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ObjectiveTo investigate the effects of probiotics combined with bismuth quadruple therapy （BQT） on clinical efficacy、gastrointestinal adverse reactions and intestinal flora in Helicobacter pylori （HP） positive patients.MethodsThe patients who were positive for HP from May 2023 to July 2023 in the department of gastroenterology of Shanghai first people's hospital were randomly divided into 2 groups with 40 people in each group. The probiotic group was given 2 weeks of quadruple therapy with probiotics and standard BQT， followed by 4 weeks of oral probiotics after quadruple discontinuation. The placebo group was given 2 weeks of probiotic placebo and standard BQT， followed by 4 weeks of oral probiotic placebo. 13C urea breath test was used to evaluate the clinical efficacy， gastrointestinal symptoms rating Scale was used to evaluate the gastrointestinal adverse reactions of patients before and after the intervention， and microbial diversity 16S rDNA sequencing technology was used to detect the level of intestinal flora of patients before and after the intervention.ResultsThere was no significant difference in the eradication rate between the two groups （P>0.05）. Before the intervention， there was no significant difference in the scores of the gastrointestinal symptom rating scale between the probiotic group and the placebo group. After the intervention， patients in the probiotic group had significantly lower pain scores on acid reflux （1.10±0.30 vs 1.35±0.53， P

Objective A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction. Methods Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent. Results In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively. Conclusion Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.

As magnetic field strength in Magnetic Resonance Imaging (MRI) technology increases, maintaining the specific absorption rate (SAR) within safe limits across human head tissues becomes challenging due to the formation of standing waves at a shortened wavelength. Compounding this challenge is the uncertainty in the dielectric properties of head tissues, which notably affects the SAR induced by the radiofrequency (RF) coils in an ultra-high-field (UHF) MRI system. To this end, this study introduces a computational framework to quantify the impacts of uncertainties in head tissues’ dielectric properties on the induced SAR. The framework employs a surrogate model-assisted Monte Carlo (MC) technique, efficiently generating surrogate models of MRI observables (electric fields and SAR) and utilizing them to compute SAR statistics. Particularly, the framework leverages a high-dimensional model representation technique, which constructs the surrogate models of the MRI observables via univariate and bivariate component functions, approximated through generalized polynomial chaos expansions. The numerical results demonstrate the efficiency of the proposed technique, requiring significantly fewer deterministic simulations compared with traditional MC methods and other surrogate model-assisted MC techniques utilizing machine learning algorithms, all while maintaining high accuracy in SAR statistics. Specifically, the proposed framework constructs surrogate models of a local SAR with an average relative error of 0.28% using 289 simulations, outperforming the machine learning-based surrogate modeling techniques considered in this study. Furthermore, the SAR statistics obtained by the proposed framework reveal fluctuations of up to 30% in SAR values within specific head regions. These findings highlight the critical importance of considering dielectric property uncertainties to ensure MRI safety, particularly in 7 T MRI systems.

This work highlights the radiation-induced syntheses of numerous semiconductor nanomaterials, including metal oxides, metal sulfides, metal selenides, polymers, surface precious metal nanoparticles deposition, and vacancy-defect semiconductors. The morphology, size, uniformity, and surface properties of semiconductor nanomaterials are controlled through parameters such as the dissolved oxygen content, pH, stabilizers, complexing agents, absorbed dose, and the dose rate.

In a telerehabilitation environment, it is difficult for a therapist to understand the condition of a patient’s finger joints because of the lack of direct assessment. In particular, not enabling the provision of spasticity evaluation significantly reduces the optimal performance of telerehabilitation. In a previous study, it has been proposed that finger stiffness could be estimated using an analytical model of a whole-finger soft actuator. However, because the whole-finger soft actuators require high air pressure for high bending performance and are costly to customize for each patient, using joint modular soft actuators for telerehabilitation turns to be a necessity, though stiffness estimation with joint modular soft actuators has not been studied yet. Another problem is caused by using a marker-based joint angle measurement, which requires the markers to be attached to the exact positions, and limits its application in telerehabilitation. In this study, we proposed a procedure of finger joint stiffness estimation that combines information acquired from a joint modular soft actuator and a marker-less hand joint position acquisition device. Correction parameters were added to the previous analytical model for -the bending analysis of a joint assisted using a joint modular soft actuator. Moreover, a multi-variate regression model was implemented for correcting joint angles obtained from the hand joint position acquisition device. As a result, a reasonable accuracy of stiffness estimation was achieved for rehabilitation with the joint modular soft actuators, which suggests the possibility of using the proposed method to evaluate the finger spasticity in a telerehabilitation environment. This is a big step forward towards optimal hand telerehabilitation.

