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#32746493   2020/08/03 To Up

The association between Epstein-Barr virus and oral lichen planus: A systematic review and meta-analysis.

Oral lichen planus (OLP) is a relatively common inflammatory disease, with unclear etiology. A number of studies have linked Epstein-Barr virus (EBV) with OLP. The present systematic review and meta-analysis aimed to evaluate the available evidence regarding the potential association between EBV and OLP.
Sajna Ashraf, Sadeq Ali Al-Maweri, Nader Alaizari, Ayesha Umair, Zaihan Ariffin, Mohammed Nasser Alhajj, Saba Kassim, Kamran Awan

1030 related Products with: The association between Epstein-Barr virus and oral lichen planus: A systematic review and meta-analysis.

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#32746479   2020/08/03 To Up

Analytical Method Development and Validation of UV-visible Spectrophotometric Method for the Estimation of Vildagliptin in Gastric Medium.

Vildagliptin is an antidiabetic agent, belongs to the dipeptidyl peptidase IV (DPP-4) inhibitors.
Beena Kumari, Aparna Khansili

1361 related Products with: Analytical Method Development and Validation of UV-visible Spectrophotometric Method for the Estimation of Vildagliptin in Gastric Medium.

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#32746391   // To Up

Development and Validation of the Bladder and Bowel Incontinence Phobia Severity Scale.

The current research investigates the development and validation of the Bladder and Bowel Incontinence Phobia Severity Scale (BBIPSS). Over two studies, two independent samples consisting of university students and respondents from the general public were used to validate the scale (study 1 = 226; study 2 = 377). A 15-item, two-factor model was confirmed in study 2 where strong construct (convergent and divergent) validity was demonstrated. The BBIPSS did not display significant correlations with openness and gender (divergent validity) and displayed significant correlations with depression, anxiety, and stress scores (DASS), alongside paruresis and parcopresis scores (Shy Bladder and Bowel Scale [SBBS]; convergent validity) and the Bowel and Bladder-Control Anxiety Scale [BoBCAtS]. The BBIPSS also demonstrated strong test-retest reliability (bladder = 0.89; bowel = 0.86) in a small sample of adults ( = 13). Overall, this scale provides researchers and clinicians with a reliable and psychometrically valid assessment tool to measure bladder and bowel incontinence phobia severity.
Kenley L J Kuoch, Denny Meyer, David W Austin, Simon R Knowles

1080 related Products with: Development and Validation of the Bladder and Bowel Incontinence Phobia Severity Scale.

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#32746294   2020/06/16 To Up

Model-based Sensitivity Analysis of EMG Clustering Index with Respect to Motor Unit Properties: Investigating Post-stroke FDI Muscle.

The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight types in three degrees of MU alterations. Specifically, the CI decision exhibited an abnormally increased pattern for five types, an abnormally decreased pattern for two types, and an invariant pattern for one type. Furthermore, the CI diagnostic decision was found to be highly sensitive to three types because a 50% degree of alteration in these types resulted in a distinct deviation of 2.5 in the CI Z-score. The mixed CI patterns were confirmed in experimental data collected from the paretic muscles of 14 subjects with stroke, as compared to the healthy muscles of 10 control subjects. Given the simulation results as a guideline, the CI diagnostic decision could be interpreted from general neural or muscular changes into specific MU changes (in eight types). This can further promote clinical applications of the convenient surface EMG tool in examining and monitoring paretic muscle changes toward customized stroke rehabilitation.
Xu Zhang, Xiao Tang, Zhongqing Wei, Xiang Chen, Xun Chen

2660 related Products with: Model-based Sensitivity Analysis of EMG Clustering Index with Respect to Motor Unit Properties: Investigating Post-stroke FDI Muscle.

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#32746263   2020/06/15 To Up

Augmenting Perceived Softness of Haptic Proxy Objects through Transient Vibration and Visuo-Haptic Illusion in Virtual Reality.

In this work, we investigate the effects of active transient vibration and visuo-haptic illusion to augment the perceived softness of haptic proxy objects. We introduce a system combining active transient vibration at the fingertip with visuo-haptic illusions. In our hand-held device, a voice coil actuator transmits active transient vibrations to the index fingertip, while a force sensor measures the force applied on passive proxy objects to create visuo-haptic illusions in virtual reality. We conducted three user studies to understand both the vibrotactile effect and its combined effect with visuo-haptic illusions. A preliminary study confirmed that active transient vibrations can intuitively alter the perceived softness of a proxy object. Our first study demonstrated that those same active transient vibrations can generate different perceptions of softness depending on the material of the proxy object used. In our second study, we evaluated the combination of active transient vibration and visuo-haptic illusion, and found that both significantly influence perceived softness, with with the visuo-haptic effect being dominant. Our third study further investigated the vibrotactile effect while controlling for the visuo-haptic illusion. The combination of these two methods allows users to effectively perceive various levels of softness when interacting with haptic proxy objects.
Inrak Choi, Evan Zhao, Eric J Gonzalez, Sean Follmer

2612 related Products with: Augmenting Perceived Softness of Haptic Proxy Objects through Transient Vibration and Visuo-Haptic Illusion in Virtual Reality.

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#32746222   2020/07/24 To Up

Doppler Passive Acoustic Mapping.

