● B[a]P, nicotine and phenanthrene molecules altered the secondary structure of Aβ42. ● β-content of the peptide was significantly enhanced in the presence of the PAHs.● Nicotine made stable cluster with Aβ42 peptide via hydrogen bonds. ● Phenanthrene due to its small size, interfered with the Aβ42 monomer more strongly. Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons (PAHs) with the progression of neurodegenerative disorders, either by using statistical data from various cities, or via tracking biomarkers during in-vivo experiments. Among different neurodegenerative disorders, PAHs are known to cause increased risk for Alzheimer’s disease, related to the development of amyloid beta (Aβ) peptide oligomers. However, the complex molecular interactions between peptide monomers and organic pollutants remains obscured. In this work, we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ42 peptide monomer in the presence of benzo[a]pyrene, nicotine, and phenanthrene. Interestingly the results revealed strong hydrophobic, and hydrogen-bond based interactions between Aβ peptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters. The strong interactions affected the secondary structure of the Aβ42 peptide in the presence of the organic pollutants, with almost 50 % decrease in the α-helix and 2 %–10 % increase in the β-sheets of the peptide. Overall, the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβ peptide oligomers, and consequent progression of Alzheimer’s disease. 相似文献
Objective: The aim of this study was to explore whether varying levels of operational and tactical driving task demand differentially affect drivers with Parkinson's disease (PD) and control drivers in their sign recall.
Methods: Study participants aged between 50 and 70 years included a group of drivers with PD (n = 10) and a group of age- and sex-matched control drivers (n = 10). Their performance in a sign recall task was measured using a driving simulator.
Results: Drivers in the control group performed better than drivers with PD in a sign recall task, but this trend was not statistically significant (P =.43). In addition, regardless of group membership, subjects' performance differed according to varying levels of task demand. Performance in the sign recall task was more likely to drop with increasing task demand (P =.03). This difference was significant when the variation in task demand was associated with a cognitive task; that is, when drivers were required to apply the instructions from working memory.
Conclusions: Although the conclusions drawn from this study are tentative, the evidence presented here is encouraging with regard to the use of a driving simulator to examine isolated cognitive functions underlying driving performance in PD. With an understanding of its limitations, such driving simulation in combination with functional assessment batteries measuring physical, visual, and cognitive abilities could comprise one component of a multitiered system to evaluate medical fitness to drive. 相似文献