A compact motorized end-effector for ankle rehabilitation training.

This paper introduces a compact end-effector ankle rehabilitation robot (CEARR) system for addressing ankle range of motion (ROM) rehabilitation. The CEARR features a bilaterally symmetrical rehabilitation structure, with each side possessing three degrees of freedom (DOF) driven by three independently designed actuators. The working intervals of each actuator are separated by a series connection, ensuring they operate without interference to accommodate the dorsiflexion/plantarflexion (DO/PL), inversion/eversion (IN/EV), and adduction/abduction (AD/AB) DOF requirements for comprehensive ankle rehabilitation. In addition, we integrated an actuator and foldable brackets to accommodate patients in varied postures. We decoded the motor intention based on the surface electromyography (sEMG) and torque signals generated by the subjects' ankle joints in voluntary rehabilitation. Besides, we designed a real-time voluntary-triggered control (VTC) strategy to enhance the rehabilitation effect, in which the root mean square (RMS) of sEMG was utilized to trigger and adjust the CEARR rehabilitation velocity support. We verified the consistency of voluntary movement with CEARR rehabilitation support output for four healthy subjects on a nonlinear sEMG signal with an R 2 metric of approximately 0.67. We tested the consistency of triggering velocity trends with a linear torque signal for one healthy individual with an R 2 metric of approximately 0.99.

Semi-supervised segmentation of metastasis lesions in bone scan images.

To develop a deep image segmentation model that automatically identifies and delineates lesions of skeletal metastasis in bone scan images, facilitating clinical diagnosis of lung cancer-caused bone metastasis by nuclear medicine physicians. A semi-supervised segmentation model is proposed, comprising the feature extraction subtask and pixel classification subtask. During the feature extraction stage, cascaded layers which include the dilated residual convolution, inception connection, and feature aggregation learn the hierarchal representations of low-resolution bone scan images. During the pixel classification stage, each pixel is first classified into categories in a semi-supervised manner, and the boundary of pixels belonging to an individual lesion is then delineated using a closed curve. Experimental evaluation conducted on 2,280 augmented samples (112 original images) demonstrates that the proposed model performs well for automated segmentation of metastasis lesions, with a score of 0.692 for DSC if the model is trained using 37% of the labeled samples. The self-defined semi-supervised segmentation model can be utilized as an automated clinical tool to detect and delineate metastasis lesions in bone scan images, using only a few manually labeled image samples. Nuclear medicine physicians need only attend to those segmented lesions while ignoring the background when they diagnose bone metastasis using low-resolution images. More images of patients from multiple centers are typically needed to further improve the scalability and performance of the model via mitigating the impacts of variability in size, shape, and intensity of bone metastasis lesions.

Application of ultrasound treatment for improving the quality of infant meat puree.

Infant meat puree has an indispensable effect on the oral development and nutritional intake of infants. However, commercially available products have poor texture and relatively low digestibility. In this study, ultrasound (20 kHz and 200 W, 400 W, or 600 W) was applied to the pretreatment of raw meat for preparing infant meat puree for 15 min, 30 min, and 45 min. To assess the impact of ultrasound on infant meat puree, the viscosity, texture, water distribution, particle size and in vitro digestibility were determined. The results showed that, compared with control, viscosity and hardness of meat puree decreased and the texture was better in 400 W and 600 W groups. The content of immobilized water increased in comparison with the control. Ultrasound had no obvious effect on the digestibility of meat puree in gastric phase, but it increased the digestibility in intestinal phase with the highest digestibility (80.85%±3.33) in 600 W, 15 min group. Overall, the ultrasound parameters of 600 W for 15 min can be selected as the best condition to process infant meat puree. The findings provide a new perspective for the improvement of infant meat puree.

Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images.

SPECT imaging has been identified as an effective medical modality for diagnosis, treatment, evaluation and prevention of a range of serious diseases and medical conditions. Bone SPECT scan has the potential to provide more accurate assessment of disease stage and severity. Segmenting hotspot in bone SPECT images plays a crucial role to calculate metrics like tumor uptake and metabolic tumor burden. Deep learning techniques especially the convolutional neural networks have been widely exploited for reliable segmentation of hotspots or lesions, organs and tissues in the traditional structural medical images (i.e., CT and MRI) due to their ability of automatically learning the features from images in an optimal way. In order to segment hotspots in bone SPECT images for automatic assessment of metastasis, in this work, we develop several deep learning based segmentation models. Specifically, each original whole-body bone SPECT image is processed to extract the thorax area, followed by image mirror, translation and rotation operations, which augments the original dataset. We then build segmentation models based on two commonly-used famous deep networks including U-Net and Mask R-CNN by fine-tuning their structures. Experimental evaluation conducted on a group of real-world bone SEPCT images reveals that the built segmentation models are workable on identifying and segmenting hotspots of metastasis in bone SEPCT images, achieving a value of 0.9920, 0.7721, 0.6788 and 0.6103 for PA (accuracy), CPA (precision), Rec (recall) and IoU, respectively. Finally, we conclude that the deep learning technology have the huge potential to identify and segment hotspots in bone SPECT images.

The eukaryotic translation initiation factor 3 subunit E binds to classical swine fever virus NS5A and facilitates viral replication.

Classical swine fever virus (CSFV) NS5A protein is a multifunctional protein, playing critical roles in viral RNA replication, translation and assembly. To further explore its functions in viral replication, interaction of NS5A with host factors was assayed using a his-tag "pull down" assay coupled with shotgun LC-MS/MS. Host protein translation initiation factor 3 subunit E was identified as a binding partner of NS5A, and confirmed by co-immunoprecipitation and co-localization analysis. Overexpression of eIF3E markedly enhanced CSFV genomic replication, viral protein expression and production of progeny virus, and downregulation of eIF3E by siRNA significantly decreased viral proliferation in PK-15 cells. Luciferase reporter assay showed an enhancement of translational activity of the internal ribosome entry site of CSFV by eIF3E and a decrease in cellular translation by NS5A. These data indicate that eIF3E plays an important role in CSFV replication, thereby identifying it as a potential target for inhibition of the virus.

Cellular Hsp27 interacts with classical swine fever virus NS5A protein and negatively regulates viral replication by the NF-κB signaling pathway.

Classical swine fever virus (CSFV) nonstructural protein NS5A is a multifunctional protein functioning in regulation of viral genome replication, protein translation and assembly by interaction with viral or host proteins. Here, heat shock protein 27 (Hsp27) has been identified as a novel binding partner of NS5A by using His tag "pull down" coupled with shotgun LC-MS/MS, with interaction of both proteins further confirmed by co-immunoprecipitation and laser confocal assays. In PK-15 cells, silencing of Hsp27 expression by siRNA enhanced CSFV replication, and upregulation of Hsp27 inhibited viral proliferation. Additionally, we have shown that overexpression of Hsp27 increased NF-κB signaling induced by TNFα. Blocking NF-κB signaling in PK-15 cells overexpressing Hsp27 by ammonium pyrrolidinedithiocarbamate (PDTC) eliminated the inhibition of CSFV replication by Hsp27. These findings clearly demonstrate that the inhibition of CSFV replication by Hsp27 is mediated via the NF-κB signaling pathway.