In therapeutic ultrasound using microbubbles, it is essential to drive the microbubbles into the correct type of activity and the correct location to produce the desired biological response. Although passive acoustic mapping (PAM) is capable of locating where microbubble activities are generated, it is well known that microbubbles move rapidly within the ultrasound beam. We propose a technique that can image microbubble movement by estimating their velocities within the focal volume. Microbubbles embedded within a wall-less channel of a tissue-mimicking material were sonicated using 1-MHz focused ultrasound. The acoustic emissions generated by the microbubbles were captured with a linear array (L7-4). PAM with robust Capon beamforming was used to localize the microbubble acoustic emissions. We spectrally analyzed the time trace of each position and isolated the higher harmonics. Microbubble velocity maps were constructed from the position-dependent Doppler shifts at different time points during sonication. Microbubbles moved primarily away from the transducer at velocities on the order of 1 m/s due to primary acoustic radiation forces, producing a time-dependent velocity distribution. We detected microbubble motion both away and towards the receiving array, revealing the influence of acoustic radiation forces and fluid motion due to the ultrasound exposure. High-speed optical images confirmed the acoustically-measured microbubble velocities. Doppler PAM enables passive estimation of microbubble motion and may be useful in therapeutic applications, such as drug delivery across the blood-brain barrier, sonoporation, sonothrombolysis and drug release.
Antonios N Pouliopoulos, Cameron A B Smith, James H Bezer, Ahmed El Ghamrawy, Krit Sujarittam, Charlotte J Bouldin, Sophie V Morse, Meng-Xing Tang, James J Choi

2617 related Products with: Doppler Passive Acoustic Mapping.



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#32746111   2020/06/11 To Up

Deep Learning Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.
Ivan Olefir, Stratis Tzoumas, Courtney Restivo, Pouyan Mohajerani, Lei Xing, Vasilis Ntziachristos

2423 related Products with: Deep Learning Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

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#32746105   2020/06/05 To Up

Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal.

Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm suffers from so-called conebeam artifacts as the cone angle increases. Various model-based iterative reconstruction methods have been developed to reduce the cone-beam artifacts, but these algorithms usually require multiple applications of computational expensive forward and backprojections. In this paper, we develop a novel deep learning approach for accurate conebeam artifact removal. In particular, our deep network, designed on the differentiated backprojection domain, performs a data-driven inversion of an ill-posed deconvolution problem associated with the Hilbert transform. The reconstruction results along the coronal and sagittal directions are then combined using a spectral blending technique to minimize the spectral leakage. Experimental results under various conditions confirmed that our method generalizes well and outperforms the existing iterative methods despite significantly reduced runtime complexity.
Yoseob Han, Junyoung Kim, Jong Chul Ye

1620 related Products with: Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal.

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#32746033   2020/06/22 To Up

Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy.

The aim of this work is to propose a new machine-learning based model to improve the calculation of mealtime insulin boluses (MIB) in type 1 diabetes (T1D) therapy using continuous glucose monitoring (CGM) data. Indeed, MIB is still often calculated with the standard formula (SF), which does not account for glucose rate-of-change (ROC), causing critical hypo/hyperglycemic episodes.
Giulia Noaro, Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Simone Del Favero, Andrea Facchinetti

2395 related Products with: Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy.

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#32745961   2020/07/31 To Up

Successful application of anti-CD19 CAR-T therapy with IL-6 knocking down to patients with central nervous system B-cell acute lymphocytic leukemia.

Few studies have described chimeric antigen receptor-modified T cell (CAR-T) therapy for central nervous system (CNS) B-cell acute lymphocytic leukemia (B-ALL) patients due to life-threatening CAR-T-related encephalopathy (CRES) safety issues. In this study, CAR-Ts targeting CD19 with short hairpin RNA (shRNA)-IL-6 gene silencing technology (ssCART-19s) were prepared. We conducted a phase 1 clinical trial (ClinicalTrials.gov number, NCT03064269). Three patients with relapsed CNS B-ALL were enrolled, conditioned with the fludarabine and cyclophosphamide for lymphocyte depletion and infused with ssCART-19s for three consecutive days. Clinical symptoms and laboratory examinations were monitored. After ssCART-19 treatment, three patients' symptoms resolved almost entirely. Brain leukemic infiltration reduced significantly based on magnetic resonance imaging (MRI), and there were no leukemic blasts in cerebrospinal fluid (CSF), which was confirmed by cytological and molecular examinations. Additionally, increases in the levels of cytokines and immune cells were observed in the CSF of all patients. Only grade 1 cytokine release syndrome (CRS) manifesting as fever was noted in patients. In conclusion, CAR-Ts with shRNA-IL-6 gene knockdown migrated into the CNS, eradicated leukemic cells and elevated cytokines in CSF with mild, acceptable side effects.
Li-Yun Chen, Li-Qing Kang, Hai-Xia Zhou, Han-Qing Gao, Xue-Fei Zhu, Nan Xu, Lei Yu, De-Pei Wu, Sheng-Li Xue, Ai-Ning Sun

2489 related Products with: Successful application of anti-CD19 CAR-T therapy with IL-6 knocking down to patients with central nervous system B-cell acute lymphocytic leukemia.

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