Samara F Kiihl

Publications (2)4.35 Total impact

• Article: Manganese exposure induces microglia activation and dystrophy in the substantia nigra of non-human primates.

  Tatyana Verina, Samara F Kiihl, Jay S Schneider, Tomás R Guilarte
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  ABSTRACT: Chronic manganese (Mn) exposure produces neurological deficits including a form of parkinsonism that is different from Parkinson's disease (PD). In chronic Mn exposure, dopamine neurons in the substantia nigra (SN) do not degenerate but they appear to be dysfunctional. Further, previous studies have suggested that the substantia nigra pars reticulata (SNr) is affected by Mn. In the present study, we investigated whether chronic Mn exposure induces microglia activation in the substantia nigra pars compacta (SNc) and SNr in Cynomolgus macaques. Animals were exposed to different weekly doses of Mn (3.3-5.0, 5.0-6.7, 8.3-10 mg Mn/kg body weight) and microglia were examined in the substantia nigra using LN3 immunohistochemistry. We observed that in control animals, LN3 labeled microglia were characterized by a resting phenotype. However, in Mn-treated animals, microglia increased in number and displayed reactive changes with increasing Mn exposure. This effect was more prominent in the SNr than in the SNc. In the SNr of animals administered the highest Mn dose, microglia activation was the most advanced and included dystrophic changes. Reactive microglia expressed increased iNOS, L-ferritin, and intracellular ferric iron which were particularly prominent in dystrophic compartments. Our observations indicate that moderate Mn exposure produces structural changes on microglia, which may have significant consequences on their function.
  NeuroToxicology 11/2010; 32(2):215-26. · 3.10 Impact Factor
• Article: Asymptotic behavior of the scaled mutation rate estimators.

  Hildete P Pinheiro, Samara F Kiihl, Aluísio Pinheiro, Sérgio F dos Reis
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  ABSTRACT: Important aspects of population evolution have been investigated using nucleotide sequences. Under the neutral Wright-Fisher model, the scaled mutation rate represents twice the average number of new mutations per generations and it is one of the key parameters in population genetics. In this study, we present various methods of estimation of this parameter, analytical studies of their asymptotic behavior as well as comparisons of the distribution's behavior of these estimators through simulations. As knowledge of the genealogy is needed to estimate the maximum likelihood estimator (MLE), an application with real data is also presented, using jackknife to correct the bias of the MLE, which can be generated by the estimation of the tree. We proved analytically that the Waterson's estimator and the MLE are asymptotically equivalent with the same rate of convergence to normality. Furthermore, we showed that the MLE has a better rate of convergence than Waterson's estimator for values of the parameter greater than one and this relationship is reversed when the parameter is less than one.
  Biometrical Journal 06/2010; 52(3):400-16. · 1.25 Impact Factor