Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN.

Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

Biofeedback Respiratory Rehabilitation Training System Based on Virtual Reality Technology.

Traditional respiratory rehabilitation training fails to achieve visualization and quantification of respiratory data in improving problems such as decreased lung function and dyspnea in people with respiratory disorders, and the respiratory rehabilitation training process is simple and boring. Therefore, this article designs a biofeedback respiratory rehabilitation training system based on virtual reality technology. It collects respiratory data through a respiratory sensor and preprocesses it. At the same time, it combines the biofeedback respiratory rehabilitation training virtual scene to realize the interaction between respiratory data and virtual scenes. This drives changes in the virtual scene, and finally the respiratory data are fed back to the patient in a visual form to evaluate the improvement of the patient's lung function. This paper conducted an experiment with 10 participants to evaluate the system from two aspects: training effectiveness and user experience. The results show that this system has significantly improved the patient's lung function. Compared with traditional training methods, the respiratory data are quantified and visualized, the rehabilitation training effect is better, and the training process is more active and interesting.

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review.

Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.

Identification of Denatured Biological Tissues Based on Compressed Sensing and Improved Multiscale Dispersion Entropy during HIFU Treatment.

Identification of denatured biological tissue is crucial to high-intensity focused ultrasound (HIFU) treatment, which can monitor HIFU treatment and improve treatment efficiency. In this paper, a novel method based on compressed sensing (CS) and improved multiscale dispersion entropy (IMDE) is proposed to evaluate the complexity of ultrasonic scattered echo signals during HIFU treatment. In the analysis of CS, the method of orthogonal matching pursuit (OMP) is employed to reconstruct the denoised signal. CS-OMP can denoise the ultrasonic scattered echo signal effectively. Comparing with traditional multiscale dispersion entropy (MDE), IMDE improves the coarse-grained process in the multiscale analysis, which improves the stability of MDE. In the analysis of simulated signals, the entropy value of the IMDE method has less fluctuation compared with MDE, indicating that the IMDE method has better stability. In addition, MDE and IMDE are applied to the 300 cases of ultrasonic scattered echo signals after denoising (including 150 cases of normal tissues and 150 cases of denatured tissues). The experimental results show that the MDE and IMDE values of denatured tissues are higher than normal tissues. Both the MDE and IMDE method can be used to identify whether biological tissue is denatured. However, the multiscale entropy curve of IMDE is smoother and more stable than MDE. The interclass distance of IMDE is greater than MDE, and the intraclass distance of IMDE is less than MDE at different scale factors. This indicates that IMDE can better distinguish normal tissues and denatured tissues to obtain more accurate clinical diagnosis during HIFU treatment.

Improved breast ultrasound tumor classification using dual-input CNN with GAP-guided attention loss.

Ultrasonography is a widely used medical imaging technique for detecting breast cancer. While manual diagnostic methods are subject to variability and time-consuming, computer-aided diagnostic (CAD) methods have proven to be more efficient. However, current CAD approaches neglect the impact of noise and artifacts on the accuracy of image analysis. To enhance the precision of breast ultrasound image analysis for identifying tissues, organs and lesions, we propose a novel approach for improved tumor classification through a dual-input model and global average pooling (GAP)-guided attention loss function. Our approach leverages a convolutional neural network with transformer architecture and modifies the single-input model for dual-input. This technique employs a fusion module and GAP operation-guided attention loss function simultaneously to supervise the extraction of effective features from the target region and mitigate the effect of information loss or redundancy on misclassification. Our proposed method has three key features: (i) ResNet and MobileViT are combined to enhance local and global information extraction. In addition, a dual-input channel is designed to include both attention images and original breast ultrasound images, mitigating the impact of noise and artifacts in ultrasound images. (ii) A fusion module and GAP operation-guided attention loss function are proposed to improve the fusion of dual-channel feature information, as well as supervise and constrain the weight of the attention mechanism on the fused focus region. (iii) Using the collected uterine fibroid ultrasound dataset to train ResNet18 and load the pre-trained weights, our experiments on the BUSI and BUSC public datasets demonstrate that the proposed method outperforms some state-of-the-art methods. The code will be publicly released at https://github.com/425877/Improved-Breast-Ultrasound-Tumor-Classification.

Physiological evaluation of a new Chlorella sorokiniana isolate for its biomass production and lipid accumulation in photoautotrophic and heterotrophic cultures.

