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1.
Molecules ; 26(7)2021 Mar 30.
Artigo em Inglês | MEDLINE | ID: mdl-33808318

RESUMO

Liver disorders have been recognized as one major health concern. Fucoidan, a sulfated polysaccharide extracted from the brown seaweed Fucus serratus, has previously been reported as an anti-inflammatory and antioxidant. However, the discovery and validation of its hepatoprotective properties and elucidation of its mechanisms of action are still unknown. The objective of the current study was to investigate the effect and possible modes of action of a treatment of fucoidan against thioacetamide (TAA)-induced liver injury in male C57BL/6 mice by serum biochemical and histological analyses. The mouse model for liver damage was developed by the administration of TAA thrice a week for six weeks. The mice with TAA-induced liver injury were orally administered fucoidan once a day for 42 days. The treated mice showed significantly higher body weights; food intakes; hepatic antioxidative enzymes (catalase, glutathione peroxidase (GPx), and superoxide dismutase (SOD)); and a lower serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and C-reactive protein (CRP) levels. Additionally, a reduced hepatic IL-6 level and a decreased expression of inflammatory-related genes, such as cyclooxygenase-2 (COX-2), and inducible nitric oxide synthase (iNOS) mRNA was observed. These results demonstrated that fucoidan had a hepatoprotective effect on liver injury through the suppression of the inflammatory responses and acting as an antioxidant. In addition, here, we validated the use of fucoidan against liver disorders with supporting molecular data.

2.
Environ Sci Technol ; 55(6): 3568-3581, 2021 03 16.
Artigo em Inglês | MEDLINE | ID: mdl-33656863

RESUMO

Peroxyacetyl nitrate (PAN) is an important indicator for photochemical pollution, formed similar to ozone in the photochemistry of certain volatile organic compounds (VOCs) in the presence of nitrogen oxides, and has displayed surprisingly high concentrations during wintertime that were better correlated to particulate rather than ozone concentrations, for which the reasons remained unknown. In this study, wintertime observations of PAN, VOCs, PM2.5, HONO, and various trace gases were investigated to find the relationship between aerosols and wintertime PAN formation. Wintertime photochemical pollution was affirmed by the high PAN concentrations (average: 1.2 ± 1.1 ppb, maximum: 7.1 ppb), despite low ozone concentrations. PAN concentrations were determined by its oxygenated VOC (OVOC) precursor concentrations and the NO/NO2 ratios and can be well parameterized based on the understanding of their chemical relationship. Data analysis and box modeling results suggest that PAN formation was mostly contributed by VOC aging processes involving OH oxidation or photolysis rather than ozonolysis pathways. Heterogeneous reactions on aerosols have supplied key photochemical oxidants such as HONO, which produced OH radicals upon photolysis, promoting OVOC formation and thereby enhancing PAN production, explaining the observed PM2.5-OVOC-PAN intercorrelation. In turn, parts of these OVOCs might participate in the formation of secondary organic aerosol, further aggravating haze pollution as a feedback. Low wintertime temperatures enable the long-range transport of PAN to downwind regions, and how that will impact their oxidation capacity and photochemical pollution requires further assessment in future studies.


Assuntos
Poluentes Atmosféricos , Ozônio , Aerossóis , Poluentes Atmosféricos/análise , China , Monitoramento Ambiental , Ozônio/análise , Ácido Peracético/análogos & derivados
3.
J Genet Genomics ; 2021 Feb 09.
Artigo em Inglês | MEDLINE | ID: mdl-33753019

RESUMO

Microtus fortis is the only mammalian host that exhibits intrinsic resistance against Schistosoma japonicum infection. However, the underlying molecular mechanisms of this resistance are not yet known. Here, we performed the first de novo genome assembly of M. fortis, comprehensive gene annotation analysis, and evolution analysis. Furthermore, we compared the recovery rate of schistosomes, pathological changes, and liver transcriptomes between M. fortis and mice at different time points after infection. We observed that the time and type of immune response in M. fortis were different from those in mice. M. fortis activated immune and inflammatory responses on the 10th day post infection, such as leukocyte extravasation, antibody activation, Fc-gamma receptor-mediated phagocytosis, and the interferon signaling cascade, which played important roles in preventing the development of schistosomes. In contrast, an intense immune response occurred in mice at the late stages of infection and could not eliminate schistosomes. Infected mice suffered severe pathological injury and continuous decreases in cell cycle, lipid metabolism, and other functions. Our findings offer new insights into the intrinsic resistance mechanism of M. fortis against schistosome infection. The genome sequence also provides the basis for future studies of other important traits in M. fortis.

