Wongdelacruz7835

78×0.78mm; FOV 200mm; FA 180 degrees; PB 230Hz/pixel) and a MESE T2* mapping sequence (TR/TE 873/3.82-19.1ms (5 echoes); VS 3×0.625×0.625mm; FOV 160mm; FA 25 degrees; PB 250Hz/pixel).

Automated cartilage segmentation and quantitative analysis provided T2 and T2* data from test-retest MR examinations to assess immediate reliability.

Coefficient of variation (CV) and intraclass correlations (ICC

) to analyse automated T2 and T2* mapping reliability focusing on the clinically important superior cartilage regions of the hip joint.

Comparisons between test-retest T2 and (T2*) data revealed mean CV's of 3.385%(1.25%), mean ICC

's of 0.871 (0.984) and median mean differences of -1.139ms (+0.195ms).

The T2 and T2* times from automated analyses of hip cartilage from test-retest MR examinations had high (T2) and excellent (T2*) immediate reliability.

The T2 and T2* times from automated analyses of hip cartilage from test-retest MR examinations had high (T2) and excellent (T2*) immediate reliability.

To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hold (MBH) T2WI.

One hundred and fifty-two adult patients with suspected liver disease were prospectively enrolled. Two independent readers reviewed images acquired with conventional MBH-T2WI and SBH-T2WI at 3.0T MR scanner. For image quality analyses, motion artifacts scores and boundary sharpness scores were compared using nonparametric Wilcoxon matched pairs tests between MBH-T2WI and SBH-T2WI. With the reference standard, 89 patients with 376 index lesions were included for lesion analyses. The lesion detection rates were compared by chi-square test, the lesion conspicuity scores and lesion-liver contrast ratio (CR) were compared using nonparametric Wilcoxon matched pairs tests between the two sequences.

For both readers, motion artifacts scores of SBH-T2WI were signing based reconstruction showed promising performance as it provided significantly better image quality, lesion detectability, lesion conspicuity and contrast within a single breath-hold, compared with the conventional MBH-T2WI.MVI is a risk assessment factor related to hepatocellular carcinoma (HCC) recurrence after hepatectomy or liver transplantation. The goal of this paper is to study the preoperative diagnosis of microvascular invasion (MVI) by using a deep learning algorithm in non-contrast T2 weighted magnetic resonance imaging (MRI) images instead of pathological images. Herein, an ensemble learning algorithm named H-DARnet-based on the difference degree and attention mechanism, combined with radiomics, for MVI prediction-is proposed. Our hybrid network combines the fine-grained, high-level semantic, and radiomics features and exhibits a rich multilevel-feature architecture composed of global-local-prior knowledge with suitable complementarity. The total loss function comprises two regularization items--the triplet and the cross-entropy loss function--which are selected for the triplet network and SE-DenseNet, respectively. The hard triplet sample selection strategy for a triplet network and data augmentation for small-scale liver image datasets in convolutional neural network (CNN) training is indispensable. For 200 patch level test samples (135 positive samples and 65 negative samples), our method can obtain the best prediction results, the AUC, sensitivity, and specificity were 0.826, 79.5% and 73.8%, respectively. The experiment results show that MVI can be predicted by using MRI images, and the proposed method is better than other deep learning algorithms and hand-crafted feature algorithms. The proposed ensemble learning algorithm is proved to be an effective method for MVI prediction.

To develop and validate an accelerated free-breathing 3D whole-heart magnetic resonance angiography (MRA) technique using a radial k-space trajectory with compressed sensing and curvelet transform.

A 3D radial phyllotaxis trajectory was implemented to traverse the centerline of k-space immediately before the segmented whole-heart MRA data acquisition at each cardiac cycle. Selleck Hydroxyfasudil The k-space centerlines were used to correct the respiratory-induced heart motion in the acquired MRA data. The corrected MRA data were then reconstructed by a novel compressed sensing algorithm using curvelets as the sparsifying domain. The proposed 3D whole-heart MRA technique (radial CS curvelet) was then prospectively validated against compressed sensing with a conventional wavelet transform (radial CS wavelet) and a standard Cartesian acquisition in terms of scan time and border sharpness.

