Directing extracellular vesicles (EVs), such as exosomes and microvesicles, toward specific cells is an emerging focus in nanomedicine, owing to their natural role as carriers of proteins, RNAs, and drugs. EVs can be manipulated by external electric fields due to their intrinsic surface charge and biophysical properties. This study investigates the feasibility of using extremely low-frequency electromagnetic fields to guide EV transport. A theoretical framework based on the Fokker-Planck equation was developed and numerically solved to model vesicle trajectories under time-harmonic drift. Computational simulations were conducted to systematically assess the influence of key electric field parameters-including phase, frequency, and intensity- on vesicle displacement and trajectory. The findings demonstrate that frequencies below 5Hz combined with field strengths of 200-2000 V/m can induce substantial directional control of EV motion. Moreover, enhanced directivity was achieved through the application of multi-component electric fields. Overall, this work establishes a theoretical foundation for the external-field-based beam steering of nanoparticles within the framework of MC.

This survey paper provides an in-depth exploration of Federated Learning (FL) in Internet of Things (IoT) environments, focusing on privacy-preserving techniques and their influence on model performance and network efficiency. It highlights key challenges and opportunities at the intersection of these technologies by offering a comprehensive review of FL applications in IoT. First, a customized taxonomy is introduced to evaluate the privacy levels, quality of service (QoS) and network efficiency of various Privacy-Preserving FL (PPFL) solutions in IoT configurations. Furthermore, the survey investigates strategies to improve FL accuracy while addressing resource and network constraints, both independently and together with privacy preservation techniques. Our findings underscore the complexity of optimizing resource utilization, learning performance, and privacy resilience, revealing that no single PPFL solution universally applies. The paper further identifies future research directions, including the integration of advanced technologies beyond 5G networks, and discusses standards, protocols, real-world PPFL projects from world-renowned industries for potential IoT applications.

We investigate the application of semantic information theory to drug delivery systems (DDS) within the molecular communication (MC) framework. To operationalise this, we observe a DDS as a molecular concentration-based channel. Semantic information is defined as the amount of information required for a DDS to achieve its therapeutic goal in a dynamic environment. We derive it by introducing interventions, defined as modifications to DDS parameters, a viability function, and system-environment correlations quantified via the channel capacity. Here, the viability function represents DDS performance based on a drug dose-response relationship. Our model considers a system capable of inducing functional changes in a receiver cancer cell, where exceeding critical DDS parameter values can significantly reduce performance or cost-effectiveness. By analysing the MC-based DDS model through a semantic information perspective, we examine how correlations between the internalised particle concentration and the released particle concentration evolve under interventions. The final catalogue of results provides a quantitative basis for DDS design and optimisation, offering a method to determine optimal DDS parameter values under constraints such as chemical budget, desired effect and accuracy. Thus, the proposed framework can serve as a novel tool for guiding DDS design and optimisation.

Intercellular communication is crucial for organ function, with extracellular vesicles (EVs) acting as common messengers for almost all cells. This study proposes a novel EV-mediated intercellular communication system that uses a modulation technique regulated via altered intracellular-cytosolic calcium dynamics regulation. As a case study, intercellular communication within a cardiac muscle is considered with cardiomyocyte cells serving as transceivers. Through molecular communication theory, we propose a comprehensive model addressing EV release kinetics, propagation, degradation, and uptake. A linear time-invariant Poisson channel model is developed and closed-form expressions, verified through particle-based simulations, are derived for EV detection probabilities at the receiver. A closed-form bit error probability is derived and validated through Monte Carlo simulations. By selecting an optimal receiver threshold: 1) bit error rate (BER) in EV-mediated intercellular communication is robust against adverse effects from cardiac disorders, such as myocardial infarction; 2) transmitter design can be optimized by minimizing actuation signal amplitude and pulse width; 3) BER stays stable across heart rates for different distances, demonstrating the robustness of EV-mediated communication. This study enhances our ability to engineer precise and reliable intra-body cardiac communications, which may offer valuable applications for cardiovascular disease treatment, for example, the realization of biological lead-less multi-nodal pacemakers.