4.
IEEE Trans Med Imaging ; PP2021 Mar 30.
Artigo em Inglês | MEDLINE | ID: mdl-33784617

RESUMO

Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to facilitate neuroimaging studies. Though achieving good performance, available methods are either time consuming or not flexible to extend to multiple or high dimensional features. Considering the explosive availability of large-scale and multimodal brain MRI data, fast surface registration methods that can flexibly handle multimodal features are desired. In this study, we develop a Superfast Spherical Surface Registration (S3Reg) framework for the cerebral cortex. Leveraging an end-to-end unsupervised learning strategy, S3Reg offers great flexibility in the choice of input feature sets and output similarity measures for registration, and meanwhile reduces the registration time significantly. Specifically, we exploit the powerful learning capability of spherical Convolutional Neural Network (CNN) to directly learn the deformation fields in spherical space and implement diffeomorphic design with "scaling and squaring" layers to guarantee topology-preserving deformations. To handle the polar-distortion issue, we construct a novel spherical CNN model using three orthogonal Spherical U-Nets. Experiments are performed on two different datasets to align both adult and infant multimodal cortical features. Results demonstrate that our S3Reg shows superior or comparable performance with state-of-the-art methods, while improving the registration time from 1 min to 10 sec.

5.
Genome Biol Evol ; 13(1)2021 Jan 07.
Artigo em Inglês | MEDLINE | ID: mdl-33462601

RESUMO

Metagenomic studies permit the exploration of microbial diversity in a defined habitat, and binning procedures enable phylogenomic analyses, taxon description, and even phenotypic characterizations in the absence of morphological evidence. Such lineages include asgard archaea, which were initially reported to represent archaea with eukaryotic cell complexity, although the first images of such an archaeon show simple cells with prokaryotic characteristics. However, these metagenome-assembled genomes (MAGs) might suffer from data quality problems not encountered in sequences from cultured organisms due to two common analytical procedures of bioinformatics: assembly of metagenomic sequences and binning of assembled sequences on the basis of innate sequence properties and abundance across samples. Consequently, genomic sequences of distantly related taxa, or domains, can in principle be assigned to the same MAG and result in chimeric sequences. The impacts of low-quality or chimeric MAGs on phylogenomic and metabolic prediction remain unknown. Debates that asgard archaeal data are contaminated with eukaryotic sequences are overshadowed by the lack of evidence indicating that individual asgard MAGs stem from the same chromosome. Here, we show that universal proteins including ribosomal proteins of asgard archaeal MAGs fail to meet the basic phylogenetic criterion fulfilled by genome sequences of cultured archaea investigated to date: These proteins do not share common evolutionary histories to the same extent as pure culture genomes do, pointing to a chimeric nature of asgard archaeal MAGs. Our analysis suggests that some asgard archaeal MAGs represent unnatural constructs, genome-like patchworks of genes resulting from assembly and/or the binning process.

6.
IEEE Trans Med Imaging ; 40(5): 1363-1376, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33507867

RESUMO

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

7.
IEEE Trans Med Imaging ; 40(4): 1217-1228, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33417540

RESUMO

Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we create the 1-ring filter on spherical cortical triangular meshes and accordingly develop convolution/pooling operations for constructing Spherical U-Net for cortical surface data. However, the regular nature of the 1-ring filter makes it inherently limited to model fixed geometric transformations. To further enhance the transformation modeling capability of Spherical U-Net, we introduce the deformable convolution and deformable pooling to cortical surface data and accordingly propose the Spherical Deformable U-Net (SDU-Net). Specifically, spherical offsets are learned to freely deform the 1-ring filter on the sphere to adaptively localize cortical structures with different sizes and shapes. We then apply the SDU-Net to two challenging and scientifically important tasks in neuroimaging: cortical surface parcellation and cortical attribute map prediction. Both applications validate the competitive performance of our approach in accuracy and computational efficiency in comparison with state-of-the-art methods.

