Abnormal metabolic network activity in REM sleep behavior disorder

OBJECTIVE: To determine whether the Parkinson disease-related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion. METHODS: For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 +/- 9.4 years old) and 10 healthy volunteers (62.7 +/- 8.6 years old) who underwent resting-state metabolic brain imaging with (18)F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 +/- 4.8 years old) and 17 healthy volunteers (66.6 +/- 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 +/- 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values. RESULTS: PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p \u3c 0.04; cohort 2: p \u3c 0.005). Of the 17 subjects with long-term follow-up, 8 were diagnosed with Parkinson disease or dementia with Lewy bodies; the others did not phenoconvert. For individual subjects with RBD, final phenoconversion status was predicted using a logistical regression model based on PDRP expression and subject age at the time of imaging (r(2) = 0.64, p \u3c 0.0001). CONCLUSIONS: Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BackgroundThe role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.MethodsLungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.ResultsDespite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.ConclusionsVisual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts

Diagnostic value of cerebrospinal fluid alpha-synuclein seed quantification in synucleinopathies

Several studies have confirmed the α-synuclein real-time quaking-induced conversion (RT-QuIC) assay to have high sensitivity and specificity for Parkinson's disease. However, whether the assay can be used as a robust, quantitative measure to monitor disease progression, stratify different synucleinopathies and predict disease conversion in patients with idiopathic REM sleep behaviour disorder remains undetermined. The aim of this study was to assess the diagnostic value of CSF α-synuclein RT-QuIC quantitative parameters in regard to disease progression, stratification and conversion in synucleinopathies. We performed α-synuclein RT-QuIC in the CSF samples from 74 Parkinson's disease, 24 multiple system atrophy and 45 idiopathic REM sleep behaviour disorder patients alongside 55 healthy controls, analysing quantitative assay parameters in relation to clinical data. α-Synuclein RT-QuIC showed 89% sensitivity and 96% specificity for Parkinson's disease. There was no correlation between RT-QuIC quantitative parameters and Parkinson's disease clinical scores (e.g. Unified Parkinson's Disease Rating Scale motor), but RT-QuIC positivity and some quantitative parameters (e.g. Vmax) differed across the different phenotype clusters. RT-QuIC parameters also added value alongside standard clinical data in diagnosing Parkinson's disease. The sensitivity in multiple system atrophy was 75%, and CSF samples showed longer T50 and lower Vmax compared to Parkinson's disease. All RT-QuIC parameters correlated with worse clinical progression of multiple system atrophy (e.g. change in Unified Multiple System Atrophy Rating Scale). The overall sensitivity in idiopathic REM sleep behaviour disorder was 64%. In three of the four longitudinally followed idiopathic REM sleep behaviour disorder cohorts, we found around 90% sensitivity, but in one sample (DeNoPa) diagnosing idiopathic REM sleep behaviour disorder earlier from the community cases, this was much lower at 39%. During follow-up, 14 of 45 (31%) idiopathic REM sleep behaviour disorder patients converted to synucleinopathy with 9/14 (64%) of convertors showing baseline RT-QuIC positivity. In summary, our results showed that α-synuclein RT-QuIC adds value in diagnosing Parkinson's disease and may provide a way to distinguish variations within Parkinson's disease phenotype. However, the quantitative parameters did not correlate with disease severity in Parkinson's disease. The assay distinguished multiple system atrophy patients from Parkinson's disease patients and in contrast to Parkinson's disease, the quantitative parameters correlated with disease progression of multiple system atrophy. Our results also provided further evidence for α-synuclein RT-QuIC having potential as an early biomarker detecting synucleinopathy in idiopathic REM sleep behaviour disorder patients prior to conversion. Further analysis of longitudinally followed idiopathic REM sleep behaviour disorder patients is needed to better understand the relationship between α-synuclein RT-QuIC signature and the progression from prodromal to different synucleinopathies

An Analysis of Factors Contributing to the Development of Total Parenteral Nutrition‐Induced Cholestasis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141659/1/jpen0586.pd

Total parenteral nutrition associated cholestasis: A predisposing factor for sepsis in surgical neonates?