Molecular communication (MC) is envisioned to realize nanotheranostics as an emerging diagnostic tool to improve existing treatment modalities. Phase separation (PS) is a complex time-dependent process responsible for discrimination of two independent phases from a single homogeneous mixture. Recently, PS was revealed to be the fundamental mechanism behind formation and organization of the living cells. Inspired by PS mechanisms in nature, we establish a novel modulation scheme for MC which encodes the information in the dispersion of molecules diffusing in the environment. Hence, the diffusion distribution can be considered as carriers of molecules. To evaluate the performance of this communication scheme, a dual-carrier diffusion-division modulation (DDM) is adopted where each carrier can be effectively modeled by superposition of multiple independent phases. We derive the theoretical bit error rate (BER) of the proposed DDM scheme which is validated by particle-based simulation (PBS). Furthermore, performance of the multi-carrier DDM scheme is compared to the well-known on-off keying (OOK) modulation scheme (as the most relevant benchmark) and pulse-position modulation (PPM) scheme. It is shown that performance of the DDM-based MC system can be boosted by increasing the transmission power and/or using multiple carriers. Interestingly, the proposed DDM is a covert modulation scheme since any other receiver cannot decode the transmitted signal by just counting the number of received molecules unless having the shared key. Moreover, the DDM scheme requires a receiver that exploits the displacement distribution of the molecules inside the receiver to infer about the tranmitted bit. We strongly believe that this concept will introduce novel types of communication schemes more compatible with biological microenvironments. Also, this work establishes the foundation for more complex orthogonal multi-carrier DDM schemes which can potentially unlock novel cellular sensing mechanisms in biology.

Extracellular vesicles (EVs) are lipid bilayer enclosed nanovesicles involved in intercellular communication. EVs are emerging as potential cancer biomarkers, providing insights into the condition of parent cancer cells. Their composition and entry into the bloodstream are influenced by factors such as tumor grade, type, and the configuration of the vascular network at the release site. In this work, we propose a computer simulation model to emulate the penetration of EVs into the bloodstream. We take into account convective and diffusive parameters that are influenced by the tumor’s characteristics, and the configuration of the vasculature and lymphatic network. We investigate the penetration rate of EVs into the bloodstream in terms of various parameters such as vessel wall permeability and the configuration of the vasculature and lymphatic networks. Our parametric study using a 2D model demonstrates that increasing the permeability coefficient, as observed in tumor tissue, could lead to a two-fold increase in EV penetration rate into the bloodstream. We believe that this model offers pre-experimental insights concerning liquid biopsy assays and the metastatic progression of the disease.

Accurate decoding of EEG signals requires comprehensive modeling of both temporal dynamics within individual channels and spatial dependencies across channels. While Transformer-based models utilizing channel-independence (CI) strategies have demonstrated strong performance in various time series tasks, they often overlook the inter-channel correlations that are critical in multivariate EEG signals. This omission can lead to information degradation and reduced prediction accuracy, particularly in complex tasks such as neurological outcome prediction. To address these challenges, we propose Biaxialformer, characterized by a meticulously engineered two-stage attention-based framework. This model independently captures both sequence-specific (temporal) and channel-specific (spatial) EEG information, promoting synergy and mutual reinforcement across channels without sacrificing CI. By employing joint learning of positional encodings, Biaxialformer preserves both temporal and spatial relationships in EEG data, mitigating the interchannel correlation forgetting problem common in traditional CI models. Additionally, a tokenization module with variable receptive fields balance the extraction of fine-grained, localized features and broader temporal dependencies. To enhance spatial feature extraction, we leverage bipolar EEG signals, which capture inter-hemispheric brain interactions, a critical but often overlooked aspect in EEG analysis. Our study broadens the use of Transformer-based models by addressing the challenge of predicting neurological outcomes in comatose patients. Using the multicenter I-CARE data from five hospitals, we validate the robustness and generalizability of Biaxialformer with an average AUC 0.7688, AUPRC 0.8643, and F1 0.6518 in a cross-hospital scenario.