Classification of pain levels from evoked electroencephalography (eEEG) has been achieved for the purpose of objective assessment of pain. However, differentiation of acute and chronic pain resulting from the activation of nociceptive nerves, the Aδ and C fibers, respectively, has remained unsolved, mainly due to the lack of effective features. Granger causality (GC) was applied to show the role of specific brain areas in the differentiation between pain and tactile sensation but yet was used to differentiate nociceptive fiber activations. The purpose of this study is two-fold: first, to develop pain perception classification systems using GC as features, and second, to identify causal connectivity for effective differentiation of nociceptive fiber activations. The eEEG responding to three stimulation waveforms, 1 Hz square waves, 5 Hz sine waves, both targeting C fibers, and 250 Hz sine waves, targeting Aδ, were recorded. Three GC categories based on channel areas, frequency bands, and stimulation waveforms were computed from the eEEG and used as features to classify nociceptive fiber activations and pain levels. It was found that GCs in the alpha band produced the highest average classification accuracy of 99.71%, followed by the gamma band, with 98.66%. Meanwhile, the GC feature contributing most to differentiating Aδ and C was found in the alpha band. The GC flow computed from the results of 5 Hz and 250 Hz stimulation had an important role in the prediction of mild pain and maximum pain, respectively. These results demonstrated the high potential of GC as features of pain-related information.

It has been reported that stimulating nociceptive unmyelinated C nerves (C) near the sarcolemma could induce the secretion of endogenous opioids that relieve chronic pain. However, a substantial concern remains: concomitant stimulation might cause acute pain to nociceptive myelinated Aδ nerves (Aδ), which generally have a lower activation threshold than the C. However, few studies have reported on C selectivity over Aδ (C-selectivity). In this study, the C and Aδ nerves were modeled using the Hodgkin-Huxley (HH) and McIntyre-Richardson-Grill (MRG) models, respectively. Two potential stimulation schemes, including bipolar square waves and burst modulated alternating current, together with a new stimulation scheme named inhibit-Aδ (i-Aδ) that inhibits the excitability of theAδ, were systematically investigated. Their stimuli were given to the C and Aδ nerves through point electrodes located near the nerve fibers. Simulation results revealed that i-Aδ presented the highest C-selectivity, which provides a basis for non-invasive effective chronic-pain relief.

Increased density of printed circuit board assembles (PCBAs) is diminishing physical contact test access of the in-circuit testing (ICT) solutions for defects detection. As a result, it is necessary to design contactless test solutions with high spatial resolution to overcome the current test limitations. In this paper, a compact contactless sensor is presented for monitoring the fabrication quality of PCBAs. The sensor consists of an inductor-capacitor-(LC)-based resonant circuit and it can detect defects on metallic traces of PCBAs in close proximity without a contact. The sensor is designed such that, strong electric field is confined around the sensor tip, which creates strong capacitive coupling between the sensor tip and devices under test (DUTs) within a small region for sensing, i.e. high resolution sensing. The sensitivity of the proposed sensor is verified by the simulated and experimental 2D surface mapping results with sample DUTs, and it shows that the sensor can detect the open or short defects with a size as small as 0.15 mm × 0.2 mm. Circuit models are proposed to predict the sensing behavior and for guiding the design. The proposed sensor shows great potential to be used in the real-time monitoring of defects along the manufacturing supply chain.

Electrical properties (EPs) of tissues facilitate early detection of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively probe the EPs of tissues from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) to solve partial differential Equations (PDEs) to reconstruct the EPs. However, they are not practical for clinical data because ND is noise sensitive and the MRI measurements for MREPT are noisy in nature. Recently, Physics informed neural networks (PINNs) have been introduced to solve PDEs by substituting ND with automatic differentiation (AD). To the best of our knowledge, it has not been applied to MREPT due to the challenges in using PINN on MREPT as (i) a PINN requires part of ground-truth EPs as collocation points to optimize the network’s AD, (ii) the noisy input data disrupts the optimization of PINNs despite the noise-filtering nature of NNs and additional denoising processes. In this work, we propose a PINN-MREPT model based on a canonical analytic MREPT model. A reference padding layer with known EPs was added to surround the region of interest for providing additive collocation points. Moreover, an optimizable diffusion coefficient was embedded in the analytic MREPT model used in the PINN-MREPT. The noise robustness of the proposed PINN-MREPT for single-sample reconstruction was tested by using numerical phantoms of human brain with extra tumor-like tissues at different noise levels. The results of numerical experiments show that PINN-MREPT outperforms two typical numerical MREPT methods in terms of reconstruction accuracy, sensitivity to the extra tissues, and the correlations of line profiles in the regions of interest. The advantage of the PINN-MREPT is shown by the results of an experiment on phantom measurement, too. Moreover, it is found that the diffusion term plays an important role to achieve a noise-robust PINN-MREPT. This is an important step moving forward to a clinical application of MREPT.

