Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities.

Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition.

Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the type of input features. Although deep learning models for EEG-based emotion recognition can deliver superior accuracy, it comes at the cost of high computational complexity. Here, we propose a novel 3D convolutional neural network with a channel bottleneck module (CNN-BN) model for EEG-based emotion recognition, with the aim of accelerating the CNN computation without a significant loss in classification accuracy. To this end, we constructed a 3D spatiotemporal representation of EEG signals as the input of our proposed model. Our CNN-BN model extracts spatiotemporal EEG features, which effectively utilize the spatial and temporal information in EEG. We evaluated the performance of the CNN-BN model in the valence and arousal classification tasks. Our proposed CNN-BN model achieved an average accuracy of 99.1% and 99.5% for valence and arousal, respectively, on the DEAP dataset, while significantly reducing the number of parameters by 93.08% and FLOPs by 94.94%. The CNN-BN model with fewer parameters based on 3D EEG spatiotemporal representation outperforms the state-of-the-art models. Our proposed CNN-BN model with a better parameter efficiency has excellent potential for accelerating CNN-based emotion recognition without losing classification performance.

Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.

Object Manipulation with an Anthropomorphic Robotic Hand via Deep Reinforcement Learning with a Synergy Space of Natural Hand Poses.

Anthropomorphic robotic hands are designed to attain dexterous movements and flexibility much like human hands. Achieving human-like object manipulation remains a challenge especially due to the control complexity of the anthropomorphic robotic hand with a high degree of freedom. In this work, we propose a deep reinforcement learning (DRL) to train a policy using a synergy space for generating natural grasping and relocation of variously shaped objects using an anthropomorphic robotic hand. A synergy space is created using a continuous normalizing flow network with point clouds of haptic areas, representing natural hand poses obtained from human grasping demonstrations. The DRL policy accesses the synergistic representation and derives natural hand poses through a deep regressor for object grasping and relocation tasks. Our proposed synergy-based DRL achieves an average success rate of 88.38% for the object manipulation tasks, while the standard DRL without synergy space only achieves 50.66%. Qualitative results show the proposed synergy-based DRL policy produces human-like finger placements over the surface of each object including apple, banana, flashlight, camera, lightbulb, and hammer.

Hand Gesture Recognition Using Single Patchable Six-Axis Inertial Measurement Unit via Recurrent Neural Networks.

Recording human gestures from a wearable sensor produces valuable information to implement control gestures or in healthcare services. The wearable sensor is required to be small and easily worn. Advances in miniaturized sensor and materials research produces patchable inertial measurement units (IMUs). In this paper, a hand gesture recognition system using a single patchable six-axis IMU attached at the wrist via recurrent neural networks (RNN) is presented. The IMU comprises IC-based electronic components on a stretchable, adhesive substrate with serpentine-structured interconnections. The proposed patchable IMU with soft form-factors can be worn in close contact with the human body, comfortably adapting to skin deformations. Thus, signal distortion (i.e., motion artifacts) produced for vibration during the motion is minimized. Also, our patchable IMU has a wireless communication (i.e., Bluetooth) module to continuously send the sensed signals to any processing device. Our hand gesture recognition system was evaluated, attaching the proposed patchable six-axis IMU on the right wrist of five people to recognize three hand gestures using two models based on recurrent neural nets. The RNN-based models are trained and validated using a public database. The preliminary results show that our proposed patchable IMU have potential to continuously monitor people's motions in remote settings for applications in mobile health, human-computer interaction, and control gestures recognition.