The integration of harmonic backscattering and RF (Radio Frequency) wireless power transfer (WPT) technologies offers a new pathway for powering and communicating with deep in-body implantable medical sensors. This work introduces a novel system that addresses the critical challenges of miniaturization, low power consumption, and robust wireless communication in implantable devices. By employing a voltage doubler rectifier and leveraging harmonic modulation, the system harvests RF energy efficiently and transmits data reliably via harmonic backscattering. The approach separates uplink communication frequencies from WPT frequencies, reducing interference, mitigating self-jamming, and significantly enhancing receiver sensitivity. Experimental validation demonstrates reliable data transmission at receiver sensitivity levels as low as -97 dBm, even under deep implantation conditions. The compact design employs low-cost components, enabling battery-free operation while maintaining high performance. This innovative system presents a scalable and practical solution for longer-lasting, and more reliable devices for diagnostics and therapeutic applications.

Focused ultrasound (FUS) combined with microbubbles has emerged as an effective technique for enhancing drug delivery across the stringent blood-brain barrier (BBB). However, tuning up FUS-sonicating parameters that lead to the desired uniform drug distribution at the target spots remains a challenge due to underlying biophysiological complexity, particularly in multi-target scenarios. Here, we propose a novel system model that characterizes the relationship between external actuating signals and therapeutic responses within a Multiple-Input Multiple-Output (MIMO) framework. We observe the drug delivery pathway as a communication channel, where transvascular and intratumoral pharmacokinetics introduce effects equivalent to channel distortion and interference, further leading to non-uniform drug distribution and unintended accumulation in healthy tissues. To mitigate these effects, we develop an equivalent channel matrix and exploit a known concept of precoding scheme with the aim to optimize sonication parameters in a way to ensure uniform and effective drug delivery across all targeted regions. We numerically demonstrate our approach using available preclinical data on low-intensity FUS-mediated chemotherapy for brain tumors.

Inspired by wireless multi-carrier communications, we extend the recently introduced diffusion-division modulation (DDM) scheme to the orthogonal diffusion-division multiplexing (ODDM) technique which realizes multi-carrier molecular communication (MC) systems. Moreover, ODDM enables implementing interference-free orthogonal diffusion-division multiple-access (ODDMA) networks. Like the DDM, the ODDM(A) emulates the interesting covert communication mechanism where the information cannot be retrieved without having the shared key. To do so, the diffusion distribution, also referred to as diffusion spectrum (DS), is divided into several carriers having optimal bandwidths in the sense of minimizing mean-value deviations. Interestingly, the results based on the particle-based simulations (PBS) reveal that the bit error rate (BER) remains constant for all carriers even by increasing the number of carriers. The proposed ODDMA has the potential to achieve performance similar to molecular-division multiple-access (MDMA), which serves as the upper bound for all multiple-access techniques.

Focused ultrasound (FUS) has emerged as a transformative technique for enhancing drug delivery to brain tumors by temporarily and locally disrupting the blood-brain barrier (BBB). Despite significant progress in both pre-clinical and clinical research, a major challenge remains: the absence of a model that connects the properties of drug particles and FUS sonication parameters to therapeutic effectiveness. In this study, we introduce a novel empirical model that integrates key factors, including drug pharmacodynamics, microbubble kinetics for BBB disruption, intrabrain ultrasound signal propagation, and skull-thickness variations. The model defines a new sonication parameter that encapsulates ultrasound signal characteristics and predicts the concentration of therapeutic agents internalized or bound to DNA with an accuracy exceeding 82%. By employing data from previous pre-clinical studies, this model facilitates the development of precise sonication protocols tailored for clinical applications. These advancements represent a significant step toward personalized FUS-mediated treatments, bridging the gap between experimental research and patient-centered therapies.

