Epstein-Barr virus (EBV)-associated gastric cancer (EBVaGC) was a unique molecular subtype of gastric cancer (GC). However, the clinicopathological characteristics and prognostic role of EBV infection remains unclear. We aimed to evaluate the clinicopathological features of EBVaGC and its role on prognosis.EBV-encoded RNA (EBER) in situ hybridization method was used to evaluate the EBV status in GC. The serum tumor markers AFP, CEA, CA19-9 and CA125 of patients were detected before treatment. HER2 expression and microsatellite instability (MSI) status was evaluated according to established criteria. The relationship between EBV infection and clinicopathological factors as well as its role on prognosis were investigated.420 patients were enrolled in the study and of 53 patients (12.62%) were identified as EBVaGC. EBVaGC was more common in males (p = 0.001) and related to early T stage (p = 0.045), early TNM stage (p = 0.001) and lower level of serum CEA (p = 0.039). No association could be found between EBV infection and HER2 expression, MSI status and other factors (p all > 0.05). Kaplan-Meier analysis revealed that both the overall survival and disease-free survival of EBVaGC patients were similar to that of EBV-negative GC (EBVnGC) patients (p = 0.309 and p = 0.264, respectively).EBVaGC was more common in males and in patients with the early T stage and TNM stage as well as patients with lower serum CEA level. Difference in overall survival and disease-free survival between EBVaGC and EBVnGC patients cannot be detected.
Hypomimia is a non-motor symptom of Parkinson's disease that manifests as delayed facial movements and expressions, along with challenges in articulation and emotion. Currently, subjective evaluation by neurologists is the primary method for hypomimia detection, and conventional rehabilitation approaches heavily rely on verbal prompts from rehabilitation physicians. There remains a deficiency in accessible, user-friendly and scientifically rigorous assistive tools for hypomimia treatments. To investigate this, we developed HypomimaCoach, an Action Unit (AU)-based digital therapy system for hypomimia detection and rehabilitation in Parkinson's disease. The HypomimaCoach system was designed to facilitate engagement through the incorporation of both relaxed and controlled rehabilitation exercises, while also stimulating initiative through the integration of digital therapies that incorporated traditional face training methods. We extract action unit(AU) features and their relationship for hypomimia detection. In order to facilitate rehabilitation, a series of training programmes have been devised based on the Action Units (AUs) and patients are provided with real-time feedback through an additional AU recognition model, which guides them through their training routines. A pilot study was conducted with seven participants in China, all of whom exhibited symptoms of Parkinson's disease hypomimia. The results of the pilot study demonstrated a positive impact on participants' self-efficacy, with favourable feedback received. Furthermore, physician evaluations validated the system's applicability in a therapeutic setting for patients with Parkinson's disease, as well as its potential value in clinical applications.
Human biomechanical energy, with features of fluctuating amplitudes and low frequency, has been considered as a potential sustainable power source for wearable healthcare monitoring devices. Developing an effective energy harvester to ensure robust energy harvesting efficiency remains highly desired. Herein, we propose a wearable pendulum–rotor-separated triboelectric–electromagnetic hybrid generator (PTEHG). The novel pendulum–rotor separation design can make the rotor propelled in one direction by the swinging pendulum, which can further facilitate a wearable hybrid energy harvester with stable energy harvesting, a broad operating bandwidth, and system reliability. By converting the biomechanical energy into electric power, the peak power density of 83.12 W/m3 is delivered by the PTEHG at a frequency of 1.6 Hz. A PTEHG-based healthcare monitoring system was also demonstrated for real-time motion tracking and fall detection. This work paves a new way for enhancing the efficiency of human biomechanical energy harvesting and presents a practical pathway for continuous healthcare monitoring.
With the development of information technology, bidding evaluation process has changed from the traditional offline paper-based mode to online digital mode. To ensure an open and fair environment for online bidding evaluation, an intelligent supervision system is needed, which includes the phone call behavior detection. In this paper, we proposed a deep learning method for bidding evaluation expert's phone call behaviors detection. The proposed method consists of object detection and ROI analysis. In the detection stage, the phone and head are detected with our proposed YOLOv5+Transformer network. The phone call behavior is then recognized with self-defined ROI analysis. The experimental results show that the mAP is increased by 4.8% with the proposed network when compared with original YOLOv5s. In addition, the proposed method is used to analyze the video stream comprehensively, which can effectively solve the problems of object scale variation, occlusion, and accurately detect the phone call behavior.
Total Knee Arthroplasty (TKA) is the most effective approach for function restoration in patients with severe knee osteoarthritis. However, kinematic, kinetic and muscle activation differences between post-TKA patients and healthy people can be observed in many studies. Exoskeletons have been applied to post-TKA rehabilitation for many years, while few studies concentrated on the stance phase abnormality, neither in the aspect of kinematics nor in muscle activation. In this paper, we propose an indirect resistance strategy for post-operative TKA patient gait training. Three healthy subjects were asked to wear the hip exoskeleton and provided with 8 N·m resistance on the hip extension phase of the gait cycle. The intervention leads to an increment in the knee extension muscle activity as well as the augmentation in maximum knee angle in loading response. The results indicated that the application of resistance in the hip extension phase is a potential therapeutic approach for post-TKA rehabilitation, and may increase the gait training efficiency in the near future.
Wearable electrical textiles are of great value for future personalized healthcare applications. Herein, we report a 3-D meshed textile pressure sensor, using a facile dip coating strategy, that combing the sensitive 2-D Ti3C2Tx MXene nanolayer with woven textile fibers. Based on the sensitive 3-D cross-connected conductive network, the textile sensor achieves simultaneously a high sensitivity of up to 81.9 kPa−1 and a fast response time of 30 ms in a wide pressure detection range (0–19 kPa) as well as prominent durability of more than 5000 cycles. Benefiting from superior performances, the textile sensor can be applied for real-time human physiological signals monitoring from pulse waves to human joint motion. This work represents a solid step toward personalized healthcare and clinical diagnosis.
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