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. To segment breast lesions, full resolution convolutional network (FrCN), a novel segmentation model of deep network, is implemented and used. Finally, three conventional deep learning models including regular feedforward CNN, ResNet-50, and InceptionResNet-V2 are separately adopted and used to classify or recognize the detected and segmented breast lesion as either benign or malignant. To evaluate the integrated CAD system for detection, segmentation, and classification, the publicly available and annotated INbreast database is used over fivefold cross-validation tests. The evaluation results of the YOLO-based detection achieved detection accuracy of 97.27%, Matthews's correlation coefficient (MCC) of 93.93%, and F1-score of 98.02%. Moreover, the results of the breast lesion segmentation via FrCN achieved an overall accuracy of 92.97%, MCC of 85.93%, Dice (F1-score) of 92.69%, and Jaccard similarity coefficient of 86.37%. The detected and segmented breast lesions are classified via CNN, ResNet-50, and InceptionResNet-V2 achieving an average overall accuracies of 88.74%, 92.56%, and 95.32%, respectively. The performance evaluation results through all stages of detection, segmentation, and classification show that the integrated CAD system outperforms the latest conventional deep learning methodologies. We conclude that our CAD system could be used to assist radiologists over all stages of detection, segmentation, and classification for diagnosis of breast lesions.

Development of a Novel Multi-Channel Thermocouple Array Sensor for In-Situ Monitoring of Ice Accretion.

A test was performed to determine the efficacy of a novel multi-channel thermocouple temperature sensor employing "N+1" array architecture for the in-situ detection of icing in cold climates. T-type thermoelements were used to fabricate a sensor with six independent temperature sensing points, capable of two-dimensional temperature mapping. The sensor was intended to detect the high latent heat of fusion of water (334 J/g) which is released to the environment during ice formation. The sensor was embedded on a plywood board and an aluminium plate, respectively by an epoxy resin. Three different ice accretion cases were considered. Ice accretion for all cases was achieved on the surface of the resin layer. In order to analyse the temperature variation for all three cases, the first 20 s response for each case was averaged between three cases. A temperature increase of (1.0 ± 0.1) °C and (0.9 ± 0.1) °C was detected by the sensors 20 s after the onset of icing, attributed to the latent heat of fusion of water. The results indicate that the sensor design is well-suited to cold temperature applications and that detection of the latent heat of fusion could provide a rapid and robust means of icing detection.

Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks.

PURPOSE: To develop and evaluate a method of parallel imaging time-of-flight (TOF) MRA using deep multistream convolutional neural networks (CNNs). METHODS: A deep parallel imaging network ("DPI-net") was developed to reconstruct 3D multichannel MRA from undersampled data. It comprises 2 deep-learning networks: a network of multistream CNNs for extracting feature maps of multichannel images and a network of reconstruction CNNs for reconstructing images from the multistream network output feature maps. The images were evaluated using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) values, and the visibility of blood vessels was assessed by measuring the vessel sharpness of middle and posterior cerebral arteries on axial maximum intensity projection (MIP) images. Vessel sharpness was compared using paired t tests, between DPI-net, 2 conventional parallel imaging methods (SAKE and ESPIRiT), and a deep-learning method (U-net). RESULTS: DPI-net showed superior performance in reconstructing vessel signals in both axial slices and MIP images for all reduction factors. This was supported by the quantitative metrics, with DPI-net showing the lowest NRMSE, the highest PSNR and SSIM (except R = 3.8 on sagittal MIP images, and R = 5.7 on axial slices and sagittal MIP images), and significantly higher vessel sharpness values than the other methods. CONCLUSION: DPI-net was effective in reconstructing 3D TOF MRA from highly undersampled multichannel MR data, achieving superior performance, both quantitatively and qualitatively, over conventional parallel imaging and other deep-learning methods.

Automatic hip geometric feature extraction in DXA imaging using regional random forest.