8.
Med Image Anal ; 68: 101853, 2020 Oct 17.
Artigo em Inglês | MEDLINE | ID: mdl-33264713

RESUMO

The connectional map of the baby brain undergoes dramatic changes over the first year of postnatal development, which makes its mapping a challenging task, let alone learning how to predict its evolution. Currently, learning models for predicting brain connectomic developmental trajectories remain broadly absent despite their great potential in spotting atypical neurodevelopmental disorders early. This is most likely due to the scarcity and often incompleteness of longitudinal infant neuroimaging studies for training such models. In this paper, we propose the first approach for progressively predicting longitudinal development of brain networks during the postnatal period solely from a baseline connectome around birth. To this end, a supervised multi-regression sample selection strategy is designed to learn how to identify the best set of neighbors of a testing baseline connectome to eventually predict its evolution trajectory at follow-up timepoints. However, given that the training dataset may have missing samples (connectomes) at certain timepoints, this may affect the training of the predictive model. To overcome this problem, we perform a low-rank tensor completion based on a robust principal component analysis to impute the missing training connectomes by linearly approximating similar complete training networks. In the prediction step, our sample selection strategy aims to preserve spatiotemporal relationships between consecutive timepoints. Therefore, the proposed method learns how to identify the set of the local closest neighbors to a target network by training an ensemble of bidirectional regressors leveraging temporal dependency between consecutive timepoints with a recall to the baseline observations to progressively predict the evolution of a testing network over time. Our method achieves the best prediction results and better captures the dynamic changes of each brain connectome over time in comparison to its ablated versions using leave-one-out cross-validation strategy.

9.
Proc Natl Acad Sci U S A ; 117(38): 23904-23913, 2020 09 22.
Artigo em Inglês | MEDLINE | ID: mdl-32868436

RESUMO

Adult brains are functionally flexible, a unique characteristic that is thought to contribute to cognitive flexibility. While tools to assess cognitive flexibility during early infancy are lacking, we aimed to assess the spatiotemporal developmental features of "neural flexibility" during the first 2 y of life. Fifty-two typically developing children 0 to 2 y old were longitudinally imaged up to seven times during natural sleep using resting-state functional MRI. Using a sliding window approach, MR-derived neural flexibility, a quantitative measure of the frequency at which brain regions change their allegiance from one functional module to another during a given time period, was used to evaluate the temporal emergence of neural flexibility during early infancy. Results showed that neural flexibility of whole brain, motor, and high-order brain functional networks/regions increased significantly with age, while visual regions exhibited a temporally stable pattern, suggesting spatially and temporally nonuniform developmental features of neural flexibility. Additionally, the neural flexibility of the primary visual network at 3 mo of age was significantly and negatively associated with cognitive ability evaluated at 5/6 y of age. The "flexible club," comprising brain regions with neural flexibility significantly higher than whole-brain neural flexibility, were consistent with brain regions known to govern cognitive flexibility in adults and exhibited unique characteristics when compared to the functional hub and diverse club regions. Thus, MR-derived neural flexibility has the potential to reveal the underlying neural substrates for developing a cognitively flexible brain during early infancy.


Assuntos
Encéfalo/crescimento & desenvolvimento , Encéfalo/fisiologia , Encéfalo/diagnóstico por imagem , Pré-Escolar , Cognição/fisiologia , Feminino , Humanos , Lactente , Recém-Nascido , Imagem por Ressonância Magnética , Masculino , Descanso/fisiologia
10.
Environ Pollut ; 265(Pt A): 115062, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32806405

RESUMO

Measuring ammonia (NH3) is important for understanding the role of NH3 in secondary aerosol formation and the atmospheric deposition of reactive N. In this study, NH3 was measured in an urban area, a background region, and a tunnel in Beijing. The average NH3 concentrations between September 2017 and August 2018 were 24.8 ± 14.8 ppb and 11.6 ± 10.3 ppb in the urban area and background region, respectively. Higher NH3 concentrations at both the urban and background sites, relative to some earlier measurements indicated a likely increase in the NH3 concentrations in these regions. The urban NH3 level in Beijing was much higher than that typically observed at urban and industrial sites in other domestic and foreign cities, suggesting that the Beijing urban area was affected by greater NH3 emissions than other regions. Based on the relationship among NH3, wind direction, and wind speed, the urban area was affected by both local emissions and air transported from North China Plain (NCP). Potential source contribution function analyses suggested that regional transport from the NCP could greatly affect local concentrations of NH3 in both urban and background areas in spring and autumn; however, in addition to the NCP, urban emissions could also affect NH3 levels in the background region in summer and winter. The average NH3 concentration at the Fenshuiling Tunnel was 8.5 ± 7.7 ppb from December 2017 to February 2018. The NH3:CO emission ratio measured in the tunnel test was 0.022 ± 0.038 ppb/ppb, which was lower than values in the USA and South Korea. The contribution of traffic to NH3 in Beijing did not agree well with the available emission inventories, suggesting that vehicular emissions were underestimated and further evaluation is necessary.


