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Photodynamic Treatment employing Talaporfin Sea for the Recurrence regarding Cholangiocarcinoma soon after Surgical Resection.
Alzheimer's disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). selleck inhibitor However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.Alzheimer's disease (AD) is a degenerative brain disease and the most common cause of dementia. Early stage β-amyloid oligomers (AβOs) and late stage Aβ plaques are the pathological hallmarks of AD brains. AβOs are known to be more neurotoxic and contribute to neuronal damage. Most current approaches are focused on detecting Aβ plaques, which occurs at the late stage of AD, and are limited by poor sensitivity and/or contrast agent toxicity. In previous studies, we developed a new curcumin-conjugated magnetic nanoparticle (Cur-MNPs) to target the Aβ pathologies. In this study, we investigate the in vivo feasibility of this novel Cur-MNPs to detect Aβ pathologies at the early and late stages of AD in transgenic AD mice and perform immunohistochemical examinations to validate the specific targeting of various form of Aβ pathologies.Simultaneously resting brain glucose metabolism and intrinsic functional activity, by integrated PET/MRI scans, both reflect nerve actions. Studies showed that there existed relevance between two phenotypes of neuros in normal human brains. However, whether the relevance will change in cognitive dysfunction (CD) brains is still unknown. The aim of this study therefore is to explore the relevance between voxel-wise glucose metabolism and functional connectivity in Chinese CD people. The dataset in this study included two imaging modalities and clinical information of 21 healthy control (HC) individuals and 15 CD patients, from Xuanwu hospital, Beijing, China. Firstly, we calculated the standardized uptake value rate (SUVR) from positron emission tomography (PET), and three parameters for intrinsic functional activity from functional magnetic resonance imaging (fMRI), including amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Second, the two sample t-test was used to compare each parameter between HC and CD groups respectively. Third, the relevance between SUVR and the three fMRI parameters were measured by Spearman's rank correlation. The results of t-test showed that glucose metabolism consumption decreased in Default Mode Network (DMN) (p less then 0.01), and the damage of functional connection also happened DMN area in CD group. The correlation between glucose metabolism and functional activity in CD group was lower than that in HC group in DMN. Especially, the correlation between SUVR and ReHo was significantly reduced (p less then 0.05). Above results promoted a deeper understanding on the pathogenesis of cognitive impairment, and providing new biomarkers to discriminate CD and HC subjects.Neuronal-related activity can be estimated from functional magnetic resonance imaging (fMRI) data with no knowledge of the timings of blood oxygenation level-dependent (BOLD) events by means of deconvolution with regularized least-squares. This work proposes two improvements on the deconvolution algorithm of sparse paradigm free mapping (SPFM) a new formulation that enables the estimation of neuronal events with long, sustained activity; and the implementation of a subsampling approach based on stability selection that avoids the choice of any regularization parameter. The proposed method is evaluated on real fMRI data and compared with both the original SPFM algorithm and conventional analysis with a general linear model (GLM) that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel stability-based SPFM algorithm yields activation maps with higher resemblance to the maps obtained with GLM analyses and offers improved detection of neuronal-related events over SPFM, particularly in scenarios with low contrast-to-noise ratio.A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.
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