BACKGROUND: Hip fracture is considered one of the salient disability factors across the global population. People with hip fractures are prone to become permanently disabled or die from complications. Although currently the premier determiner, bone mineral density has some notable limitations in terms of hip fracture risk assessment. OBJECTIVES: To learn more about bone strength, hip geometric features (HGFs) can be collected. However, organizing a hip fracture risk study for a large population using a manual HGFs collection technique would be too arduous to be practical. Thus, an automatic HGFs extraction technique is needed. METHOD: This paper presents an automated HGFs extraction technique using regional random forest. Regional random forest localizes landmark points from femur DXA images using local constraints of hip anatomy. The local region constraints make random forest robust to noise and increase its performance because it processes the least number of points and patches. RESULTS: The proposed system achieved an overall accuracy of 96.22% and 95.87% on phantom data and real human scanned data respectively. CONCLUSION: The proposed technique's ability to measure HGFs could be useful in research on the cause and facts of hip fracture and could help in the development of new guidelines for hip fracture risk assessment in the future. The technique will reduce workload and improve the use of X-ray devices.

KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

PURPOSE: To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a deep CNN operating on an image domain (ICNN), and (3) an interleaved data consistency operations. These components are alternately applied, and each CNN is trained to minimize the loss between the reconstructed and corresponding fully sampled k-spaces. The final reconstructed image is obtained by forward-propagating the undersampled k-space data through the entire network. RESULTS: Performances of K-net (KCNN with inverse Fourier transform), I-net (ICNN with interleaved data consistency), and various combinations of the 2 different networks were tested. The test results indicated that K-net and I-net have different advantages/disadvantages in terms of tissue-structure restoration. Consequently, the combination of K-net and I-net is superior to single-domain CNNs. Three MR data sets, the T2 fluid-attenuated inversion recovery (T2 FLAIR) set from the Alzheimer's Disease Neuroimaging Initiative and 2 data sets acquired at our local institute (T2 FLAIR and T1 weighted), were used to evaluate the performance of 7 conventional reconstruction algorithms and the proposed cross-domain CNNs, which hereafter is referred to as KIKI-net. KIKI-net outperforms conventional algorithms with mean improvements of 2.29 dB in peak SNR and 0.031 in structure similarity. CONCLUSION: KIKI-net exhibits superior performance over state-of-the-art conventional algorithms in terms of restoring tissue structures and removing aliasing artifacts. The results demonstrate that KIKI-net is applicable up to a reduction factor of 3 to 4 based on variable-density Cartesian undersampling.

Treatment of infra-isthmal femoral fracture with an intramedullary nail: Is retrograde nailing a better option than antegrade nailing?

INTRODUCTION: Antegrade intramedullary (IM) nailing is ideal for femoral shaft fractures, but fixing the fracture distal to the isthmal level may be difficult because of medullary canal widening and the proximity of fracture location from the distal femoral joint line. This study aimed to compare treatment results between antegrade and retrograde nailing for infra-isthmal femoral shaft fracture, and to identify influencing factors of nonunion and malalignment. MATERIALS AND METHODS: Sixty patients with infra-isthmal femoral shaft fractures treated with IM nailing and followed-up for > 1 year were enrolled in this retrospective study, 38 in the antegrade nailing group, and 22 in the retrograde nailing group. The two groups had no significant differences in age, sex, and fracture location (p = 0.297, Mann-Whitney test). Radiological evaluation was performed, and functional result was assessed using the Knee Society scoring system. Complications were analyzed in accordance with fracture location, fracture type, and operative method. RESULTS: According to the AO/OTA classification, 35, 16, and 9 cases were type A (A1: 1, A2: 11, A3: 23), B (B1: 2, B2: 7, B3: 7), and C fractures (C2: 4, C3: 5), respectively. The mean follow-up duration was 29.5 months. In the antegrade and retrograde nailing groups, the primary bony union rates were 73.7% in 20.7 weeks (range 12-41) and 86.4% in 17.4 weeks (range 12-30), respectively. The two groups showed no significant differences in union rate (p = 0.251, Pearson's Chi-square test) and union time (p = 0.897, Mann-Whitney test). No cases of malalignment of > 10° in any plane were found in both groups. The mean Knee Society scores were 92 (range 84-100) and 91 (range 83-95) in the antegrade and retrograde nailing groups, respectively, showing no significant difference (p = 0.297, Pearson's Chi-square test). Although fracture location was not significantly related to union rate (p = 0.584, Mann-Whitney test), patients with an effective working length of the distal segment of < 0.75 were prone to nonunion (p = 0.003, Pearson's Chi-square test). CONCLUSIONS: Although no significant difference was found in IM nail type, the IM nail with a shorter working length distal to the fracture showed a strong relationship with nonunion.