Assuntos
Poluentes Atmosféricos/análise , Amônia/análise , Pequim , China , Cidades , Monitoramento Ambiental , República da Coreia
12.
IEEE Trans Med Imaging ; 39(11): 3607-3618, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32746109

RESUMO

During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, invo- lving differentiation of neuronal types, dendritic arbori- zation, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b -values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion proce- sses, characterizing restricted and hindered diffusion stemming respectively from intra- and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ( µ FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

13.
IEEE Trans Med Imaging ; 39(11): 3691-3702, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32746115

RESUMO

Fast and automated image quality assessment (IQA) of diffusion MR images is crucial for making timely decisions for rescans. However, learning a model for this task is challenging as the number of annotated data is limited and the annotation labels might not always be correct. As a remedy, we will introduce in this paper an automatic image quality assessment (IQA) method based on hierarchical non-local residual networks for pediatric diffusion MR images. Our IQA is performed in three sequential stages, i.e., 1) slice-wise IQA, where a nonlocal residual network is first pre-trained to annotate each slice with an initial quality rating (i.e., pass/questionable/fail), which is subsequently refined via iterative semi-supervised learning and slice self-training; 2) volume-wise IQA, which agglomerates the features extracted from the slices of a volume, and uses a nonlocal network to annotate the quality rating for each volume via iterative volume self-training; and 3) subject-wise IQA, which ensembles the volumetric IQA results to determine the overall image quality pertaining to a subject. Experimental results demonstrate that our method, trained using only samples of modest size, exhibits great generalizability, and is capable of conducting rapid hierarchical IQA with near-perfect accuracy.

14.
IEEE Trans Med Imaging ; 39(12): 4137-4149, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32746154

RESUMO

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated on infant age (35 to 848 days after birth) prediction using incomplete multimodal neuroimages. The mean absolute error of the prediction based on DMM-AAE reaches 37.6 days, outperforming state-of-the-art methods. Generally, our proposed DMM-AAE can serve as a promising model for prediction with multimodal data.

16.
Cereb Cortex ; 30(11): 5626-5638, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32537641

RESUMO

Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal resting-state functional magnetic resonance imaging dataset from 51 typically developing infants and, for the first time, thoroughly investigated how the temporal variability of the FC architecture develops at the "global" (entire brain), "mesoscale" (functional system), and "local" (brain region) levels in the first 2 years of age. Our results revealed that, in such a pivotal stage, 1) the whole-brain FC dynamic is linearly increased; 2) the high-order functional systems tend to display increased FC dynamics for both within- and between-network connections, while the primary systems show the opposite trajectories; and 3) many frontal regions have increasing FC dynamics despite large heterogeneity in developmental trajectories and velocities. All these findings indicate that the brain is gradually reconfigured toward a more flexible, dynamic, and adaptive system with globally increasing but locally heterogeneous trajectories in the first 2 postnatal years, explaining why infants have rapidly developing high-order cognitive functions and complex behaviors.

17.
Neuroimage ; 218: 116978, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32447015

RESUMO

Perivascular spaces (PVSs) are fluid-filled spaces surrounding penetrating blood vessels in the brain and are an integral pathway of the glymphatic system. A PVS and the enclosed blood vessel are commonly visualized as a single vessel-like complex (denoted as PVSV) in high-resolution MRI images. Quantitative characterization of the PVSV morphology in MRI images in healthy subjects may serve as a reference for detecting disease related PVS and/or blood vessel alterations in patients with brain diseases. To this end, we evaluated the age dependences, spatial heterogeneities, and dynamic properties of PVSV morphological features in 45 healthy subjects (21-55 years old), using an ultra-high-resolution three-dimensional transverse relaxation time weighted MRI sequence (0.41 â€‹× â€‹0.41 â€‹× â€‹0.4 â€‹mm3) at 7T. Quantitative PVSV parameters, including apparent diameter, count, volume fraction (VF), and relative contrast to noise ratio (rCNR) were calculated in the white matter and subcortical structures. Dynamic changes were induced by carbogen breathing which are known to induce vasodilation and increase the blood oxygenation level in the brain. PVSV count and VF significantly increased with age in basal ganglia (BG), so did rCNR in BG, midbrain, and white matter (WM). Apparent PVSV diameter also showed a positive association with age in the three brain regions, although it did not reach statistical significance. The PVSV VF and count showed large inter-subject variations, with coefficients of variation ranging from 0.17 to 0.74 after regressing out age and gender effects. Both apparent diameter and VF exhibited significant spatial heterogeneity, which cannot be explained solely by radio-frequency field inhomogeneities. Carbogen breathing significantly increased VF in BG and WM, and rCNR in thalamus, BG, and WM compared to air breathing. Our results are consistent with gradual dilation of PVSs with age in healthy adults. The PVSV morphology exhibited spatial heterogeneity and large inter-subject variations and changed during carbogen breathing compared to air breathing.

