Body-Centric Terahertz Networks: Prospects and Challenges

Data Transmission Enhancement Using Optimal Coding Technique Over In Vivo Channel for Interbody Communication

Wireless in vivo actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of in vivo wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. In vivo wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring. Also, patient data may be collected over a long period of time. Given the large fading in in vivo channels due to the signal path going through flesh, bones, skins, and blood, channel coding is considered a solution for increasing the efficiency and overcoming inter-symbol interference in wireless communications. Simulations are performed by using 50 MHz bandwidth at Ultra-Wideband frequencies (3.10-10.60 GHz). Optimal channel coding (Turbo codes, Convolutional codes, with the help of polar codes) improves data transmission performance over the in vivo channel in this research. Moreover, the results reveal that turbo codes outperform polar and convolutional codes in terms of bit error rate. Other approaches perform similarly when the information block length is increased. The simulation in this work indicates that the in vivo channel shows less performance than the Rayleigh channel due to the dense structure of the human body (flesh, skins, blood, bones, muscles, and fat).

Effect of terahertz waves on the aggregation behavior of neurotransmitters

Understanding the dynamics of neurotransmitters is crucial for unraveling synaptic transmission mechanisms in neuroscience. In this study, we investigated the impact of terahertz (THz) waves on the aggregation of four common neurotransmitters through all-atom molecular dynamics (MD) simulations. The simulations revealed enhanced nicotine (NCT) aggregation under 11.05 and 21.44 THz, with a minimal effect at 42.55 THz. Structural analysis further indicated strengthened intermolecular interactions and weakened hydration effects under specific THz stimulation. In addition, enhanced aggregation was observed at stronger field strengths, particularly at 21.44 THz. Furthermore, similar investigations on epinephrine (EPI), 5-hydroxytryptamine (5-HT), and γ-aminobutyric acid (GABA) corroborated these findings. Notably, EPI showed increased aggregation at 19.05 THz, emphasizing the influence of vibrational modes on aggregation. However, 5-HT and GABA, with charged or hydrophilic functional groups, exhibited minimal aggregation under THz stimulation. The present study sheds some light on neurotransmitter responses to THz waves, offering implications for neuroscience and interdisciplinary applications.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.