A novel green unicellular microalgal isolate from the freshwater of the Inner Mongolia Province of China and named as CCTCC M209220, grows between pH 6 and 11 and temperatures of 20-35°C with optimal conditions at pH 9 and 30°C. Morphological features and the phylogenetic analysis for the 18S rRNA gene reveal that the isolate is a Chlorella sorokiniana strain. A nitrogen source test reveals that this strain can grow well with nitrate and urea, but not ammonium. The strain can grow heterotrophically with glucose as the carbon source and accumulates lipid content as high as 56% (w/w) dry weight after 7 days in high glucose concentrations compared to 19% lipids achieved in 30 days of photoautotrophic culture. The relative neutral lipid content as a fraction of the total lipid is also much higher in heterotrophic culture as compared to photoautotrophic culture.

User experience and usability when the automated driving system fails: Findings from a field experiment.

We are entering an era of automated vehicles (AVs), which has potential to improve road safety considerably. A compelling user experience is crucial to AV adoption in the future commercial market. The automated driving system (ADS) that replaces human drivers should be perceived as very useful before the latter are willing to give up their control and entrust their lives to the ADS. However, compared with the growing number of studies on public acceptance of AVs, there has been limited research focusing on user experience and usability. We examined AV and ADS user experience and usability, ADS failures' influence on them, and their influences on re-riding willingness. We conducted a field study using a real AV and a large-scale test track. We invited participants (N = 261) to travel in the AV as passengers in a low-speed environment. Participants were randomly assigned into the normal condition or the fault condition (its participants were exposed to an ADS failure). We measured participants' positive experience (feeling relaxed, safe, and comfortable) and negative experience (feeling tense and risky) while riding in the AV and perceived usability of the ADS based on the System Usability Scale. In both conditions, participants reported moderate positive experience and perceived usability but a relatively high level of willingness to ride in our AV again. The ADS failure reduced positive experience and perceived usability, and it increased negative experience. Positive experience and perceived usability, but not negative experience, influenced re-riding willingness. Compared with male participants, female participants reported less positive experience and lower perceived usability. We discuss implications of our results as well as limitations of this research.

Acupuncture Can Regulate the Distribution of Lymphocyte Subsets and the Levels of Inflammatory Cytokines in Patients With Mild to Moderate Vascular Dementia.

Background: Vascular dementia (VD) is the second most common type of dementia after Alzheimer's disease, but there is a lack of definitive treatment for VD. Acupuncture treatment is effective in improving the cognitive impairment and behavioral capacity of patients with VD. In recent years, more studies indicated that peripheral inflammation and abnormal peripheral immune function may aggravate neuroinflammation and cognitive dysfunction. However, there are few studies about the acupuncture and the abnormal peripheral immune function of VD. Also, few studies concern the regulating effect of acupuncture on peripheral immunity of patients with VD. Objective: The aim of this study was to explore the effect of the "sanjiao" acupuncture method on peripheral immunity of patients with mild to moderate VD. Methods: A total of 30 patients with VD were involved in the acupuncture group (AG), which was treated with the "sanjiao" acupuncture method once a day for six times a week and lasted for 12 weeks, and 30 healthy elderly people were assigned to the normal group (NG), which had no treatment. The distribution of lymphocyte subsets and the levels of some inflammatory cytokines in the peripheral blood of subjects were evaluated using the flow cytometry (FCM) and the enzyme-linked immunosorbent assay (ELISA). Results: A total of 60 subjects were involved in this study, while 58 subjects completed the entire trial. Before treatment, the levels of CD3+ T, CD4+ T cells, CD4+/CD8+, Tregs, B cells, IFN-γ, and IL-10 in patients with VD were significantly decreased compared with the normal group (all P < 0.05 or P < 0.01). The level of TNF-α in peripheral blood of patients with VD was significantly increased (P < 0.01). After acupuncture treatment, the levels of CD3+ T, CD4+ T cells, and IFN-γ were significantly increased (all P < 0.05 or P < 0.01). The level of TNF-α was significantly decreased (P < 0.01). The proportion of Tregs was increased (P < 0.01), but it was still lower than that of the normal group (P < 0.05). Conclusion: The acupuncture method can increase the proportion of CD3+, CD4+ T cells, and Tregs in peripheral blood of patients with VD. And, it reduces the levels of pro-inflammatory factor TNF-α, which achieves the anti-inflammatory effects and immunostimulation. It suggests that acupuncture can improve the peripheral immune dysfunction of patients with VD by regulating the distribution of lymphocyte subsets and the levels of inflammatory cytokines. Clinical Trial Registration: [www.chictr.org.cn], identifier [ChiCTR-IOR-17012052].

