Parkinson's disease with hyposmia and dysautonomia: does it represent a distinct subtype?

BACKGROUND AND PURPOSE: Olfactory dysfunction or dysautonomia is one of the earliest prodromal nonmotor symptoms of Parkinson's disease (PD). We aimed to investigate whether PD patients with dysautonomia and hyposmia at the de novo stage present different prognoses regarding PD dementia (PDD) conversion, motor complication development, and change in levodopa-equivalent doses (LED). METHODS: In this retrograde cohort study, we included 105 patients with newly diagnosed PD patients who underwent cross-cultural smell identification test (CC-SIT), autonomic function tests (AFT), and dopamine transporter (DAT) scan at the de novo stage. PD patients were divided into Hyposmia + /Dysautonomia + (H + /D +) and Hyposmia - /Dysautonomia - (H - /D -) groups depending on the result of AFT and CC-SIT. Baseline clinical, cognitive, imaging characteristics, longitudinal risks of PDD development and motor complication occurrence, and longitudinal LED changes were compared between the two groups. RESULTS: When compared with the H - /D - group, the H + /D + group showed lower standardized uptake value ratios in all subregions, lower asymmetry index, and steeper ventral - dorsal gradient in the DAT scan. The H + /D + group exhibited poorer performance in frontal/executive function and a higher risk of PDD development. The risk of motor complications including levodopa-induced dyskinesia, wearing off, and freezing of gait, was comparable between the two groups. The analysis of longitudinal changes in LED using a linear mixed model showed that the increase of LED in the H + /D + group was more rapid. CONCLUSIONS: Our results suggest that PD patients with dysautonomia and hyposmia at the de novo stage show a higher risk of PD dementia conversion and rapid progression of motor symptoms.

Alteration of medial temporal lobe metabolism related to Alzheimer's disease and dementia with lewy bodies.

BACKGROUND: Association of medial temporal lobe (MTL) metabolism with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) has not been evaluated considering their mixed disease (MD). METHODS: 131 patients with AD, 133 with DLB, 122 with MD, and 28 normal controls (NCs) underwent neuropsychological tests, assessments for parkinsonism, cognitive fluctuation (CF), and visual hallucinations (VH), and 18F-fluorodeoxyglucose PET to quantify MTL metabolism in the amygdala, hippocampus, and entorhinal cortex. The effects of AD and DLB on MTL metabolism were evaluated using general linear models (GLMs). Associations between MTL metabolism, cognition, and clinical features were evaluated using GLMs or logistic regression models separately performed for the AD spectrum (NC + AD + MD), DLB spectrum (NC + DLB + MD), and disease groups (AD + DLB + MD). Covariates included age, sex, and education. RESULTS: AD was associated with hippocampal/entorhinal hypometabolism, whereas DLB was associated with relative amygdalar/hippocampal hypermetabolism. Relative MTL hypermetabolism was associated with lower attention/visuospatial/executive scores and severe parkinsonism in both the AD and DLB spectra and disease groups. Left hippocampal/entorhinal hypometabolism was associated with lower verbal memory scores, whereas right hippocampal hypometabolism was associated with lower visual memory scores in both the AD spectrum and disease groups. Relative MTL hypermetabolism was associated with an increased risk of CF and VH in the disease group, and relative amygdalar hypermetabolism was associated with an increased risk of VH in the DLB spectrum. CONCLUSIONS: Entorhinal-hippocampal hypometabolism and relative amygdala-hippocampal hypermetabolism could be characteristics of AD- and DLB-related neurodegeneration, respectively.

Local Structures of Ex-Solved Nanoparticles Identified by Machine-Learned Potentials.

In this study, we identify the local structures of ex-solved nanoparticles using machine-learned potentials (MLPs). We develop a method for training machine-learned potentials by sampling local structures of heterointerface configurations as a training set with its efficacy tested on the Ni/MgO system, illustrating that the error in interface energy is only 0.004 eV/Å2. Using the developed scheme, we train an MLP for the Ni/La0.5Ca0.5TiO3 ex-solution system and identify the local structures for both exo- and endo-type particles. The established model aligns well with the experimental observations, accurately predicting a nucleation size of 0.45 nm. Lastly, the density functional theory calculations on the established atomistic model verify that the kinetic barrier for the dry reforming of methane are substantially reduced by 0.49 eV on the ex-solved catalysts compared to that on the impregnated catalysts. Our findings offer insights into the local structures, growth mechanisms, and underlying origin of the catalytic properties of ex-solved nanoparticles.