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

Abdollah Rezagholi, Sergi Abadal, Filip Lemic, Eduard Alarcon, Ethungshan Shitiri

TL;DR

This study investigates a minimally invasive approach for early-stage cancer biomarker detection using nanomachines introduced into the bloodstream using computational simulations to emulate the vascular environment and evaluate biomarker detection performance under different physiological conditions.

Abstract

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.

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

TL;DR

This study investigates a minimally invasive approach for early-stage cancer biomarker detection using nanomachines introduced into the bloodstream using computational simulations to emulate the vascular environment and evaluate biomarker detection performance under different physiological conditions.

Abstract

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.
Paper Structure (12 sections, 7 equations, 8 figures, 3 tables)

This paper contains 12 sections, 7 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. General Scenario
  4. Flow Dynamics
  5. Size-Dependent Particle Movement
  6. Particle Margination
  7. Simulation Methodology
  8. Evaluation Framework
  9. Simulation Parameters
  10. Performance Metrics
  11. Results
  12. Conclusion

Figures (8)

  • Figure 1: Schematic representation of an intra-body nanonetworks for in-vivo nanosensing and connection to external world.
  • Figure 2: Overview of the system model. (a) Biomarker detection mechanism in a generic vessel. (b) Nanomachine velocity cofactor assignment. (c) Modeling of nanomachine margination.
  • Figure 3: Laminar microvessel model. (a) Three-dimensional laminar velocity field in a microvessel. (b) Velocity heat map of vessel cross section.
  • Figure 4: Detection probability versus number of nanomachines in (a) capillary, (b) venule, and (c) arteriole under uniform and laminar flow.
  • Figure 5: Detection probability as a function of nanomachine velocity cofactor in capillary under uniform and laminar flow, with $N$ fixed at 100.
  • ...and 3 more figures