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Register a new userGraphene quantum dots prevent alpha-synucleinopathy in Parkinsons disease
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While the emerging evidence indicates that the pathogenesis of Parkinsons disease (PD) is strongly correlated to the accumulation of alpha-synuclein ({alpha}-syn) aggregates, there has been no clinical success in anti-aggregation agents for the disease to date. Here we show that graphene quantum dots (GQDs) exhibit anti-amyloid activity via direct interaction with {alpha}-syn. Employing biophysical, biochemical, and cell-based assays as well as molecular dynamics (MD) simulation, we find that GQDs have notable potency in not only inhibiting fibrillization of {alpha}-syn but also disaggregating mature fibrils in a time-dependent manner. Remarkably, GQDs rescue neuronal death and synaptic loss, reduce Lewy body (LB)/Lewy neurite (LN) formation, ameliorate mitochondrial dysfunctions, and prevent neuron-to-neuron transmission of {alpha}-syn pathology induced by {alpha}-syn preformed fibrils (PFFs) in neurons. In addition, in vivo administration of GQDs protects against {alpha}-syn PFFs-induced loss of dopamine neurons, LB/LN pathology, and behavioural deficits through the penetration of the blood-brain barrier (BBB). The finding that GQDs function as an anti-aggregation agent provides a promising novel therapeutic target for the treatment of PD and related {alpha}-synucleinopathies.
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The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinsons disease (PD). Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and the free water compartment as well as and reductions in fractional anisotropy. However, studies examining nigral diffusivity changes from PD with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. Iron-sensitive data and diffusion data were analyzed in two cohorts. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment and iron content were found to increase in substantia nigra of the PD group in both cohorts. However, diffusion markers derived from the single-compartment model were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort and validation cohort but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. The variability of single-compartment nigral diffusion metrics in PD may be attributed to competing influences of increased iron content, which drives diffusivity down, and increases in the free water compartment, which drives diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.
Mobility is severely impacted in patients with Parkinsons disease (PD), especially when they experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between voluntary stopping and involuntary stopping caused by FOG is vital for the detection and potential intervention of FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal stop and walk instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was used to study the dynamics of the EEG spectra induced by different walking phases, which included normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that of the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared to that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based treatment strategy that can accurately predict FOG episodes, excluding the potential confound of voluntary stopping.
Parkinsons disease (PD) is a common neurodegenerative disease with a high degree of heterogeneity in its clinical features, rate of progression, and change of variables over time. In this work, we present a novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of PD subtypes and 2) early prediction of disease progression in individual patients. Our subtype identification is based not only on PD variables, but also on their complex patterns of progression, providing a useful tool for the analysis of large heterogenous, longitudinal data. Specifically, we cluster patients based on the similarity of their trajectories through a time series of bipartite networks connecting patients to demographic, clinical, and genetic variables. We apply this approach to demographic and clinical data from the Parkinsons Progression Markers Initiative (PPMI) dataset and identify 3 patient clusters, consistent with 3 distinct PD subtypes, each with a characteristic variable progression profile. Additionally, TPC predicts an individual patients subtype and future disease trajectory, based on baseline assessments. Application of our approach resulted in 74% accurate subtype prediction in year 5 in a test/validation cohort. Furthermore, we show that genetic variability can be integrated seamlessly in our TPC approach. In summary, using PD as a model for chronic progressive diseases, we show that TPC leverages high-dimensional longitudinal datasets for subtype identification and early prediction of individual disease subtype. We anticipate this approach will be broadly applicable to multidimensional longitudinal datasets in diverse chronic diseases.
Alzheimers disease (AD) and Parkinsons disease (PD) are the two most common neurodegenerative disorders in humans. Because a significant percentage of patients have clinical and pathological features of both diseases, it has been hypothesized that the patho-cascades of the two diseases overlap. Despite this evidence, these two diseases are rarely studied in a joint manner. In this paper, we utilize clinical, imaging, genetic, and biospecimen features to cluster AD and PD patients into the same feature space. By training a machine learning classifier on the combined feature space, we predict the disease stage of patients two years after their baseline visits. We observed a considerable improvement in the prediction accuracy of Parkinsons dementia patients due to combined training on Alzheimers and Parkinsons patients, thereby affirming the claim that these two diseases can be jointly studied.
Berry phase plays an important role in determining many physical properties of quantum systems. However, a Berry phase altering energy spectrum of a quantum system is comparatively rare. Here, we report an unusual tunable valley polarized energy spectra induced by continuously tunable Berry phase in Bernal-stacked bilayer graphene quantum dots. In our experiment, the Berry phase of electron orbital states is continuously tuned from about pi to 2pi by perpendicular magnetic fields. When the Berry phase equals pi or 2pi, the electron states in the two inequivalent valleys are energetically degenerate. By altering the Berry phase to noninteger multiples of pi, large and continuously tunable valley polarized energy spectra are detected in our experiment. The observed Berry phase-induced valley splitting, on the order of 10 meV at a magnetic field of 1 T, is about 100 times larger than Zeeman splitting for spin, shedding light on graphene-based valleytronics.
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