Continuous, real-time respiratory monitoring is essential in managing chronic respiratory diseases, perioperative care, and critical care settings, where the rapid and accurate detection of hypoxia can significantly impact patients' outcomes. Transcutaneous oxygen monitoring$(\text{PtcO}_{2})$is an emerging wearable sensing technology that measures the partial pressure of oxygen diffusing through the skin which correlates with arterial oxygen partial pressure$(\text{PaO}_{2})$, the clinical gold standard. Despite its promise, the clinical adoption of$\text{PtcO}_{2}$has been limited. Prior studies have primarily focused on direct comparisons between$\text{PaO}_{2}$and$\text{PtcO}_{2}$, yielding mixed results regarding their agreement and limiting$\text{PtcO}_{2}$to trend monitoring. To address this gap, we propose a finite element modeling (FEM) framework to investigate the physiological relationship between$\text{PaO}_{2}$and$\text{PtcO}_{2}$. By simulating oxygen transport across multiple skin layers, the model provides mechanistic insights into transcutaneous oxygen dynamics under varying physiological conditions, such as arterial oxygen fluctuations and localized blood flow occlusion. This personalized computational model aims to improve the accuracy and reliability of transcutaneous oxygen monitoring, enabling more effective continuous respiratory assessment.

Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient alternative solution, making it well-suited for resource-constrained environments, such as wearable devices. Its lightweight architecture and efficient memory management make it ideal for embedded systems, enabling real-time AMS detection with accuracy comparable to traditional ML models. We introduce AMS-HD, a novel framework that leverages custom feature engineering and quasi-random hyper-vector encoding to further enhance the efficiency and accuracy of HDC for AMS detection. The proposed framework demonstrates the potential for seamless integration into wearable biomedical devices for on-the-go health monitoring.

Electrochemical communication is a mechanism that enables intercellular interaction among bacteria within communities. Bacteria achieves synchronization and coordinates collective actions at the population level through the utilization of electrochemical signals. In this work, we investigate the response of bacterial biofilms to artificial potassium concentration stimulation. We introduce signal inputs at a specific location within the biofilm and observe their transmission to other regions, facilitated by intermediary cells that amplify and relay the signal. We analyze the output signals when biofilm regions are subjected to different input signal types and explore their impact on biofilm growth. Furthermore, we investigate how the temporal gap between input pulses influences output signal characteristics, demonstrating that an appropriate gap yields distinct and well-defined output signals. Our research sheds light on the potential of bacterial biofilms as communication nodes in electrochemical communication networks.

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.

Recent progress in the field of nanotechnology is enabling communication between nanodevices to be considered also for in-vivo applications. In particular, nanodevices enabling light-controlled process have started to attract a lot of interest in the research community, due to potential applications like healthcare wearable devices, intra-body applications, and optogenetics, whose main objective is to stimulate neurons by the means of the light signals. Leveraging on these premises, we consider upconversion phenomenon, namely the ability of specific nanoparticles to convert low energy radiations (e.g., near-infrared radiations) into higher energy radiation (e.g., visible light) via a non-linear process. Energy transfer as viable communication mean has been already considered based on the Förster-Resonance-Energy Transfer. In this paper, we focus on phonon-assisted energy transfer, and we analyze the requirements and conditions for generating optical signals from a genuine signal processing point of view. More specifically, based on information theory approach, we derive the conditions to minimize the error probability. We show that the dopant concentration, (i.e., the concentration of elements drugging a pure material in order to change its features) mostly influences the correct generation of the light signal. The performed analysis is based on experimental results.

Molecular Communication (MC) is a nano-scale communication paradigm where the information is carried by molecular signals. To establish information transmission using molecules, biological nanomachines can be utilized as transmitters and receivers. A bacteria can be programmed as a biotransceiver by modifying their genetic code to implement biological circuits. In this paper, genetically engineered bacteria-based biotransceivers are investigated for MC, where bacteria can generate and respond to the molecular signals. A biochemical model of biological circuits is presented, and both analog and digital signaling are studied. The challenges in connecting basic biological circuits to build these blocks are revealed. A biotransceiver architecture is introduced that combines sensing, transmitting, receiving and processing blocks. Furthermore, biological circuit design framework is proposed for transmission of signals with M-ary pulse amplitude modulation. The biological circuits designed for biotransceiver are elaborated via numerical results based on biochemical parameters of the genetically engineered bacteria. The provided results show that using biological circuits, bacteria can function as a transmitter and receiver node for MC. Our work stands as a basis for future biotransceiver design for MC.