Wireless modules have become a crucial component of many modern implantable medical devices (IMDs), including those used for cardiac applications. In this study, we conducted a preliminary multi-physics simulation for intra-cardiac communication based on a simplified biventricular geometry. Initially, we simulated the cardiac cycle, demonstrating the deformation of the biventricular, and in one scenario, two cardiac implants were positioned within the cardiac chamber, with relative displacement observed. Based on electromagnetic simulations, we analyzed the fluctuations in coupling at different frequencies (6.78, 13.56, 433, 915, and 2400 MHz) during myocardial mechanical deformation over a simulated cardiac cycle. The method used in this study provides a paradigm for the intra-cardiac implant investigation, and the results are a major component in the link budget analysis.

The discovery that tumor cells discharge vast quantities of extracellular vesicles (EVs) that contain functional molecules which promote immune modulation and drug resistance, urges the need for novel therapeutic interventions. Here we take an approach based on the EV-release-modulation strategy to treat tumors, suppress their spread, and monitor the therapy efficacy. We propose a molecular communication (MC)-based system model to implement the oncogenic EV release modulation and monitor the EV spatiotemporal biodistribution. The proposed system uses drugs which target the tumor cell pH regulatory biochemical mechanisms. We develop a comprehensive computational framework where we integrate adapted and extended versions of the biophysical model of tumor cell pH regulation, the tumor cell proliferation model, and our previously developed MC model of pHe-dependent EV biodistribution. We fix specific parameter values of the system model by combining available experimental data performed in diverse tumor cell systems. Using the developed system, we analyse the dynamics of intracellular pH (pHi), extracellular pH (pHe), tumor cell growth pattern, and EV release and biodistribution. Our proposed system and computational framework can be used as a tool to track the oncogenic EV biodistribution, which can be used as a biomarker to monitor the tumor and optimize anticancer therapy.

This paper examines the use of radiofrequency (RF) channels for hemodynamic monitoring in cardiac pacemakers. It analyzes RF signal variations between intracardiac transceivers in the right ventricle (RV) and right atrium (RA), as well as subcutaneous receivers, to determine their correlation with cardiac dynamics. The study shows that temporal RF signal variations closely align with cardiac rhythm, allowing for the estimation of parameters such as chamber volume, valve behavior, and pressure changes. These results underscore the potential of RF-based sensing as a novel method for real-time cardiac monitoring in pacemaker systems.

A variant of the Hodgkin-Huxley model has been proposed in which time-varying Na+ and K+ ion conductances are replaced by memristors, based on the proposal that ion channels are memristors. This hypothesis predicts that current-voltage plots of neurons subjected to sinusoidal stimulation should exhibit a pinched hysteresis loop, the fingerprint of a memristor. We tested this using whole-cell patch clamp recordings from human neurons derived from neural progenitor cells and observed pinched hysteresis loops in 16% of all recorded neurons. Recordings with current clamp and voltage clamp with a holding potential of 0 mV yielded a higher success rate than voltage clamp recordings with a holding potential of around - 60 mV. In addition, more neurons exhibited pinched hysteresis loops when the applied voltage or current amplitude was high, and the frequency was low. The recording of pinched hysteresis loops in neurons gives evidence that these contain memristors and this is the first time to be shown experimentally. This is not just an indication that the memristor-based Hodgkin-Huxley model is valid; it also demonstrates that models of neurons based on memristors actually resemble nature. We then subjected simulated neurons expressing Hodgkin-Huxley voltage-gated Na+ and K+ channels to sinusoidal membrane potential oscillations and found that the presence of pinched hysteresis loops depended strongly on the density of voltage-gated K+ channels and on the membrane capacitance but not on the presence of voltage-gated Na+ channels. Because Na+ channels are expected to be largely inactivated in neurons at a holding potential of 0 mV, these findings together suggest that it is the presence of voltage-gated K+ channels that is critical for the observed memristive behavior.

