Articles
Publication Date: 2026
Biosensors and Bioelectronics (18734235)292pp. 118078-118078
Early and accurate diagnosis of breast cancer, due to the complexity and cost of conventional methods, remains a major challenge. A new DNA computing-based detection processor is proposed for high-precision breast cancer diagnosis through miRNA biomarker analysis. RNA sequencing data of 1103 cancer and 104 standard samples from The Cancer Genome Atlas (TCGA) are analyzed through Differential Expression Gene (DEG) analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify the critical up-regulated and down-regulated miRNAs. The selected miRNAs are integrated into a logical processor framework by applying DNA strand displacement and logic gate design. The processor consists of an increasing detector for oncogenic miRNAs and a decreasing detector for tumor-suppressor miRNAs components. Simulation results indicate a robust performance, with positive and negative predictive values of 0.91 and 0.98, respectively. Experimental validation confirms the functionality of miRNAs (miR-200a and miR-141) representatives, which supports the feasibility of this proposed design. A novel, enzyme-free, scalable, and cost-effective DNA computing system for enhancing biomarker-based breast cancer detection is proposed here. Copyright © 2025 Elsevier B.V. All rights reserved.
Publication Date: 2025
European Physical Journal Plus (21905444)140(8)
Researchers and designers should face the challenges caused by memory and energy limitations. Quantum-dot Cellular Automata (QCA) offers a promising alternative with its high speed and low power consumption for dense emerging nano-electronic structures. Applying the approximate computing paradigm, where lower hardware complexity is prioritized over complete accuracy, can reduce power consumption. Integrating approximate computing with QCA reduces energy consumption and enhances system performance, although at the potential cost of reduced accuracy. The arithmetic unit is responsible for binary addition, subtraction, and multiplication. This article proposes a methodology for integrating QCA-based gates with approximate computing to achieve high-speed computation while minimizing resource usage. Additionally, it introduces a novel high-speed and cost-efficient design for a QCA-based approximate full adder, demonstrating improved hardware evaluation metrics, including delay, energy consumption, and acceptable error margins. The cost analysis indicates that the proposed design effectively balances circuit design trade-offs, particularly regarding delay and area. The functionality validation of the proposed circuit is assessed by the QCADesigner-E tool. Compared to the state of the art, the proposed design enhances performance metrics, achieving average improvements of 50% in delay, 26% in the number of QCA cells, and 78% in cost. These advancements are significant for the development of efficient and cost-effective QCA-based systems. Various error evaluation metrics assess the proposed approximate full adder's computational accuracy across three implementation scenarios of the 8-bit approximate adder architecture. Application-level simulation outputs show that the proposed circuits perform well in all scenarios, with the Peak-Signal-to-Noise Ratio (PSNR) exceeding 30 dB. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025.
