• Advances in nanopore sensing:...

*Result*: Advances in nanopore sensing: Signal processing prospects for the future.

Title:
Advances in nanopore sensing: Signal processing prospects for the future.
Authors:
Das N; Department of Electronics and Communication Engineering Department, Birla Institute of Technology, Ranchi, Patna Campus, Bihar, India. Electronic address: narentitun@gmail.com., Jhalani A; Department of Electronics and Communication Engineering Department, Birla Institute of Technology, Ranchi, Patna Campus, Bihar, India.
Source:
Advances in clinical chemistry [Adv Clin Chem] 2026; Vol. 130, pp. 205-234. Date of Electronic Publication: 2025 Nov 25.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Academic Press Country of Publication: United States NLM ID: 2985173R Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2162-9471 (Electronic) Linking ISSN: 00652423 NLM ISO Abbreviation: Adv Clin Chem Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, Ny : Academic Press
MeSH Terms:
Nanopores* , Biosensing Techniques*/methods , Signal Processing, Computer-Assisted*, Humans ; Machine Learning ; Biomarkers/analysis
Contributed Indexing:
Keywords: Analyte identification; Feature extraction; Machine learning; Nanopore sensing; Signal processing algorithm
Substance Nomenclature:
0 (Biomarkers)
Entry Date(s):
Date Created: 20260122 Date Completed: 20260122 Latest Revision: 20260122
Update Code:
20260130
DOI:
10.1016/bs.acc.2025.10.011
PMID:
41571381
Database:
MEDLINE

*Further Information*

*Nanopore sensing has developed as a revolutionary analytical tool in clinical chemistry, which facilitates fast, label-free analysis of biomolecules from nucleic acids through proteins and small metabolites. Its potential for converting molecular interaction into measurable ionic current fingerprints makes real-time, high-resolution analysis of clinically relevant targets possible in complex biological matrices. In this chapter, we discuss the changing scene of signal processing methods that augment the diagnostic potential of nanopore platforms. The focus areas are reduction of noise, extraction of features, and integration of machine learning for precise biomarker identification under physiologically noisy environments. The chapter also mentions advances in real-time processing essential for point-of-care diagnostics, such as the adoption of edge AI and application-specific integrated circuits (ASICs). Finally, we present the future application of quantum computing and multimodal sensing in pushing nanopore-based clinical diagnostics forward.
(Copyright © 2026. Published by Elsevier Inc.)*