A Molecular Communications System for the Detection of Inflammatory Levels Related to COVID-19 Disease

Influence of Red Blood Cells on Channel Characterization in Cylindrical Vasculature

Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver.

Identification of the shared molecular mechanisms between major depressive disorder and COVID-19 from postmortem brain transcriptome analysis

OBJECTIVES

This study aims to investigate the molecular mechanisms underlying the interaction of major depressive disorder (MDD) and COVID-19, and on this basis, diagnostic biomarkers and potential therapeutic drugs are further explored.

METHODS

Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify common key genes involved in the pathogenesis of COVID-19 and MDD. Correlations with clinical features were explored. Detailed mechanisms were further investigated through protein interaction networks, GSEA, and immune cell infiltration analysis. Finally, Enrichr's Drug Signature Database and Coremine Medical were used to predict the potential drugs associated with key genes.

RESULTS

The study identified 18 genes involved in both COVID-19 and MDD. Four key genes (MBP, CYP4B1, ERMN, and SLC26A7) were selected based on clinical relevance. A multi-gene prediction model showed good diagnostic efficiency for the two diseases: AUC of 0.852 for COVID-19 and 0.915 for MDD. GO and GSEA analyses identified specific biological functions and pathways associated with key genes in COVID-19 (axon guidance, metabolism, stress response) and MDD (neuron ensheathment, biosynthesis, glutamatergic neuron differentiation). The key genes also affected immune infiltration. Potential therapeutic drugs, including small molecules and traditional Chinese medicines, targeting these genes were identified.

CONCLUSION

This study provides insights into the complex biological mechanisms underlying COVID-19 and MDD, develops an effective diagnostic model, and predicts potential therapeutic drugs, which may contribute to the prevention and treatment of these two prevalent diseases.

A Simple Queuing Model for Molecular Communications Receivers

The complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A typical scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in the diseased area. In this paper, we propose a simple but reliable receiver model for diffusion-based molecular communication systems tackling the time needed for analyzing such a system. The proposed model consists of using an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. It takes into account not only the fact that the absorption of molecules can occur only through receptors, but also that absorption is not an instantaneous process and may require a significant time during which the receptor is not available to bind to other molecules. Our results, expressed in terms of number of absorbed molecules and average number of busy receptors, demonstrate that the proposed approach is in good agreement with results obtained through particle-based simulations of a large number of receptors, although the time taken for obtaining the results with the proposed model is an order of magnitudes lower than the simulation time. We believe that this model can be the precursor of novel class of models based on similar principles that allow realizing reliable simulations of much larger systems.