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While dynamic ocular motor abnormalities (e.g., gaze-evoked nystagmus (GEN), low optokinetic nystagmus (OKN), pursuit and vestibulo-ocular reflex (VOR) gains, and dysmetric saccades) have been shown to be potential biomarkers in spinocerebellar ataxia type 3 (SCA3), the value of static abnormalities (e.g., convergent [esodeviation] and divergent strabismus [exodeviation]) is unknown. Moreover, studies on dynamic abnormalities in SCA3 usually do not take into account the existence of potential abduction-adduction asymmetries in patients with degenerative ataxia. Thirty-eight patients with genetically confirmed SCA3 (24 females; mean age ± SD, 49.8± 12.2 years) and 22 healthy controls (12 females, p = 0.589; mean age ± SD, 50.7± 12.5 years, p = 0.651) underwent clinical and video-oculographic assessment. A p value

Cognitive impairment is an essential feature of Huntington’s disease (HD) and dementia is a predictable outcome in all patients. However, validated instruments to assess global cognitive performance in the field of HD are lacking. We aimed to explore the utility of the Parkinson’s disease-Cognitive Rating Scale (PD-CRS) for the screening of global cognition in HD. A multicenter cohort of 132 HD patients at different disease stages and 33 matched healthy controls were classified as having preserved cognition, mild cognitive impairment (HD-MCI) or dementia (HD-Dem) according to the Clinical Dementia Rating and Functional Independence Score. The PD-CRS and the Mini-Mental State Examination were administered. Receiver operating characteristic curve analysis was used to determine optimal cutoffs to differentiate patients according to their cognitive status. A PD-CRS cutoff score ≤ 81/82 was optimal to detect HD-MCI (sensitivity = 93%; specificity = 80%; area under the curve (AUC) = 0.940), and ≤ 63/64 was optimal to detect HD-Dem (sensitivity = 90%; specificity = 87%; AUC = 0.933). MMSE scores failed to show robust psychometric properties in this context. The PD-CRS is a valid and reliable instrument to assess global cognition in HD in routine clinical care and clinical trials.

The studies of nonlinear models in epidemiology have generated a deep interest in gaining insight into the mechanisms that underlie AIDS-related cancers, providing us with a better understanding of cancer immunity and viral oncogenesis. In this article, we analyze an HIV-1 model incorporating the relations between three dynamical variables: cancer cells, healthy CD4 + T lymphocytes, and infected CD4 + T lymphocytes. Recent theoretical investigations indicate that these cells interactions lead to different dynamical outcomes, for instance to periodic or chaotic behavior. Firstly, we analytically prove the boundedness of the trajectories in the system’s attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. Our calculations reveal that the highest observable variable is the population of cancer cells, thus indicating that these cells could be monitored in future experiments in order to obtain time series for attractor’s reconstruction. We identify different dynamical behaviors of the system varying two biologically meaningful parameters: r 1, representing the uncontrolled proliferation rate of cancer cells, and k 1, denoting the immune system’s killing rate of cancer cells. The maximum Lyapunov exponent is computed to identify the chaotic regimes. Considering very recent developments in the literature related to the homotopy analysis method (HAM), we calculate the explicit series solutions of the cancer model and focus our analysis on the dynamical variable with the highest observability index. An optimal homotopy analysis approach is used to improve the computational efficiency of HAM by means of appropriate values for the convergence control parameter, which greatly accelerate the convergence of the series solution. The approximated analytical solutions are used to compute density plots, which allow us to discuss additional dynamical features of the model.

