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Stroke is one of the leading causes of death. It not only affects adult people but also many children. It is estimated that, every year, 15 million people suffer a stroke worldwide. Among them, 5 million people die, while 5 million people are left permanently disabled. In this sense, the research to find new treatments should be accompanied with new therapies to combat neuronal death and to avoid developing cognitive impairment and dementia. Phytocannabinoids are among the compounds that have been used by mankind for the longest period of history. Their beneficial effects such as pain regulation or neuroprotection are widely known and make them possible therapeutic agents with high potential. These compounds bind cannabinoid receptors CB1 and CB2. Unfortunately, the psychoactive side effect has displaced them in the vast majority of areas. Thus, progress in the research and development of new compounds that show efficiency as neuroprotectors without this psychoactive effect is essential. On the one hand, these compounds could selectively bind the CB2 receptor that does not show psychoactive effects and, in glia, has opened new avenues in this field of research, shedding new light on the use of cannabinoid receptors as therapeutic targets to combat neurodegenerative diseases such as Alzheimer's, Parkinson's disease, or stroke. On the other hand, a new possibility lies in the formation of heteromers containing cannabinoid receptors. Heteromers are new functional units that show new properties compared to the individual protomers. Thus, they represent a new possibility that may offer the beneficial effects of cannabinoids devoid of the unwanted psychoactive effect. Nowadays, the approval of a mixture of CBD (cannabidiol) and Δ9-THC (tetrahydrocannabinol) to treat the neuropathic pain and spasticity in multiple sclerosis or purified cannabidiol to combat pediatric epilepsy have opened new therapeutic possibilities in the field of cannabinoids and returned these compounds to the front line of research to treat pathologies as relevant as stroke. [ABSTRACT FROM AUTHOR]

GM1 is one of the major glycosphingolipids (GSLs) on the cell surface in the central nervous system (CNS). Its expression level, distribution pattern, and lipid composition are dependent upon cell and tissue type, developmental stage, and disease state, which suggests a potentially broad spectrum of functions of GM1 in various neurological and neuropathological processes. The major focus of this review is the roles that GM1 plays in the development and activities of brains, such as cell differentiation, neuritogenesis, neuroregeneration, signal transducing, memory, and cognition, as well as the molecular basis and mechanisms for these functions. Overall, GM1 is protective for the CNS. Additionally, this review has also examined the relationships between GM1 and neurological disorders, such as Alzheimer's disease, Parkinson's disease, GM1 gangliosidosis, Huntington's disease, epilepsy and seizure, amyotrophic lateral sclerosis, depression, alcohol dependence, etc., and the functional roles and therapeutic applications of GM1 in these disorders. Finally, current obstacles that hinder more in-depth investigations and understanding of GM1 and the future directions in this field are discussed. [ABSTRACT FROM AUTHOR]

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer's disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson's disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer's disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism-enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E #4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways. [ABSTRACT FROM AUTHOR]