Of 496 neonates and infants less than 1 year of age admitted to the paediatric surgical intensive care unit (PSICU) over a 5 year period (1983-1987), 94 required total parenteral nutrition (TPN) for more than 14 consecutive days, generally due to congenital anomalies of the digestive tract. Cholestasis occurred in 15 of them and 12 of these patients developed sepsis. In contrast, of the 79 patients on TPN that remained free from cholestasis, only 23 developed sepsis. The mortality rate for the TPNAC-group was substantially higher than for the group without TPNAC. It is suggested that development of TPNAC might lead to impairment of non-specific cellular immunity in neonates

Sleep-Related Falling Out of Bed in Parkinson's Disease

Background and purposeSleep-related falling out of bed (SFOB), with its potential for significant injury, has not been a strong focus of investigation in Parkinson's disease (PD) to date. We describe the demographic and clinical characteristics of PD patients with and without SFOB.MethodsWe performed a retrospective analysis of 50 consecutive PD patients, who completed an REM sleep behavior disorder screening questionnaire (RBDSQ), questionnaires to assess for RBD clinical mimickers and questions about SFOB and resulting injuries. Determination of high risk for RBD was based on an RBDSQ score of 5 or greater.ResultsThirteen patients reported history of SFOB (26%). Visual hallucinations, sleep-related injury, quetiapine and amantadine use were more common in those patients reporting SFOB. Twenty-two patients (44%) fulfilled criteria for high risk for RBD, 12 of which (55%) reported SFOB. Five patients reported injuries related to SFOB. SFOB patients had higher RBDSQ scores than non-SFOB patients (8.2±3.0 vs. 3.3±2.0, p<0.01). For every one unit increase in RBDSQ score, the likelihood of SFOB increased two-fold (OR 2.4, 95% CI 1.3-4.2, p<0.003).ConclusionsSFOB may be a clinical marker of RBD in PD and should prompt confirmatory polysomnography and pharmacologic treatment to avoid imminent injury. Larger prospective studies are needed to identify risk factors for initial and recurrent SFOB in PD

High performance 3D CZT spectro-imager for BNCT-SPECT: preliminary characterization

The National Institute of Nuclear Physics (INFN) is supporting the 3CaTS project with the aim of developing a new Single Photon Emission Computed Tomography (SPECT) system for real time 10 B therapeutic dose monitoring in the binary experimental hadron therapy called Boron Neutron Capture Therapy (BNCT). BNCT is a highly selective tumour treatment based on the neutron capture reaction 10 B(n,α) 7 Li. The secondary particles have a high LET with ranges in tissues of the order of 10 μm (thus less than the mean cell diameter of few tens μm). Targeting the 10 B delivery towards cancer, the released energy lethally damages only the malignant cells sparing the normal tissues, thus enabling a cell-level selective treatment. To properly exploit this selectivity it is mandatory to know the 10 B spatial distribution inside patients body during neutron irradiation. This can be achieved by detecting the 478 keV γ ray emitted in the 94% of 10 B capture reactions by a SPECT system. A 3D CZT drift strip detector with a sensitive volume of 20x20x5 mm 3 was developed, able to perform high-resolution X-ray and γ ray spectroscopic imaging (10-1000 keV). The detector signals are analysed by a custom digital multi-channel electronics, based on two pipelined fast and slow analysis, able to perform multi-parameter analysis and fine temporal coincidences (<; 20 ns). Energy resolution of 3.3% (4 keV) and 2% (13 keV) FWHM was measured, with uncollimated sources and no corrections, at 122 keV and 662 keV, respectively

Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition

Purpose of Review We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results. Recent Findings The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion