Proposed biochemistry of Parkinson's and Alzheimer's diseases

Possible Signaling Pathways in the Gut Microbiota-Brain Axis for the Development of Parkinson's Disease Caused by Chronic Consumption of Food Additives

It is well-known that consumption of synthetic and natural food additives has both positive and negative effects in the human body. However, it is not clear yet how food additives are related to the development of Parkinson's disease. Therefore, in this review work, the food additive effects related to the gut microbiota-brain axis and the processes that are carried out to develop Parkinson's disease are studied. To this end, a systematic literature analysis is performed with the selected keywords and the food additive effects are studied to draw possible routes of action. This analysis leads to the proposition of a model that explains the pathways that relate the ingestion of food additives to the development of Parkinson's disease. This work motivates further research that ponders the safety of food additives by measuring their impacts over the gut microbiota-brain axis.

T1-weighted MRI-driven Brain Age Estimation in Alzheimer's Disease and Parkinson's Disease

Neuroimaging-driven brain age estimation has introduced a robust (reliable and heritable) biomarker for detecting and monitoring neurodegenerative diseases. Here, we computed and compared brain age in Alzheimer's disease (AD) and Parkinson's disease (PD) patients using an advanced machine learning procedure involving T1-weighted MRI scans and gray matter (GM) and white matter (WM) models. Brain age estimation frameworks were built using 839 healthy individuals and then the brain estimated age difference (Brain-EAD: chronological age subtracted from brain estimated age) was assessed in a large sample of PD patients (n = 160) and AD patients (n = 129), respectively. The mean Brain-EADs for GM were +9.29 ± 6.43 years for AD patients versus +1.50 ± 6.03 years for PD patients. For WM, the mean Brain-EADs were +8.85 ± 6.62 years for AD patients versus +2.47 ± 5.85 years for PD patients. In addition, PD patients showed a significantly higher WM Brain-EAD than GM Brain-EAD. In a direct comparison between PD and AD patients, we observed significantly higher Brain-EAD values in AD patients for both GM and WM. A comparison of the Brain-EAD between PD and AD patients revealed that AD patients may have a significantly "older-appearing" brain than PD patients.

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.

Investigation of plasma metabolomics and neurotransmitter dysfunction in the process of Alzheimer's disease rat induced by amyloid beta 25-35

Alzheimer's disease (AD) has become one of the major diseases endangering the health of the elderly. Clarifying the features of each AD animal model is valuable for understanding the onset and progression of diseases and developing potential treatments in the pharmaceutical industry. In this study, we aimed to clarify plasma metabolomics and neurotransmitter dysfunction in the process of AD model rat induced by amyloid beta 25-35 (Aβ 25-35). Firstly, Morris Water Maze (MWM) test was used to investigate cognitive impairment in AD rat after 2, 4 and 8 weeks of modelling. Based on this, the effects on levels of AD-related enzymes and eight neurotransmitters were analyzed. And plasma metabolomics analysis based on ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was used to research the metabolic disturbances in the process of AD rat. The results shown the injury on the spatial learning ability of AD rats was gradually aggravated within 4 weeks, reached the maximum at 4 weeks and then was stable until 8 weeks. During 8 weeks of modeling, the levels of enzymes including β-secretase, γ-secretase, glycogen synthase kinase-3β (GSK-3β), acetyl cholinesterase (AchE) and nitric oxide synthase (NOS) were significant increased in the plasma of AD rats. The neurotransmitter dysfunction was mainly involved in γ-aminobutyric acid (GABA), acetyl choline (Ach), glutamic acid (Glu), 5-hydroxytryptamine (5-HT), dopamine (DA) and norepinephrine (NE). 17 endogenous metabolites correlated with AD were successfully detected in the metabolomics analysis. These metabolites were mainly involved in fatty acids, sphingolipids, and sterols metabolisms, vitamin metabolism, and amino acid metabolism. These metabolites might be the potential biomarkers that correctly mark different stages of AD. The study on peripheral plasma indices reflecting the process of AD laid the foundation for understand the pathophysiology of AD and find an effective and radical cure. And the rules of endogenous metabolic disorder in AD rats also have a certain guiding significance for the future study of food–drug interactions at different stages of AD.

The cognitive impairment, Alzheimer's disease (AD) related enzymes, neurotransmitters and endogenous metabolites shown a dynamic change in AD model rat induced by amyloid beta 25-35.

Synthesis and Evaluation of Novel Ligustrazine Derivatives as Multi-Targeted Inhibitors for the Treatment of Alzheimer's Disease

A series of novel ligustrazine derivatives 8a–r were designed, synthesized, and evaluated as multi-targeted inhibitors for anti-Alzheimer’s disease (AD) drug discovery. The results showed that most of them exhibited a potent ability to inhibit both ChEs, with a high selectivity towards AChE. In particular, compounds 8q and 8r had the greatest inhibitory abilities for AChE, with IC₅₀ values of 1.39 and 0.25 nM, respectively, and the highest selectivity towards AChE (for 8q, IC₅₀ BuChE/IC₅₀ AChE = 2.91 × 10⁶; for 8r, IC₅₀ BuChE/IC₅₀ AChE = 1.32 × 10⁷). Of note, 8q and 8r also presented potent inhibitory activities against Aβ aggregation, with IC₅₀ values of 17.36 µM and 49.14 µM, respectively. Further cellular experiments demonstrated that the potent compounds 8q and 8r had no obvious cytotoxicity in either HepG2 cells or SH-SY5Y cells, even at a high concentration of 500 μM. Besides, a combined Lineweaver-Burk plot and molecular docking study revealed that these compounds might act as mixed-type inhibitors to exhibit such effects via selectively targeting both the catalytic active site (CAS) and the peripheral anionic site (PAS) of AChEs. Taken together, these results suggested that further development of these compounds should be of great interest.