Femur segmentation in DXA imaging using a machine learning decision tree.

BACKGROUND: Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorption. Femur segmentation remains a challenge as many existing methods fail to correctly distinguish femur from soft tissue. Reasons for this failure include low contrast and noise in DXA images, bone shape variability, and inconsistent X-ray beam penetration and attenuation, which cause shadowing effects and person-to-person variation. OBJECTIVE: To present a new method namely, a Pixel Label Decision Tree (PLDT), and test whether it can achieve higher accurate performance in femur segmentation in DXA imaging. METHODS: PLDT involves mainly feature extraction and selection. Unlike photographic images, X-ray images include features on the surface and inside an object. In order to reveal hidden patterns in DXA images, PLDT generates seven new feature maps from existing high energy (HE) and low energy (LE) X-ray features and determines the best feature set for the model. The performance of PLDT in femur segmentation is compared with that of three widely used medical image segmentation algorithms, the Global Threshold (GT), Region Growing Threshold (RGT), and artificial neural networks (ANN). RESULTS: PLDT achieved a higher accuracy of femur segmentation in DXA imaging (91.4%) than either GT (68.4%), RGT (76%) or ANN (84.4%). CONCLUSIONS: The study demonstrated that PLDT outperformed other conventional segmentation techniques in segmenting DXA images. Improved segmentation should help accurate computation of BMD which later improves clinical diagnosis of osteoporosis.

Denoising images of dual energy X-ray absorptiometry using non-local means filters.

BACKGROUND: In general, the image quality of high and low energy images of dual energy X-ray absorptiometry (DXA) suffers from noise due to the use of a small amount of X-rays. Denoising of DXA images could be a key process to improve a bone mineral density map, which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures and osteoporosis. OBJECTIVE: This study aims to develop and test a new technology to improve the quality, remove the noise, and preserve the edges and fine details of real DXA images. METHODS: In this study, a denoising technique for high and low energy DXA images using a non-local mean filter (NLM) was presented. The source and detector noises of a DXA system were modeled for both high and low DXA images. Then, the optimized parameters of the NLM filter were derived utilizing the experimental data from CIRS-BFP phantoms. After that, the optimized NLM was tested and verified using the DXA images of the phantoms and real human spine and femur. RESULTS: Quantitative evaluation of the results showed average 24.22% and 34.43% improvement of the signal-to-noise ratio for real high and low spine images, respectively, while the improvements were about 15.26% and 13.55% for the high and low images of the femur. The qualitative visual observations of both phantom and real structures also showed significantly improved quality and reduced noise while preserving the edges in both high and low energy images. Our results demonstrate that the proposed NLM outperforms the conventional method using an anisotropic diffusion filter (ADF) and median techniques for all phantom and real human DXA images. CONCLUSIONS: Our work suggests that denoising via NLM could be a key preprocessing method for clinical DXA imaging.

Prolonged stimulation with low-intensity ultrasound induces delayed increases in spontaneous hippocampal culture spiking activity.

