Thalamic GABA levels and Occupational Manganese Neurotoxicity: Association with Exposure Levels and Brain MRI

Excessive occupational exposure to Manganese (Mn) has been associated with clinical symptoms resembling idiopathic Parkinson’s disease (IPD), impairing cognitive and motor functions. Several studies point towards an involvement of the brain neurotransmitter system in Mn intoxication, which is hypothesized to be disturbed prior to onset of symptoms. Edited Magnetic Resonance Spectroscopy (MRS) offers the unique possibility to measure γ-amminobutyric acid (GABA) and other neurometabolites in vivo non-invasively in workers exposed to Mn. In addition, the property of Mn as Magnetic Resonance Imaging (MRI) contrast agent may be used to study Mn deposition in the human brain. In this study, using MRI, MRS, personal air sampling at the working place, work history questionnaires, and neurological assessment (UPDRS-III), the effects of chronic Mn exposure on the thalamic GABAergic system was studied in a group of welders (N = 39) with exposure to Mn fumes in a typical occupational setting. Two subgroups of welders with different exposure levels (Low: N = 26; mean air Mn = 0.13 ± 0.1 mg/m3; High: N = 13; mean air Mn = 0.23 ± 0.18 mg/m3), as well as unexposed control workers (N = 22, mean air Mn = 0.002 ± 0.001 mg/m3) were recruited. The group of welders with higher exposure showed a significant increase of thalamic GABA levels by 45% (p < 0.01, F(1,33) = 9.55), as well as significantly worse performance in general motor function (p < 0.01, F(1,33) = 11.35). However, welders with lower exposure did not differ from the controls in GABA levels or motor performance. Further, in welders the thalamic GABA levels were best predicted by past-12-months exposure levels and were influenced by the Mn deposition in the substantia nigra and globus pallidus. Importantly, both thalamic GABA levels and motor function displayed a non-linear pattern of response to Mn exposure, suggesting a threshold effect

Detecting seasonal cycle shift on streamflow over Turkey by using multivariate statistical methods

Climate change analysis includes the study of several types of variables such as temperature, precipitation, carbon emission, and streamflow. In this study, we focus on basin hydrology and, in particular, on streamflow values. They are geographic and climatologic indicators utilized in the study of basins. We analyze these values to better understand monthly and seasonal change over a 40-year period for all basins in Turkey. Our study differs from others by applying multivariate analysis into the streamflow data implementations rather than on trend, frequency, and/or distribution-based analysis. The characteristics of basins and climate change effects are visualized and examined with monthly data by using cluster analysis, multidimensional scaling, and gCLUTO (graphical Clustering Toolkit). As a result, we classify months as lowflow and high-flow periods. Multidimensional scaling proves that there is a clockwise movement of months from one decade to the next, which is the indicator of seasonal shift. Finally, the gCLUTO tool is utilized in a novel way in the hydrology field by revealing the seasonal change and visualizing the current changing structure of streamflow