learn representations with unlabeled data, which alleviates the cost of labeling work. However, the problem is that these self-supervised sleep staging methods either require prior knowledge, as with frequency information, or they produce unsatisfactory results. Thus, we propose a deep sequential sleep network (DSSNet), a self-supervised framework that aims to perform multi-view representations based on contrastive learning. It utilizes a single-channel electroencephalogram but achieves competitive performance. We also explore the impact of different contrastive mechanisms on DSSNet performance. The results of the Sleep-EDF dataset prove that the consistency of negative samples is crucial for improving performance. We evaluate DSSNet on Sleep-EDF and ISRUC-Sleep and achieve accuracies of 80.0% and 71.4%.