Fifteen patients (females 10, median age 34-year-old) underwent 3D whole-heart MRA imaging using a standard Cartesian trajectory and our propoe includes additional clinical trials and extending this technique to 3D cine imaging.

In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates.

21 healthy controls (age 32±13years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistrhe method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.Pulmonary microvascular barrier dysfunction is a hallmark feature of acute lung injury (ALI). IQGAP1 is a ubiquitously expressed scaffolding protein known to regulate cancer metastasis, angiogenesis, and barrier stability. However, the function of IQGAP1 in lipopolysaccharide (LPS)-induced microvascular endothelial hyperpermeability remains poorly understood. In the present study, we demonstrated that IQGAP1 was markedly upregulated in LPS-induced ALI models and rat pulmonary microvascular endothelial cells (RPMVECs). Lentivirus-mediated knockdown of IQGAP1 significantly attenuated the formation of actin stress fibers, phosphorylation of myosin light chain (MLC), and disruption of VE-cadherin, thereby protecting the RPMVECs barrier failure from LPS damage. In addition, IQGAP1 depletion reduced the reactive oxygen species (ROS)-mediated increase in intracellular adhesion molecule-1 (ICAM-1) in RPMVECs stimulated with LPS. Mechanistically, we found that the upregulation of IQGAP1 affected the activity of Rap1 and the downstream phosphorylation of Src. In conclusion, these findings reveal an essential mechanism by which increased IQGAP1 in LPS-treated RPMVECs promotes barrier dysfunction and ICAM-1 upregulation, at least in part by regulating Rap1/Src signalling, indicating that IQGAP1 may be a potential therapeutic target to prevent endothelial hyperpermeability and inflammation in ALI.From Egyptian mummies to the Chanel n°5 perfume, fatty aldehydes have long been used and keep impacting our senses in a wide range of foods, beverages and perfumes. Natural sources of fatty aldehydes are threatened by qualitative and quantitative variability while traditional chemical routes are insufficient to answer the society shift toward more sustainable and natural products. The production of fatty aldehydes using biotechnologies is therefore the most promising alternative for the flavors and fragrances industry. In this review, after drawing the portrait of the origin and characteristics of fragrant fatty aldehydes, we present the three main classes of enzymes that catalyze the reaction of fatty alcohols oxidation into aldehydes, namely alcohol dehydrogenases, flavin-dependent alcohol oxidases and copper radical alcohol oxidases. The constraints, challenges and opportunities to implement these oxidative enzymes in the flavors and fragrances industry are then discussed. By setting the scene on the biocatalytic production of fatty aldehydes, and providing a critical assessment of its potential, we expect this review to contribute to the development of biotechnology-based solutions in the flavors and fragrances industry.Starch debranching enzymes (SDBEs) hydrolyze the α-1,6 glycosidic bonds in polysaccharides such as starch, amylopectin, pullulan and glycogen. SDBEs are also important enzymes for the preparation of sugar syrup, resistant starch and cyclodextrin. As the synergistic catalysis of SDBEs and other starch-acting hydrolases can effectively improve the raw material utilization and production efficiency during starch processing steps such as saccharification and modification, they have attracted substantial research interest in the past decades. The substrate specificities of the two major members of SDBEs, pullulanases and isoamylases, are quite different. Pullulanases generally require at least two α-1,4 linked glucose units existing on both sugar chains linked by the α-1,6 bond, while isoamylases require at least three units of α-1,4 linked glucose. SDBEs mainly belong to glycoside hydrolase (GH) family 13 and 57. Except for GH57 type II pullulanse, GH13 pullulanases and isoamylases share plenty of similarities in sequence and structure of the core catalytic domains. However, the N-terminal domains, which might be one of the determinants contributing to the substrate binding of SDBEs, are distinct in different enzymes. In order to overcome the current defects of SDBEs in catalytic efficiency, thermostability and expression level, great efforts have been made to develop effective enzyme engineering and fermentation strategies. Herein, the diverse biochemical properties and distinct features in the sequence and structure of pullulanase and isoamylase from different sources are summarized. Up-to-date developments in the enzyme engineering, heterologous production and industrial applications of SDBEs is also reviewed. Finally, research perspective which could help understanding and broadening the applications of SDBEs are provided.