arXiv subjects

Ethungshan Shitiri

Publications and source records attributed to Ethungshan Shitiri.

5 recordsLinked to original sources

arXiv2026

Whole-Blood Boundary Analysis of BioFET-Based ctDNA Detection for Intravascular Sensing in Intrabody Nanonetworks

Liquid biopsy can detect tumor-derived biomarkers such as circulating tumor DNA (ctDNA), but ultra-low-fraction assays remain costly, slow, and difficult to scale. This motivates interest in intravascular in vivo sensing in the context of intrabody nanonetworks, where nanosensors could support local biomarker monitoring. BioFET-based nanosensors are relevant here because they are label-free, highly miniaturizable, and have shown strong ctDNA sensitivity in controlled media. We examine whether this sensitivity still yields reliable ctDNA detection in whole blood using a reduced-order stochastic simulation model that links operating-point selection, Debye-screened charge transduction, stochastic finite-capacity binding, nonspecific adsorption, background fluctuations, and intrinsic electronic noise to blank-threshold detection. Monte Carlo evaluation with physiologically grounded parameters shows that short Debye length and several-nanometer charge-to-channel separation attenuate the current shift, while low-frequency noise and background fluctuations reduce the margin between target-present and blank responses. Under the tested quasi-static charge-gating regime, the simulated current shifts do not reliably exceed the blank-derived threshold at low ctDNA concentrations. The model therefore provides a whole-blood boundary analysis that identifies which interface configurations and operating conditions most strongly limit reliable BioFET-based intravascular ctDNA detection.

cs.ET↗

arXiv2026

Early-Stage Cancer Biomarker Detection via Intravascular Nanomachines: Modeling and Analysis

Early detection of cancer is essential for timely diagnosis and improved patient outcomes. Among emerging technologies, intra-body nanoscale communication offers an innovative solution to identify molecular cues within the human bloodstream. This study investigates a minimally invasive approach for early-stage cancer biomarker detection using nanomachines introduced into the bloodstream. To assess the feasibility of this approach, computational simulations are used to emulate the vascular environment and evaluate biomarker detection performance under different physiological conditions. Current modeling approaches often fail to capture essential vascular characteristics, including non-uniform flow structures, size-dependent particle mobility, and particle margination driven by red blood cell interactions. To address these limitations, our study incorporates these factors into the simulation framework and quantifies their individual and combined effects on biomarker detection efficiency. Baseline detection performance is first obtained under uniform flow assumptions, after which introducing realistic vascular transport mechanisms progressively reduces detection probability for all vessel types and nanomachine sizes. Among the considered vessels, capillary consistently achieves the highest detection probability across all nanomachine sizes.

cs.ET↗

arXiv2025

Set Transformer Architectures and Synthetic Data Generation for Flow-Guided Nanoscale Localization

Flow-guided Localization (FGL) enables the identification of spatial regions within the human body that contain an event of diagnostic interest. FGL does that by leveraging the passive movement of energy-constrained nanodevices circulating through the bloodstream. Existing FGL solutions rely on graph models with fixed topologies or handcrafted features, which limit their adaptability to anatomical variability and hinder scalability. In this work, we explore the use of Set Transformer architectures to address these limitations. Our formulation treats nanodevices' circulation time reports as unordered sets, enabling permutation-invariant, variable-length input processing without relying on spatial priors. To improve robustness under data scarcity and class imbalance, we integrate synthetic data generation via deep generative models, including CGAN, WGAN, WGAN-GP, and CVAE. These models are trained to replicate realistic circulation time distributions conditioned on vascular region labels, and are used to augment the training data. Our results show that the Set Transformer achieves comparable classification accuracy compared to Graph Neural Networks (GNN) baselines, while simultaneously providing by-design improved generalization to anatomical variability. The findings highlight the potential of permutation-invariant models and synthetic augmentation for robust and scalable nanoscale localization.

cs.ET↗

arXiv2024

Enhanced Drug Delivery via Localization-Enabled Relaying in Molecular Communication Nanonetworks

Intra-body nanonetworks hold promise for advancing targeted drug delivery (TDD) systems through molecular communications (MC). In the baseline MC-TDD system, drug-loaded nanomachines (DgNs) are positioned near the infected tissues to deliver drug molecules directly. To mitigate the decline in drug delivery efficiency caused by diffusion, we propose an enhanced MC-TDD system with a relay network. This network employs a novel localization-enabled relaying mechanism, where a nano-controller broadcasts a localization signal. DgNs then measure the received signal strength against thresholds to determine their clusters relative to the infected tissue. Additionally, our study considers the effect of multiple absorbing DgNs on the channel impulse response (CIR), a factor overlooked in previous works. Our approach improves drug delivery efficiency by $17\%$ compared to the baseline system. Importantly, we find that optimizing CIR is crucial for enhancing drug delivery efficiency. These findings pave the way for further research into optimizing CIR-based relay selection, as well as investigating the impact of factors such as drug molecule lifespan, obstruction probabilities, and flow dynamics.

cs.ET↗

arXiv2024

An $M$-ary Concentration Shift Keying with Common Detection Thresholds For Multitransmitter Molecular Communication

Concentration shift keying (CSK) is a widely studied modulation technique for molecular communication-based nanonetworks, which is a key enabler for the Internet of Bio-NanoThings (IoBNT). Existing CSK methods, while offering optimal error performance, suffer from increased operational complexity that scales poorly as the number of transmitters, $K$, grows. In this study, a novel $M$-ary CSK method is proposed: CSK with common detection thresholds (CSK-CT). CSK-CT uses \textit{common} thresholds, set sufficiently low to guarantee the reliable detection of symbols from all transmitters, regardless of distance. Closed-form expressions are derived to obtain the common thresholds and release concentrations. To further enhance error performance, the release concentration is optimized using a scaling exponent that also optimizes the common thresholds. The performance of CSK-CT is evaluated against the benchmark CSK across various $K$ and $M$ values. CSK-CT has an error probability between $10^{-7}$ and $10^{-4}$, which is a substantial improvement from that of the benchmark CSK (from $10^{-4}$ to $10^{-3}$). In terms of complexity, CSK-CT is $\mathcal{O}\big(n\big)$ and does not scale with $K$ but $M$ ($M\ll K$), whereas the benchmark is $\mathcal{O}\big(n^2\big)$. Furthermore, CSK-CT can mitigate inter-symbol interference (ISI), although this facet merits further investigation. Owing to its low error rates, improved scalability, reduced complexity, and potential ISI mitigation features, CSK-CT is particularly advantageous for IoBNT applications focused on data gathering. Its effectiveness is especially notable in scenarios where a computationally limited receiver is tasked with collecting vital health data from multiple transmitters.

cs.IT↗