The potential of advanced waveform analysis of physiological signals to diagnose varying levels of hypovolemia has not been fully explored. This study extends previous work by comparing the discriminative ability of invasive and non-invasive signals to classify levels of ongoing hypovolemia using a deep learning (DL) framework. Hypovolemia was simulated via a dynamic lower body negative pressure (LBNP) model among healthy volunteers, allowing for real-life-like fluctuations in intravascular blood volume caused by bleeding and resuscitation. The analysis incorporated photoplethysmography (PPG) as a non-invasive signal and invasive arterial blood pressure (ABP) waveforms, processed using the same DL-based framework. A supervised DL model was developed to perform ternary classification, segmenting the signals and labeling them with corresponding LBNP target levels: Mild (Class 1), Moderate (Class 2), and Severe (Class 3) blood volume loss. The model utilized time-frequency representations of the waveform segments and late fusion of latent space features to enhance classification performance. Results revealed that while both PPG and ABP demonstrated the ability to detect hypovolemia levels, the PPG signal showed comparable or superior performance in classification accuracy with AUROC 0.8861 and F1 0.7216, offering a non-invasive alternative with high diagnostic potential.

Wireless power transfer is a method for energizing future implantable medical electronics. In this study, a metasurface-based near-field magnetic wireless power transfer system for deep implants is presented, and electromagnetic safety parameters, including field distributions, specific absorption rate (SAR), and temperature variations, are evaluated. The power transfer is modeled for a receiver implant at a distance of 8.5 cm from the designed metasurface. Based on the results, a maximum localized SAR of 0.072 mW/kg is achieved when the efficiency is 1.62 %. Moreover, continuous power transfer shows that the local tissue temperature rises by less than 1.1°C.

Treating brain diseases with therapeutic particles imposes significant challenges as particles are usually too large to traverse the gaps between endothelial cells in the blood-brain barrier (BBB). Focused ultrasound (FUS) for disruption of the BBB has been proposed as a remedy. However, the extent of disruption and the efficiency of the particle delivery to the regions of interest are highly dependent on FUS sonication parameters. This study investigates the effects of not only FUS sonication parameters but also the therapeutic particle admin-istration scheme by exploiting communication-theoretic channel modeling. Specifically, the particle pathways from blood vessels to hallmarked spots in the brain interstitial space are abstracted as a single-input-multiple-output (SIMO) channel. The channel outputs are then examined through the lenses of communication-theoretic measures such as channel gain, transmission efficiency, signal-to-noise ratio, and bit error ratio. The numerical results are displayed utilizing the available clinical data on six patients with brain cancer. The results show that the proposed approach could be exploited in future studies to maximize the efficacy of the treatment and minimize adverse effects.

Bacterial sensor systems can be used for the detection and measurement of molecular signal concentrations. The dynamics of the sensor directly depend on the biological properties of the bacterial sensor cells; manipulation of these features in the wet lab enables the engineering and optimization of the bacterial sensor kinetics. This necessitates the development of biologically meaningful computational models for bacterial sensors comprising a variety of different molecular mechanisms, which further facilitates a systematic and quantitative evaluation of optimization strategies. In this work, we dissect the detection chain of bacterial sensors, focusing on computational aspects. As a case example, we derive, supported by wet-lab data, a complete computational model for a Streptococcus mutans-based bacterial sensor. We address the engineering of bacterial sensors by mathematically investigating the impact of altered bacterial cell properties on the sensor response characteristics, specifically sensor sensitivity and response signal intensity. This is achieved through a sensitivity analysis targeting both the steady-state and transient sensor response characteristics. Alongside the demonstration of the suitability of our methodological approach, our analysis shows that an increase in sensor sensitivity through targeted manipulation of bacterial physiology often comes at the cost of generally diminished sensor response intensity.