Alterations in oculomotor performance are among the first observable physical alterations during presymptomatic stages of Huntington's disease (HD). Quantifiable measurements of oculomotor performance have been studied as possible markers of disease status and progression in presymptomatic and early symptomatic stages of HD, on the basis of traditional analysis methods. Whether oculomotor performance can be used to classify individuals according to HD disease stage has yet to be explored via the application of machine-learning methods. In the present study, we report the application of the support vector machine (SVM) algorithm to oculomotor features pooled from a four-task psychophysical experiment. We were able to automatically distinguish control participants from presymptomatic HD (pre-HD) participants with an accuracy of 73.47 %, a sensitivity of 74.31 %, and a specificity of 72.64 %; to distinguish control participants from HD patients with an accuracy of 81.84 %, a sensitivity of 76.19 %, and a specificity of 87.48 %; and to distinguish pre-HD participants from HD patients with an accuracy of 83.54 %, a sensitivity of 92.62 %, and a specificity of 74.45 %. These results demonstrate that the application of supervised classification methods to oculomotor features is a valuable and promising approach to the automatic detection of disease stage in HD.

Mutations in leucine-rich repeat kinase 2 gene (LRRK2) are implicated in autosomal dominant familial and sporadic Parkinson's disease (sPD). Given its relative frequency in PD and its putative function in several cellular pathways that are known to be impaired in the disease, we wanted to tackle LRRK2 physiological role and to address its potential as a PD therapeutic target. We investigated the impact of pharmacological inhibition of LRRK2 kinase activity in control and PD cell function. We provide evidence that physiologically LRRK2, through its kinase activity, regulates mitochondrial fission events and facilitates autophagic degradation by modulating lysosomal cellular localization. Upon LRRK2 inhibition, normal fission decreases, leading to the elongation of mitochondrial network which contributes to a poor degradation of deficient mitochondria. Moreover, LRRK2 inhibition promotes lysosomal perinuclear clustering, through Rab7 that further hinders autophagosomes degradation. These events induce a decrease in the autophagic flow, which contributed directly to a decreased proteolytic degradation of damaged mitochondria. These data resembled the results observed in sPD cells. Interestingly, the LRRK2 kinase activity is increased in sPD cells, and despite its inhibition recovers mitochondrial cellular localization, it did not improve microtubule network-dependent trafficking. Our results provide novel insights into the multiple mechanisms that dictate the association between LRRK2 and mitophagy in sPD, and contribute with new findings that could have important therapeutic implications.

The study of transient dynamical phenomena near bifurcation thresholds has attracted the interest of many researchers due to the relevance of bifurcations in different physical or biological systems. In the context of saddle-node bifurcations, where two or more fixed points collide annihilating each other, it is known that the dynamics can suffer the so-called delayed transition. This phenomenon emerges when the system spends a lot of time before reaching the remaining stable equilibrium, found after the bifurcation, because of the presence of a saddle-remnant in phase space. Some works have analytically tackled this phenomenon, especially in time-continuous dynamical systems, showing that the time delay, τ, scales according to an inverse square-root power law, τ∼(μ−μ c )−1/2, as the bifurcation parameter μ, is driven further away from its critical value, μ c . In this work, we first characterize analytically this scaling law using complex variable techniques for a family of one-dimensional maps, called the normal form for the saddle-node bifurcation. We then apply our general analytic results to a single-species ecological model with harvesting given by a unimodal map, characterizing the delayed transition and the scaling law arising due to the constant of harvesting. For both analyzed systems, we show that the numerical results are in perfect agreement with the analytical solutions we are providing. The procedure presented in this work can be used to characterize the scaling laws of one-dimensional discrete dynamical systems with saddle-node bifurcations.

Background Pathological brain iron deposition has been implicated as a source of neurotoxic reactive oxygen species in Alzheimer (AD) and Parkinson diseases (PD). Iron metabolism is associated with the gene hemochromatosis (HFE Human genome nomenclature committee ID:4886), and mutations in HFE are a cause of the iron mismetabolism disease, hemochromatosis. Several reports have tested the association of HFE variants with neurodegenerative diseases, such as AD and PD with conflicting results. Methods Genotypes were analysed for the two most common variants of HFE in a series of 130 AD, 55 Mild Cognitive Impairment (MCI) and 132 PD patients. Additionally, a series of 115 healthy age-matched controls was also screened. Results A statistically significant association was found in the PD group when compared to controls, showing that the presence of the C282Y variant allele may confer higher risk for developing the disease. Conclusion Taken together these results suggest that the common variants in HFE may be a risk factor for PD, but not for AD in the Portuguese population.