18.
Artigo em Inglês | MEDLINE | ID: mdl-32396089

RESUMO

In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with nearperfect accuracy.

19.
Clin Genitourin Cancer ; 18(5): 378-386.e1, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32147364

RESUMO

INTRODUCTION: Computed tomography (CT) has limited diagnostic accuracy for staging of muscle-invasive bladder cancer (MIBC). [18F] Fluorodeoxyglucose positron emission tomography (FDG-PET)/magnetic resonance imaging (MRI) is a novel imaging modality incorporating functional imaging with improved soft tissue characterization. This pilot study evaluated the use of preoperative FDG-PET/MRI for staging of MIBC. PATIENTS AND METHODS: Twenty-one patients with MIBC with planned radical cystectomy were enrolled. Two teams of radiologists reviewed FDG-PET/MRI scans to determine: (1) presence of primary bladder tumor; and (2) lymph node involvement and distant metastases. FDG-PET/MRI was compared with cystectomy pathology and computed tomography (CT). RESULTS: Eighteen patients were included in the final analysis, most (72.2%) of whom received neoadjuvant chemotherapy. Final pathology revealed 10 (56%) patients with muscle invasion and only 3 (17%) patients with lymph node involvement. Clustered analysis of FDG-PET/MRI radiology team reads revealed a sensitivity of 0.80 and a specificity of 0.56 for detection of the primary tumor with a sensitivity of 0 and a specificity of 1.00 for detection of lymph node involvement when compared with cystectomy pathology. CT imaging demonstrated similar rates in evaluation of the primary tumor (sensitivity, 0.91; specificity, 0.43) and lymph node involvement (sensitivity, 0; specificity, 0.93) when compared with pathology. CONCLUSIONS: This pilot single-institution experience of FDG-PET/MRI for preoperative staging of MIBC performed similar to CT for the detection of the primary tumor; however, the determination of lymph node status was limited by few patients with true pathologic lymph node involvement. Further studies are needed to evaluate the potential role for FDG-PET/MRI in the staging of MIBC.

20.
BMC Genomics ; 21(1): 108, 2020 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-32005147

RESUMO

BACKGROUND: Siberian musk deer, one of the seven species, is distributed in coniferous forests of Asia. Worldwide, the population size of Siberian musk deer is threatened by severe illegal poaching for commercially valuable musk and meat, habitat losses, and forest fire. At present, this species is categorized as Vulnerable on the IUCN Red List. However, the genetic information of Siberian musk deer is largely unexplored. RESULTS: Here, we produced 3.10 Gb draft assembly of wild Siberian musk deer with a contig N50 of 29,145 bp and a scaffold N50 of 7,955,248 bp. We annotated 19,363 protein-coding genes and estimated 44.44% of the genome to be repetitive. Our phylogenetic analysis reveals that wild Siberian musk deer is closer to Bovidae than to Cervidae. Comparative analyses showed that the genetic features of Siberian musk deer adapted in cold and high-altitude environments. We sequenced two additional genomes of Siberian musk deer constructed demographic history indicated that changes in effective population size corresponded with recent glacial epochs. Finally, we identified several candidate genes that may play a role in the musk secretion based on transcriptome analysis. CONCLUSIONS: Here, we present a high-quality draft genome of wild Siberian musk deer, which will provide a valuable genetic resource for further investigations of this economically important musk deer.


Assuntos
Mapeamento de Sequências Contíguas/veterinária , Cervos/genética , Perfilação da Expressão Gênica/veterinária , Sequenciamento Completo do Genoma/veterinária , Adaptação Biológica , Animais , Cervos/classificação , Evolução Molecular , Feminino , Tamanho do Genoma , Anotação de Sequência Molecular , Filogenia , Densidade Demográfica , Análise de Sequência de RNA/veterinária
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