We present a design and the optimization of an irregular-shaped inward-outward (IO) ring-pair magnet array that generates a 1D monotonic field pattern for 2D head imaging in a low-field portable magnetic resonance imaging (MRI) system. The magnet rings are discretized into fan-shaped ring segments with varying outer radii for the design and optimization. Besides, the inner radii of ring-pairs are tapered from outside in to provide the controlled field inhomogeneity. Genetic algorithm (GA) was used, and a current model for a fan-shaped ring segment was derived to have a fast forward calculation in the optimization. A monotonic field pattern is successfully obtained along the x-direction in a cylindrical field of view (FoV), with a relatively strong magnetic field (132.98 mT) and the homogeneity of 151840 ppm. The proposed array was further evaluated by applying its field as a spatial encoding magnetic field (SEM) for imaging by using simulation. Due to the field monotonicity, the reconstructed image by applying the fields of the proposed array shows clearer features (a higher structural similarity index) with a reduced error rate compared to that using a sparse dipolar Halbach array. The proposed magnet array is a promising alternative to supply SEM for imaging in a permanent-magnet-based low-field portable MRI system.

Low-cost and portable magnetic resonance imaging (MRI) may make this imaging modality more accessible. Permanent-magnet-array is an option to supply a static magnetic field (B-field) with portability, low cost, and no power consumption. However, it has low field strength. Moreover, it does not have linear gradients, thus the signals and the images are not linked by the Fourier transformation as they are in a conventional system. The B-field generated by an array and called spatial-encoding-magnetic-field (SEM), is spatially non-linear and always on. Such an SEM, in terms of the field strength, direction, homogeneity, pattern and its field pattern variation, is related to the image quality. This relation is crucial because it can be used to guide the magnet and system design for high image quality and portability. However, it has not been systematically studied. In this paper, the characteristics of the SEMs from different magnet array designs are identified. Due to the non-linearity of the SEMs, local structural similarity (SSIM) index is proposed to evaluate the region-dependent image quality, and local k-space is applied to analyze the region-dependent effects of these SEMs on image reconstruction. Moreover, point spread function is applied to analyze the overall effect of the SEMs on the quality of reconstructed images. Besides the intrinsic effects of the SEMs, those of the external factors, e.g. the receive coil sensitivity, are analyzed. This study identifies the unique characteristics of the SEMs in a permanent-magnet-array-based MRI system, and offers methods to analyze the unique relation between the image quality and the field. It can not only guide the magnet designs but also trigger more design ideas, e.g., the design of the mechanical movement of the magnet array, and that of the static magnetic field shimming coils, paving the way towards a low-field MRI system with practical portability.

In magnetic resonance imaging (MRI), several studies have demonstrated that the metamaterial-based structures can effectively improve the sensitivity, and thus the signal-to-noise ratio (SNR), of receiving radio-frequency (RF) coils. However, the use of metamaterials for this type of the MRI application is often limited due to the bulkiness of the metamaterial structure at RF wavelengths and a lack of frequency tunability of the final design. In this work, we propose a planar compact sub-wavelength (1- field at the depth of 30mm into a load experimentally. Even at a penetration depth as much as 60 mm (deep brain in the case of head imaging), an enhancement of 9% was observed. Moreover, the magnetic field enhancement comes with a decrease (10%) in specific absorption rate (SAR). In terms of tuning, by controlling the water level in the cavity, the proposed spiral resonator shows a wide tuning range of 35MHz, centered around 64 MHz, with high tunability sensitivity (2.4-0.75 MHz/ml or 15-4.8 MHz/mm), which is due to the fact that the tuning cavity is located between the two spirals, where the fields are highly confined.