Ultrasound is a promising neural stimulation modality, but an incomplete understanding of its range and mechanism of effect limits its therapeutic application. We investigated the modulation of spontaneous hippocampal spike activity by ultrasound at a lower acoustic intensity and longer time scale than has been previously attempted, hypothesizing that spiking would change conditionally upon the availability of glutamate receptors. Using a 60-channel multielectrode array (MEA), we measured spontaneous spiking across organotypic rat hippocampal slice cultures (N = 28) for 3 min each before, during, and after stimulation with low-intensity unfocused pulsed or sham ultrasound (spatial-peak pulse average intensity 780 µW/cm2 ) preperfused with artificial cerebrospinal fluid, 300 µM kynurenic acid (KA), or 0.5 µM tetrodotoxin (TTX) at 3 ml/min. Spike rates were normalized and compared across stimulation type and period, subregion, threshold level, and/or perfusion condition using repeated-measures ANOVA and generalized linear mixed models. Normalized 3-min spike counts for large but not midsized, small, or total spikes increased after but not during ultrasound relative to sham stimulation. This result was recapitulated in subregions CA1 and dentate gyrus and replicated in a separate experiment for all spike size groups in slices pretreated with aCSF but not KA or TTX. Increases in normalized 18-sec total, midsized, and large spike counts peaked predominantly 1.5 min following ultrasound stimulation. Our low-intensity ultrasound setup exerted delayed glutamate receptor-dependent, amplitude- and possibly region-specific influences on spontaneous spike rates across the hippocampus, expanding the range of known parameters at which ultrasound may be used for neural activity modulation. © 2016 Wiley Periodicals, Inc.

High-SNR multiple T2 (*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics.

PURPOSE: To develop an effective method that can suppress noise in successive multiecho T2 (*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. MATERIALS AND METHODS: For the simulation experiments, we used multiple T2 -weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2 -difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. RESULTS: Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. CONCLUSION: This study demonstrates that high-SNR multiple T2 (*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2 J. MAGN. RESON. IMAGING 2017;45:1835-1845.

Robust Protection against Highly Virulent Foot-and-Mouth Disease Virus in Swine by Combination Treatment with Recombinant Adenoviruses Expressing Porcine Alpha and Gamma Interferons and Multiple Small Interfering RNAs.

UNLABELLED: Because the currently available vaccines against foot-and-mouth disease (FMD) provide no protection until 4 to 7 days postvaccination, the only alternative method to halt the spread of the FMD virus (FMDV) during outbreaks is the application of antiviral agents. Combination treatment strategies have been used to enhance the efficacy of antiviral agents, and such strategies may be advantageous in overcoming viral mechanisms of resistance to antiviral treatments. We have developed recombinant adenoviruses (Ads) for the simultaneous expression of porcine alpha and gamma interferons (Ad-porcine IFN-αγ) as well as 3 small interfering RNAs (Ad-3siRNA) targeting FMDV mRNAs encoding nonstructural proteins. The antiviral effects of Ad-porcine IFN-αγ and Ad-3siRNA expression were tested in combination in porcine cells, suckling mice, and swine. We observed enhanced antiviral effects in porcine cells and mice as well as robust protection against the highly pathogenic strain O/Andong/SKR/2010 and increased expression of cytokines in swine following combination treatment. In addition, we showed that combination treatment was effective against all serotypes of FMDV. Therefore, we suggest that the combined treatment with Ad-porcine IFN-αγ and Ad-3siRNA may offer fast-acting antiviral protection and be used with a vaccine during the period that the vaccine does not provide protection against FMD. IMPORTANCE: The use of current foot-and-mouth disease (FMD) vaccines to induce rapid protection provides limited effectiveness because the protection does not become effective until a minimum of 4 days after vaccination. Therefore, during outbreaks antiviral agents remain the only available treatment to confer rapid protection and reduce the spread of foot-and-mouth disease virus (FMDV) in livestock until vaccine-induced protective immunity can become effective. Interferons (IFNs) and small interfering RNAs (siRNAs) have been reported to be effective antiviral agents against FMDV, although the virus has associated mechanisms of resistance to type I interferons and siRNAs. We have developed recombinant adenoviruses for the simultaneous expression of porcine alpha and gamma interferons (Ad-porcine IFN-αγ) as well as 3 small interfering RNAs (Ad-3siRNA) to enhance the inhibitory effects of these antiviral agents observed in previous studies. Here, we show enhanced antiviral effects against FMDV by combination treatment with Ad-porcine IFN-αγ and Ad-3siRNA to overcome the mechanisms of resistance of FMDV in swine.

