Oral cancer is one of the most commonly found cancers worldwide. Oral Epithelial Dysplasia (OED) is an Oral Potentially Malignant Disorder (OPMD) that can be characterized for preventive oral cancer screening. The standard for OED histological grading is conducted via the epithelial regions of tissue biopsies. However, this procedure is laborious, time-consuming, and subjective; consequently, it is prone to variability due to fatigue and limited expertise. Therefore, this study aims to explore the potential of using Convolutional Neural Network (CNN) and Transformer models for an automated epithelium segmentation algorithm directly from Whole Slide Images (WSIs). This approach can reduce the manual process and support pathologists in grading activities. Accordingly, candidate architectures based on CNN and Transformer are selected: UNet, ResNet50-UNet, VGG19-UNet, Swin-UNet, and MISSFormer. These models are trained using patch-based segmentation to mitigate the high computational cost caused by processing WSIs. The results indicate that UNet, optimized with the ADAM optimizer, demonstrates the best performance in patch-based segmentation with Intersection over Union (IoU) of 0.82 and Dice-Similarity Coefficient (DSC) of 0.87. Furthermore, this model achieves the highest IoU and DSC for tissue-level prediction, scoring 0.88 and 0.94, respectively. According to the experiment, overlapping and non-overlapping patching strategies perform similarly in most of the selected architectures. The latter approach, hence, is suggested for computational efficiency. These results can support enhancing automated epithelium segmentation to provide a reliable tool for assisting pathologists.
Stroke is a disease with a high mortality rate and is one of the leading causes of acquired disability in adults. Some patients experience some forms of disability after a stroke, such as long-lasting physical paralysis, speech impairment, and cognitive impairment. For post-stroke hemiplegia, studies have demonstrated that long-term and continuous resistance training can help patients regain some degree of motor ability. However, the fact is that some patients have not regained motor function after 3-6 months of rehabilitation training. The reasons for this are that it is difficult for patients to adhere to the training and to obtain appropriate training guidance.To address the problem of inadequate resource reserves for rehabilitation physicians, robotic systems were introduced into post-stroke rehabilitation training from the 1990s. However, in actual use, the motor assistance that robotic systems can provide is still judged by rehabilitation physicians based on the patient's status and degree of muscle damage, and the patient's motor initiative and attentional state cannot be fully explored.In this study, a hybrid brain-computer interface-based post-stroke rehabilitation training system was designed to infer patients' muscle rehabilitation status based on the level of corticomuscular coherence (CMC), so as to provide adaptive training guidance to patients during the long-term and continuous training process, promote patient initiative during the training process, and enhance the training effect. This study focuses on the correlation between corticomuscular coherence and voluntary muscle contraction, so as to infer the muscle contraction status of patients based on corticomuscular coherence. Finally the present study obtained a significant positive linear relationship between corticomuscular coherence and different force magnitudes in the alpha band.
We evaluated the effects of transcranial direct current stimulation (tDCS) on cognitive performances including the arithmetic performance, working memory, and electroencephalography (EEG) measurements of healthy participants when applied over multiple sessions spanning a five-day period. In a double-blind crossover randomized controlled study, participants received either active left tDCS or sham stimulation. Despite no significant changes in arithmetic performance, the tDCS group displayed enhanced reaction time and accuracy in a 2-back task compared with the control (sham) group, and EEG measurements revealed an increase in the alpha-band power spectral density (PSD) at the left temporal lobe for the treatment group over the experimental period, suggesting modulations in cortical activity. Further research is needed to elucidate the specific cognitive regions affected by tDCS and the underlying neurophysiological mechanisms.
Nowadays, people suffer from a variety of illnesses related to the body, including those that affect the lower body and make it difficult or impossible for patients to walk. The main goal of a wheelchair is to transport patients who cannot walk to their desired destination, either by themselves or with the help of a caregiver. The project's objective was to develop an effective solution for patients who are unable to operate the joystick on electronic wheelchairs. The smart wheelchair would be beneficial for patients who primarily reside in their rooms. This smart wheelchair will create a map of the room, allowing the patient to navigate it easily using only one finger to control it by simply clicking on the pre-created map.This project developed an electronic wheelchair that could be controlled and driven autonomously using a ROS (Robot Operating System) platform. Additionally, it utilized the SLAM (Simultaneous Localization and Mapping) method, which enables the construction of a map of an unknown environment while simultaneously localizing the wheelchair for navigation purposes. To assess the accuracy of map creation in an unknown environment, calibration was performed with and without loading weight to measure the distance and angle of the wheelchair's movement.The results of this project show that the developed electronic wheelchair successfully builds a map of an unknown environment and utilizes this map for navigation. Users can simply click on their desired destination on the map, and the wheelchair will autonomously navigate towards it. Moreover, the wheelchair can avoid obstacles while navigating. Although the smart wheelchair can perform well, there are some limitations due to the RPLidar. The RPLidar detects everything on the ground in parallel when creating the map and while navigating. Therefore, if any object is located below the RPLidar, it may not be able to detect it.
Medium-chain-length polyhydroxyalkanoate (MC-LPHA) is a biodegradable polyester with an ability to biodegrade and interact with the human body, making it a material of interest for various medical applications. As a versatile biopolymer, there is a growing trend towards its potential utilization in the field of medical elastomers. This study assessed the medical adhesive capabilities of MCL-PHA by evaluating its physical properties compared to commonly used medical adhesives. The results demonstrate that MCL-PHA possesses multiple characteristics aligning with those of existing medical adhesives. Specifically, MCL-PHA exhibits an adhesive strength to porcine skin of 50.2 kPa, closely resembling wound dressing adhesives and surpassing surgical adhesives. Furthermore, its shear resistance is comparable to surgical adhesive, measured at 27.6 kPa. The testing of material detachment from porcine skin indicates that MCL-PHA can be removed without causing harm. These findings illustrate the potential of MCL-PHA as a medical adhesive, indicating its properties which are consistent with both external wound dressings and surgical adhesives.
