IEEE Transactions on NanoBioscience最新文献

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.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.

Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was introduced, an innovative method employing deep learning models to predict high-risk sequences. The LQSF approach leverages a classification model trained on error-prone sequences, enabling efficient pre-sequencing filtration of low-quality sequences and reducing time and resources in subsequent stages. Analysis has demonstrated a clear distinction between high and low-quality sequences, confirming the efficacy of the LQSF method. Extensive training and testing were conducted across various neural networks and test sets. The results showed all models achieving an AUC value above 0.91 on ROC curves and over 0.95 on PR curves across different datasets. Notably, models such as Alexnet, VGG16, and VGG19 achieved a perfect AUC of 1.0 on the Original dataset, highlighting their precision in classification. Further validation using Illumina sequencing data substantiated a strong correlation between model scores and sequence error-proneness, emphasizing the model's applicability. The LQSF method marks a significant advancement in DNA storage technology, introducing active sequence filtering at the encoding stage. This pioneering approach holds substantial promise for future DNA storage research and applications.

The identification of biomarkers has significant benefits for early disease diagnosis and treatment. Hence, there is an increasing demand for low-cost, disposable point-of-care diagnostic devices for rapid and specific biomarker detection, with good sensitivity and range. Interdigitated electrodes (IDEs) are among the most widely used transducers, especially in chemical and biological sensors, because of their high sensitivity, low cost, and straightforward manufacturing procedure. In this work, a simple 3D printed IDE structure has been developed for cardiac troponin I detection to indicate the risk of acute myocardial infarction (AMI). IDEs have been fabricated using 3D printing technique and the electrically conductive composite polylactic acid (PLA) filament being utilized for the fabrication of electrodes. The demonstrated cardiac troponin I sensor has a calculated quantification limit and detection limit of 0.233 ng ml^-1 and 76.97 pg ml^-1, respectively which are clinically significant ranges for AMI identification. Electrochemical analytical techniques, such as electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV), were carried out for the detection of analyte concentration. Furthermore, using this fabrication methodology IDEs can be fabricated for under US$ 0.4 which can be utilized to detect several other biomarkers.

Proteins can be regarded as thermal nanosensors in an intra-body network. Upon being stimulated by Terahertz (THz) frequencies that match their vibrational modes, protein molecules experience resonant absorption and dissipate their energy as heat, undergoing a thermal process. This paper aims to analyze the effect of THz signaling on the protein heat dissipation mechanism. We therefore deploy a mathematical framework based on the heat diffusion model to characterize how proteins absorb THz-electromagnetic (EM) energy from the stimulating EM fields and subsequently release this energy as heat to their immediate surroundings. We also conduct a parametric study to explain the impact of the signal power, pulse duration, and inter-particle distance on the protein thermal analysis. In addition, we demonstrate the relationship between the change in temperature and the opening probability of thermally-gated ion channels. Our results indicate that a controlled temperature change can be achieved in an intra-body environment by exciting protein particles at their resonant frequencies. We further verify our results numerically using COMSOL Multiphysics^® and introduce an experimental framework that assesses the effects of THz radiation on protein particles. We conclude that under controlled heating, protein molecules can serve as hotspots that impact thermally-gated ion channels. Through the presented work, we infer that the heating process can be engineered on different time and length scales by controlling the THz-EM signal input.