IoT devices have complex requirements but their limitations in terms of storage, network, computing, data analytics, scalability, and big data management require it to be used it with a technology like cloud computing. IoT backend with cloud computing can present new ways to offer services that are massively scalable, can be dynamically configured, and delivered on demand with large scale infrastructure resources. However, a single cloud infrastructure might be unable to deal with the increasing demand of cloud services in which hundreds of users might be accessing cloud resources, leading to a big data problem and the need for efficient frameworks to handle a large number of user requests for IoT services. These challenges require new functional elements and provisioning schemes. To this end, we propose the usage of multi-clouds with IoT which can optimize the user requirements by allowing them to choose best IoT services from many services hosted in various cloud platforms and provide them with more infrastructure and platform resources to meet their requirements. This paper presents a novel framework for dynamic and secure IoT services access across multi-clouds using the cloud on-demand model. To facilitate multi-cloud collaboration, novel protocols are designed and implemented on cloud platforms. The various stages involved in the framework for allowing users access to IoT services in multi-clouds are service matchmaking (i.e., to choose the best service matching user requirements), authentication (i.e., a lightweight mechanism to authenticate users at runtime before granting them service access), and SLA management (including, SLA negotiation, enforcement, and monitoring). SLA management offers benefits like negotiating required service parameters, enforcing mechanisms to ensure that service execution in the external cloud is according to the agreed SLAs and monitoring to verify that the cloud provider complies with those SLAs. The detailed system design to establish secure multi-cloud collaboration has been presented. Moreover, the designed protocols are empirically implemented on two different clouds, including OpenStack and Amazon AWS. Experiments indicate that the proposed system is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, and any SLA violation by a cloud provider could be recorded and reported back to the user.

The advancements in wireless communication impose a growing range of demands on the antennas performance, requiring multiple functionalities to be present in a single device. To satisfy these different application needs within a limited space, reconfigurable antennas are often used which are able to switch between a number of states, providing multiple functions using a single antenna. Electronic switching components, such as PIN diodes, radio-frequency micromechanical systems (RF-MEMS), and varactors, are typically used to achieve antenna reconfiguration. However, some of these approaches have certain limitations, such as narrow bandwidth, complex biasing circuitry, and high activation voltages. In recent years, an alternative approach using liquid dielectric materials for antenna reconfiguration has drawn significant attention. The intrinsic conformability of liquid dielectric materials allows us to realize antennas with desired reconfigurations with different physical constraints while maintaining high radiation efficiency. The purpose of this review is to summarize different approaches proposed in the literature for the liquid dielectric reconfigurable antennas. It facilitates the understanding of the advantages and limitations of this technology, and it helps to draw general design principals for the development of reconfigurable antennas in this category.

Point-of-care magnetic resonance imaging (MRI) requires clear images within a short scanning time, a small footprint of the scanner, and relatively low memory required for image reconstruction. A permanent magnet array (PMA)-based MRI system is a good candidate to supply a magnetic field due to its compactness and low power consumption. However, it has relatively inhomogeneous magnetic field and thus non-linear gradients, which results in location-dependent k-spaces (so called local k-spaces) and uneven signal point populations in the local k-spaces, compromising the image quality. Moreover, owing to the non-linearity, imaging reconstruction using Fourier transform does not work, which leads to an increase in the required computation memory. In this study, in order to improve the image quality, the approaches of compensating the uneven signal point population by increasing the numbers of sampling points or rotation angles are investigated in terms of their impacts on image quality improvement, acquisition time, image reconstruction time, and memory consumption. Both methods give a significant improvement on image image quality although they result in a large and dense encoding matrix and thus a large memory consumption. To lower the memory consumption, it is further proposed to transform such a matrix to frequency domain where the matrix could be sparse. Moreover, a row-wise truncation to the transformed encoding matrix is applied to further reduce the memory consumption. Through the results of numerical experiments, it is shown that the required memory for calculation can effectively be reduced by 71.6% while the image becomes clearer by increasing the number of sampling point and/or the number of rotation angles. With the successful demonstration where improved image quality and a lowered memory required can be obtained simultaneously, the proposed study is one step forward for a PMA-based MRI system towards its targeted point-of-care application scenario.

Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi’s fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.

We present an analytical study on the generation of broadband electromagnetic noise in solids as a consequence of variations in the dielectric constant under the impact of polarization induced by nonequilibrium thermodynamic fluctuations. The analysis leads to a specific formulation of the fluctuation dissipation theorem in the context of dielectric materials having finite electrodynamic boundary conditions, which drive energy into the system, under feedback, during its under interaction with a heat bath. The ensuing spectral symmetry breaking of the broadband noise yields bursts of narrowband signals, which can potentially result in phase transitions and dielectric breakdown. This study sheds a new light on high temperature precision calorimetry, while also improving our understanding of unexpected breakdowns in devices like CMOS components, capacitors and batteries.