Improved biomolecular detection based on a plasmonic nanoporous gold film fabricated by oblique angle deposition.

We demonstrated an enhanced surface plasmon resonance (SPR) detection by incorporating a nanoporous gold film on a thin gold substrate. Nanoscale control of thickness and roughness of the nanoporous layer was successfully accomplished by oblique angle deposition. In biosensing experiments, the results obtained by biotin-streptavidin interaction showed that SPR samples with a nanoporous gold layer provided a notable sensitivity improvement compared to a conventional bare gold film, which is attributed to an excitation of local plasmon field and an increased surface reaction area. Imaging sensitivity enhancement factor was employed to estimate an overall sensor performance of the fabricated samples and an optimal SPR structure was determined. Our approach is intended to show the feasibility and extend the applicability of the nanoporous gold film-mediated SPR biosensor to diverse biomolecular binding events.

Fucoidan from Fucus vesiculosus protects against alcohol-induced liver damage by modulating inflammatory mediators in mice and HepG2 cells.

Fucoidan is an l-fucose-enriched sulfated polysaccharide isolated from brown algae and marine invertebrates. In this study, we investigated the protective effect of fucoidan from Fucus vesiculosus on alcohol-induced murine liver damage. Liver injury was induced by oral administration of 25% alcohol with or without fucoidan (30 mg/kg or 60 mg/kg) for seven days. Alcohol administration increased serum aspartate aminotransferase and alanine aminotransferase levels, but these increases were suppressed by the treatment of fucoidan. Transforming growth factor beta 1 (TGF-ß1), a liver fibrosis-inducing factor, was highly expressed in the alcohol-fed group and human hepatoma HepG2 cell; however, the increase in TGF-ß1 expression was reduced following fucoidan administration. Treatment with fucoidan was also found to significantly reduce the production of inflammation-promoting cyclooygenase-2 and nitric oxide, while markedly increasing the expression of the hepatoprotective enzyme, hemeoxygenase-1, on murine liver and HepG2 cells. Taken together, the antifibrotic and anti-inflammatory effects of fucoidan on alcohol-induced liver damage may provide valuable insights into developing new therapeutics or interventions.

Regional electric field induced by electroconvulsive therapy in a realistic finite element head model: influence of white matter anisotropic conductivity.

We present the first computational study investigating the electric field (E-field) strength generated by various electroconvulsive therapy (ECT) electrode configurations in specific brain regions of interest (ROIs) that have putative roles in the therapeutic action and/or adverse side effects of ECT. This study also characterizes the impact of the white matter (WM) conductivity anisotropy on the E-field distribution. A finite element head model incorporating tissue heterogeneity and WM anisotropic conductivity was constructed based on structural magnetic resonance imaging (MRI) and diffusion tensor MRI data. We computed the spatial E-field distributions generated by three standard ECT electrode placements including bilateral (BL), bifrontal (BF), and right unilateral (RUL) and an investigational electrode configuration for focal electrically administered seizure therapy (FEAST). The key results are that (1) the median E-field strength over the whole brain is 3.9, 1.5, 2.3, and 2.6 V/cm for the BL, BF, RUL, and FEAST electrode configurations, respectively, which coupled with the broad spread of the BL E-field suggests a biophysical basis for observations of superior efficacy of BL ECT compared to BF and RUL ECT; (2) in the hippocampi, BL ECT produces a median E-field of 4.8 V/cm that is 1.5-2.8 times stronger than that for the other electrode configurations, consistent with the more pronounced amnestic effects of BL ECT; and (3) neglecting the WM conductivity anisotropy results in E-field strength error up to 18% overall and up to 39% in specific ROIs, motivating the inclusion of the WM conductivity anisotropy in accurate head models. This computational study demonstrates how the realistic finite element head model incorporating tissue conductivity anisotropy provides quantitative insight into the biophysics of ECT, which may shed light on the differential clinical outcomes seen with various forms of ECT, and may guide the development of novel stimulation paradigms with improved risk/benefit ratio.