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Abstract Parkinson’s disease (PD) is characterised by the progressive loss of midbrain dopaminergic neurons and the presence of aggregated α-synuclein (α-syn). Pericytes and microglia, two non-neuronal cells contain α-syn in the human brain, however, their role in disease processes is poorly understood. Pericytes, found surrounding the capillaries in the brain are important for maintaining the blood–brain barrier, controlling blood flow and mediating inflammation. In this study, primary human brain pericytes and microglia were exposed to two different α-synuclein aggregates. Inflammatory responses were assessed using immunocytochemistry, cytometric bead arrays and proteome profiler cytokine array kits. Fixed flow cytometry was used to investigate the uptake and subsequent degradation of α-syn in pericytes. We found that the two α-syn aggregates are devoid of inflammatory and cytotoxic actions on human brain derived pericytes and microglia. Although α-syn did not induce an inflammatory response, pericytes efficiently take up and degrade α-syn through the lysosomal pathway but not the ubiquitin–proteasome system. Furthermore, when pericytes were exposed the ubiquitin proteasome inhibitor—MG132 and α-syn aggregates, there was profound cytotoxicity through the production of reactive oxygen species resulting in apoptosis. These results suggest that the observed accumulation of α-syn in pericytes in human PD brains likely plays a role in PD pathogenesis, perhaps by causing cerebrovascular instability, under conditions of cellular stress.

Abstract Methods for predicting circadian phase have been developed for healthy individuals. It is unknown whether these methods generalize to clinical populations, such as delayed sleep–wake phase disorder (DSWPD), where circadian timing is associated with functional outcomes. This study evaluated two methods for predicting dim light melatonin onset (DLMO) in 154 DSWPD patients using ~ 7 days of sleep–wake and light data: a dynamic model and a statistical model. The dynamic model has been validated in healthy individuals under both laboratory and field conditions. The statistical model was developed for this dataset and used a multiple linear regression of light exposure during phase delay/advance portions of the phase response curve, as well as sleep timing and demographic variables. Both models performed comparably well in predicting DLMO. The dynamic model predicted DLMO with root mean square error of 68 min, with predictions accurate to within ± 1 h in 58% of participants and ± 2 h in 95%. The statistical model predicted DLMO with root mean square error of 57 min, with predictions accurate to within ± 1 h in 75% of participants and ± 2 h in 96%. We conclude that circadian phase prediction from light data is a viable technique for improving screening, diagnosis, and treatment of DSWPD.

We utilize a Fourier transformation-based representation of Maxwell's equations to develop physics-constrained neural networks for electrodynamics without gauge ambiguity, which we label the Fourier-Helmholtz-Maxwell neural operator method. In this approach, both of Gauss's laws and Faraday's law are built in as hard constraints, as well as the longitudinal component of Ampère-Maxwell in Fourier space, assuming the continuity equation. An encoder-decoder network acts as a solution operator for the transverse components of the Fourier transformed vector potential, A ^ ⊥ ( k , t ) , whose two degrees of freedom are used to predict the electromagnetic fields. This method was tested on two electron beam simulations. Among the models investigated, it was found that a U-Net architecture exhibited the best performance as it trained quicker, was more accurate and generalized better than the other architectures examined. We demonstrate that our approach is useful for solving Maxwell's equations for the electromagnetic fields generated by intense relativistic charged particle beams and that it generalizes well to unseen test data, while being orders of magnitude quicker than conventional simulations. We show that the model can be re-trained to make highly accurate predictions in as few as 20 epochs on a previously unseen data set., (© 2024. The Author(s).)

Reperfusion is the only existing strategy for patients with acute ischemic stroke, however it causes further brain damage itself. A feasible therapy targeting reperfusion injury is remote ischemic conditioning (RIC). This was a two-centre, randomized, blinded international study, using translational imaging endpoints, aimed to examine the neuroprotective effects of RIC in ischemic stroke model. 80 male rats underwent 90-min middle cerebral artery occlusion. RIC consisted of 4 × 5 min cycles of left hind limb ischemia. The primary endpoint was infarct size measured on T2-weighted MRI at 24 h, expressed as percentage of the area-at-risk. Secondary endpoints were: hemispheric space-modifying edema, infarct growth between per-occlusion and 24 h MRI, neurofunctional outcome measured by neuroscores. 47 rats were included in the analysis after applying pre-defined inclusion criteria. RIC significantly reduced infarct size (median, interquartile range: 19% [8%; 32%] vs control: 40% [17%; 59%], p = 0.028). This effect was still significant after adjustment for apparent diffusion coefficient lesion size in multivariate analysis. RIC also improved neuroscores (6 [3; 8] vs control: 9 [7; 11], p = 0.032). Other secondary endpoints were not statistically different between groups. We conclude that RIC in the setting of acute ischemic stroke in rats is safe, reduces infarct size and improves functional recovery.

We analyze the electronic properties of a hybrid graphene-BN nanoribbon system, using a Hubbard model Hamiltonian within a mean field approximation. Due to the different electronegativities of the boron and nitrogen atoms, an electric field is induced across the zigzag graphene strip, breaking the spin degeneracy of the electronic band structure. Optimal tight-binding parameters are found from first-principles calculations. Edge potentials are proposed as corrections for the on-site energies, modeling the BN-graphene nanoribbon interfaces. We show that half-metallic responses in the hybrid systems may be driven with the help of an external electric field. We also study the role of defects across the graphene nanoribbon and at the h-BN/graphene interface regions. Modulations on the spin-dependent gaps may be achieved depending on the nature and position of the defect, constituting a way towards spin-gap engineering by means of spatial doping.