The circulatory system ensures continuous blood flow to all body tissues. During gestation, progressive changes in the female body and immune system lead to homeodynamic alterations, characterized by increased venous return and cardiac output, enhancing blood flow to meet tissue metabolic demands. This paper presents the Homeodynamic Maternal-Placental Molecular Communication Model (HMP-MC) in the cardiovascular network to accurately represent circulatory components and interactions. The model also analyzes the dynamics of exosomes carrying Zika virus particles throughout pregnancy, integrating new parameters from gestational physiological changes. We quantify the risk of pregnancy over different stages accounting the impact of infection on the placenta viral. This interdisciplinary approach explores cardiovascular interactions during pregnancy analysis over time with the quantification of pregnancy risk.

The integration of intelligence into organ-on-a-chip devices is increasingly recognized as a crucial advancement to enhance their pharmacological applications1. Neurons-on-a-chip provides a specialized platform for embodied biological intelligence, enabling more sophisticated biocomputing capabilities2. However, designing rationally methods to implement controlled biocomputing poses significant challenges. Previous research has focused on utilizing microtubules to promote axonal growth and manipulate synaptic connections, laying the groundwork for directing information3. Our study aims to evaluate how the rational engineering of neuronal microtunnels can be leveraged to enhance biocomputing power in neurons-on-a-chip solutions, with a particular focus on generating directed communication mechanisms between neuronal clusters.

While deep learning and backpropagation continue to dominate the field of machine learning in terms of benchmarks and versatility, recent neuroscientific advances shed light on more biologically plausible approaches. Spiking neural networks (SNNs), modelled after action potential dynamics, offer inherent time sensitivity and more efficiency in terms of performance to complexity. While investigating paradigms to support such alternatives, we attempt to answer whether reservoir computing can benefit from a spiking network based implementation with elements of biologically realistic models. This is done by varying both hyperparameters and reservoir generation approaches and comparing implementations to spot potential improvements. We demonstrate how customized training of SNNs can result in competitive performance levels at lower operational complexity and be readily applied to other paradigms, such as the development of reservoir dynamics.

Astrocytes have the ability to modulate neuronal communication by regulating various levels of ionic concentrations. The development of models for molecular communication ought to investigate the utilisation of various ionic transportation mechanisms, with the aim of enhancing performance and biocompatibility, thereby facilitating co-existence within cellular tissues. In this study, we suggest the utilisation of both Ca$^{2+}$ and Na$^{+}$ ions to investigate the non-linearities of propagation in cellular tissues for MC. In this study, we also investigated the impact of various ionic transport mechanisms, including the sodium-calcium exchanger, sodium-potassium pump, and calcium pump, to assess their impact on the intracellular propagation of Ca$^{2+}$. We analysed four metrics, namely channel capacity, bit error rate (BER), communication gain, and signal-to-noise ratio, to evaluate the effects of these structures.

In this work, we aim to study how astrocytes control electrophysiological activity during network formation. Here we use a combination of spike/burst analysis and complex analysis of the raw neuronal signals to assess the role of astrocytes on microelectrode arrays (MEA). We cultured rat primary cortical neurons and astrocytes on 60-electrode MEAs with different neuron/astrocyte ratios ranging from â\euroœpureâ\euro? neuronal cultures (NS) without separately added astrocytes to co-cultures containing 50% neurons and 50% astrocytes. We analyzed the spike and burst rates, as well as the complexity-entropy causality planes of the signals to deeply study the patterning of the neuronal signals. Moreover, we classified the spike waveforms from the NS and the co-cultures based on their signal properties, and we predicted the signals in the co-cultures with a Random Forest regressor. The dataset used can be found at: https://zenodo.org/record/7741379.

Wireless Neural Interfaces are minimally invasive untethered links between the brain tissue and silicon platforms. Even though these interfaces have been envisioned for many biomedical applications, it is unclear how the ultimate technology will be, due to the lack of studies that support spatially distributed networks. In this paper, we contribute to the first step towards a networked wireless neural interfaces scenario by addressing the distributed power allocation through adaptable beamforming by varying the acoustic beam lateral resolution. Our technique provides selectivity and depthness of neural interface nodes inside the Brain's neocortex. Our results show improvements in average power transfer efficiency for sparser beams compared to narrower ones for a randomly placed network of implantable devices with 15 nodes within a 4mm2 space in the neocortex. Lateral resolution indeed achieves selective of the devices, the average efficiency was non-constant throughout the network allowing devices to operate in different time scales suitable for asynchronous networks. Enabling a functioning network is essential for providing an impactful interface with the Brain to allow future usage of this technology in a diverse set of neurological diseases theranostics.