Low Concentrations of Methamphetamine Can Protect Dopaminergic Cells against a Larger Oxidative Stress Injury: Mechanistic Study

Mild stress can protect against a larger insult, a phenomenon termed preconditioning or tolerance. To determine if a low intensity stressor could also protect cells against intense oxidative stress in a model of dopamine deficiency associated with Parkinson disease, we used methamphetamine to provide a mild, preconditioning stress, 6-hydroxydopamine (6-OHDA) as a source of potentially toxic oxidative stress, and MN9D cells as a model of dopamine neurons. We observed that prior exposure to subtoxic concentrations of methamphetamine protected these cells against 6-OHDA toxicity, whereas higher concentrations of methamphetamine exacerbated it. The protection by methamphetamine was accompanied by decreased uptake of both [3H] dopamine and 6-OHDA into the cells, which may have accounted for some of the apparent protection. However, a number of other effects of methamphetamine exposure suggest that the drug also affected basic cellular survival mechanisms. First, although methamphetamine preconditioning decreased basal pERK1/2 and pAkt levels, it enhanced the 6-OHDA-induced increase in these phosphokinases. Second, the apparent increase in pERK1/2 activity was accompanied by increased pMEK1/2 levels and decreased activity of protein phosphatase 2. Third, methamphetamine upregulated the pro-survival protein Bcl-2. Our results suggest that exposure to low concentrations of methamphetamine cause a number of changes in dopamine cells, some of which result in a decrease in their vulnerability to subsequent oxidative stress. These observations may provide insights into the development of new therapies for prevention or treatment of PD

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

Abstract This paper proposes and validates a modified cellular automata model for determining interaction rate (i.e. number of car-following/overtaking instances) using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter β in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conventional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interactions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the validity of the model in determining interaction rates. Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles (LMVs) and heavy motor vehicles (HMVs) were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate microscopic traffic flow models