Acupuncture Can Regulate the Peripheral Immune Cell Spectrum and Inflammatory Environment of the Vascular Dementia Rat, and Improve the Cognitive Dysfunction of the Rats.

OBJECTIVE: The aim of this study is to analyze the effects of acupuncture on peripheral immune function, inflammation, and cognitive impairment in vascular dementia (VD) rats. METHODS: In this study, 2-month-old healthy male Wistar rats (260-280 g) were assigned to the groups as follows: normal group (Gn, n = 10), sham-operated group (Gs, n = 10), and operated group (Go, n = 45). The Go group was established by permanent, bilateral common carotid artery occlusion (BCCAO). Two months after operation, the operated rats were screened by hidden platform trial and the rats with cognitive dysfunction were further randomly divided into impaired group (Gi), acupoint group (Ga), and non-acupoint group (Gna) with 10 rats in each group. The Ga group was given acupuncture treatment for 14 days with a rest for every 7 days. After treatment, the Morris water maze (MWM) test was performed to evaluate the spatial learning and memory abilities of rats. The lymphocyte subsets in peripheral blood and spleen of rats were measured by flow cytometry. The levels of cytokines [i.e., interleukin (IL)-1ß, IL-2, IL-4, IL-10, tumor necrosis factor-α (TNF-α), and interferon-γ (INF-γ)], chemokines (i.e., macrophage inflammatory protein-2 (MIP-2)), and other inflammatory mediators (i.e., cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS)) in peripheral blood and hippocampus were measured by enzyme linked immunosorbent assay (ELISA). RESULTS: Compared with the Gn group, the Gi rats presented long escape latencies to find the platform. After acupuncture treatment, the escape latencies of the Ga group were rescued markedly when compared with the Gi group (P < 0.05). The proportion of CD4 + T lymphocytes in both spleen and peripheral blood in the Ga group increased (P < 0.05) in comparison with the Gi group. There is an obvious reduction in IL-1ß (P < 0.05), IL-2 (P < 0.05), TNF-α (P < 0.01), INF-γ (P < 0.01), MIP-2 (P < 0.05), and iNOS (P < 0.01), coming along with the increased levels of IL-4 and IL-10 (P < 0.01) in the Ga group when compared with the Gi group. In addition, the hippocampus proinflammatory factors IL-1ß (P < 0.01), IL-2 (P < 0.01), TNF-α (P < 0.05), INF-γ (P < 0.05), MIP-2 (P < 0.05), iNOS (P < 0.01), and COX-2 decreased in the Ga group, whereas the anti-inflammatory factors IL-4 and IL-10 (P < 0.01) increased. CONCLUSION: There are abnormal immune function and peripheral inflammation in VD rats. Acupuncture can regulate the peripheral immune function and inflammation of the VD rats and can improve the cognitive dysfunction of the rats.

Dexamethasone does not ameliorate gliosis in a mouse model of neurodegenerative disease.

Prolonged neuroinflammation is a driving force for neurodegenerative disease, and agents against inflammatory responses are regarded as potential treatment strategies. Here we aimed to evaluate the prevention effects on gliosis by dexamethasone (DEX), an anti-inflammation drug. We used DEX to treat the nicastrin conditional knockout (cKO) mouse, a neurodegenerative mouse model. DEX (10 mg/kg) was given to 2.5-month-old nicastrin cKO mice, which have not started to display neurodegeneration and gliosis, for 2 months. Immunohistochemistry (IHC) and Western blotting techniques were used to detect changes in neuroinflammatory responses. We found that activation of glial fibrillary acidic protein (GFAP) positive or ionized calcium binding adapter molecule1 (Iba1) positive cells was not inhibited in nicastrin cKO mice treated with DEX as compared to those treated with saline. These data suggest that DEX does not prevent or ameliorate gliosis in a neurodegenerative mouse model when given prior to neuronal or synaptic loss.