Preliminary Monte Carlo study of CZT response to BNCT (n+γ) background

Boron Neutron Capture Therapy (BNCT) effectiveness depends on the therapeutic dose delivered in tumour when targeted by a sufficient amount of 10B atoms and exposed to a proper flux of thermal neutrons. Presently these quantities are measured indirectly. The availability of an in vivo and real time dose monitoring tool would be a tremendous achievement to fully exploit BNCT. To this end, a Single-Photon Emission Computed Tomography (SPECT) can measure the 478keV γ-ray emitted after 94% of 10B capture reactions. Presently, the Italian National Institute of Nuclear Physics (INFN) is supporting the 3CaTS project whose aim is to develop a dedicated BNCT-SPECT based on CdZnTe (CZT) semiconductor detectors. A BNCT-SPECT must operate in a highly intense (n + γ) radiation field. Thus, it is important to study the response of CZT detectors when working in such challenging conditions. In the present work we focused on three main aspects: i) the spectra of the radiation background expected in an accelerator-based BNCT treatment room; ii) the interaction of the thermal neutrons with cadmium present in the crystal; iii) the estimation of the recorded photon counts spectrum when a 478keV photon source is simulated inside a tissue equivalent phantom

Radiobiology data of melanoma cells after low-energy neutron irradiation and boron compound administration

The cold neutron beam at the PF1b line at the Institut Laue-Langevin (ILL), without fast neutrons and a low contribution of gamma rays, is a very suitable facility to measure cell damage following low-energy neutron irradiation. The biological damage associated with the thermal and the boron doses can be obtained in order to evaluate the relative biological effectiveness (RBE) for Boron Neutron Capture Therapy. Three different experiments were carried out on the A375 melanoma cell line: the first one in a hospital LINAC, to obtain the reference radiation data, and the other two at the ILL, in which the damage to cells with and without boron compounds added was measured

Update of the MDS research criteria for prodromal Parkinson's disease

The MDS Research Criteria for Prodromal PD allow the diagnosis of prodromal Parkinson's disease using an evidence‐based conceptual framework, which was designed to be updated as new evidence becomes available. New prospective evidence of predictive values of risk and prodromal markers published since 2015 was reviewed and integrated into the criteria. Many of the predictive values (likelihood ratios, LR) remain unchanged. The positive likelihood ratio notably increase for olfactory loss and decreased for substantia nigra hyperechogenicity. Negative likelihood ratio remained largely unchanged for all markers. New levels of diagnostic certainty for neurogenic and symptomatic orthostatic hypotension have been added, which substantially differ in positive likelihood ratio from the original publication. For intermediate strength genetic variants, their age‐related penetrance is now incorporated in the calculation of the positive likelihood ratio. Moreover, apart from prospective studies, evidence from cross‐sectional case‐control genome‐wide association studies is also considered (given their likely lack of confounding and reverse causation), and to account for the effect of multiple low‐penetrance genetic variants polygenic risk scores are added to the model. Diabetes, global cognitive deficit, physical inactivity, and low plasma urate levels in men enter the criteria as new markers. A web‐based prodromal PD risk calculator allows the calculation of probabilities of prodromal PD for individuals. Several promising candidate markers may improve the diagnostic accuracy of prodromal PD in the future

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

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

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

Hepatic Abnormalities Associated with Aluminum Loading in Piglets

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141476/1/jpen0293.pd

Efficient RT-QuIC seeding activity for \u3b1-synuclein in olfactory mucosa samples of patients with Parkinson's disease and multiple system atrophy

Background: Parkinson's disease (PD) is a neurodegenerative disorder whose diagnosis is often challenging because symptoms may overlap with neurodegenerative parkinsonisms. PD is characterized by intraneuronal accumulation of abnormal \u3b1-synuclein in brainstem while neurodegenerative parkinsonisms might be associated with accumulation of either \u3b1-synuclein, as in the case of Multiple System Atrophy (MSA) or tau, as in the case of Corticobasal Degeneration (CBD) and Progressive Supranuclear Palsy (PSP), in other disease-specific brain regions. Definite diagnosis of all these diseases can be formulated only neuropathologically by detection and localization of \u3b1-synuclein or tau aggregates in the brain. Compelling evidence suggests that trace-amount of these proteins can appear in peripheral tissues, including receptor neurons of the olfactory mucosa (OM). Methods: We have set and standardized the experimental conditions to extend the ultrasensitive Real Time Quaking Induced Conversion (RT-QuIC) assay for OM analysis. In particular, by using human recombinant \u3b1-synuclein as substrate of reaction, we have assessed the ability of OM collected from patients with clinical diagnoses of PD and MSA to induce \u3b1-synuclein aggregation, and compared their seeding ability to that of OM samples collected from patients with clinical diagnoses of CBD and PSP. Results: Our results showed that a significant percentage of MSA and PD samples induced \u3b1-synuclein aggregation with high efficiency, but also few samples of patients with the clinical diagnosis of CBD and PSP caused the same effect. Notably, the final RT-QuIC aggregates obtained from MSA and PD samples owned peculiar biochemical and morphological features potentially enabling their discrimination. Conclusions: Our study provide the proof-of-concept that olfactory mucosa samples collected from patients with PD and MSA possess important seeding activities for \u3b1-synuclein. Additional studies are required for (i) estimating sensitivity and specificity of the technique and for (ii) evaluating its application for the diagnosis of PD and neurodegenerative parkinsonisms. RT-QuIC analyses of OM and cerebrospinal fluid (CSF) can be combined with the aim of increasing the overall diagnostic accuracy of these diseases, especially in the early stages

Comprehensive in vivo Mapping of the Human Basal Ganglia and Thalamic Connectome in Individuals Using 7T MRI

Basal ganglia circuits are affected in neurological disorders such as Parkinson's disease (PD), essential tremor, dystonia and Tourette syndrome. Understanding the structural and functional connectivity of these circuits is critical for elucidating the mechanisms of the movement and neuropsychiatric disorders, and is vital for developing new therapeutic strategies such as deep brain stimulation (DBS). Knowledge about the connectivity of the human basal ganglia and thalamus has rapidly evolved over recent years through non-invasive imaging techniques, but has remained incomplete because of insufficient resolution and sensitivity of these techniques. Here, we present an imaging and computational protocol designed to generate a comprehensive in vivo and subject-specific, three-dimensional model of the structure and connections of the human basal ganglia. High-resolution structural and functional magnetic resonance images were acquired with a 7-Tesla magnet. Capitalizing on the enhanced signal-to-noise ratio (SNR) and enriched contrast obtained at high-field MRI, detailed structural and connectivity representations of the human basal ganglia and thalamus were achieved. This unique combination of multiple imaging modalities enabled the in-vivo visualization of the individual human basal ganglia and thalamic nuclei, the reconstruction of seven white-matter pathways and their connectivity probability that, to date, have only been reported in animal studies, histologically, or group-averaged MRI population studies. Also described are subject-specific parcellations of the basal ganglia and thalamus into sub-territories based on their distinct connectivity patterns. These anatomical connectivity findings are supported by functional connectivity data derived from resting-state functional MRI (R-fMRI). This work demonstrates new capabilities for studying basal ganglia circuitry, and opens new avenues of investigation into the movement and neuropsychiatric disorders, in individual human subjects

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