An electrochemical DNA biosensor coupled with dual amplification strategy based on DNA-Au bio-bar codes and silver enhancement for highly sensitive and selective detection of MDR1 gene

Microchemical Journal, Journal Year: 2024, Volume and Issue: 197, P. 109889 - 109889

Published: Jan. 3, 2024

Language: Английский

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Breaking barriers in electrochemical biosensing using bioinspired peptide and phage probes

Analytical and Bioanalytical Chemistry, Journal Year: 2024, Volume and Issue: 416(30), P. 7225 - 7247

Published: April 19, 2024

Abstract Electrochemical biosensing continues to advance tirelessly, overcoming barriers that have kept it from leaving research laboratories for many years. Among them, its compromised performance in complex biological matrices due fouling or receptor stability issues, the limitations determining toxic and small analytes, use, conditioned commercial availability of receptors exploration natural molecular interactions, deserved be highlighted. To address these challenges, addition intrinsic properties electrochemical biosensing, coupling with biomimetic materials has played a fundamental role, among which bioinspired phage peptide probes stand out. The versatility design employment opened an unimaginable plethora possibilities improving their far beyond development highly sensitive selective devices. state art offers robust electroanalytical biotools, capable operating samples exciting opportunities discover determine targets regardless toxicity size, bioreceptors, prior knowledge interactions. With all this mind, review panoramic, novel, updated vision both tremendous advances offered by combination biosensors challenges efforts are envisioned immediate future. Graphical

Language: Английский

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Wearable technology for one health: Charting the course of dermal biosensing

Biosensors and Bioelectronics X, Journal Year: 2024, Volume and Issue: 19, P. 100500 - 100500

Published: May 23, 2024

Over the last decade, a significant paradigm shift has been observed towards leveraging less invasive biological fluids—such as skin interstitial fluid (ISF), sweat, tears, and saliva—for health monitoring. This evolution seeks to transcend traditional, blood-based methods, offering more accessible approach monitoring for non-specialized personnel. Skin ISF, with its profound resemblance blood, emerges pivotal medium real-time, minimally tracking of broad spectrum biomarkers, thus becoming an invaluable asset correlating data. Our exploration delves deeply into development wearable molecular biosensors, spotlighting dermal sensors their roles across both clinical everyday scenarios underscoring contributions holistic One Health initiative. In bringing forward myriad challenges that permeate this field, we also project future directions, notably potential ISF promising candidate continuous tracking. Moreover, paper aims catalyse further innovation by presenting curated selection seminal technological advancements. Amidst saturated landscape analytical literature on translational challenges, our distinctly highlight recent developments. attracting wider research groups versatile domain, endeavour broaden collective understanding trajectory potential, mapping biosensor technology. strategy not only illuminates transformative impact biosensors in reshaping diagnostics personalized medicine but fosters increased participation progress within field. Distinct from manuscripts review serves distillation key concepts, elucidating papers mark latest advancements sensors. Through collection landmark studies perspectives paths, guide new entrants area. We delineate division between epidermal subdermal sensors—focusing latter frontier—thereby establishing unique discourse ongoing narrative sensing technologies.

Language: Английский

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Comparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based receiver operating characteristic (ROC) curves, which can be limited accuracy complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic binary classification clinical samples, when using real-time electrochemical measurements. The was applied multimeric aptamer assay (RT-MAp) captures single-frequency (12.6 Hz) impedance during binding viral protein targets trimeric aptamers. collected from 172 COVID-19 saliva samples were processed through multiple nonlinear regression models extract nine key features transient These then used train three supervised ML algorithms─support vector (SVM), artificial neural network (ANN), random forest (RF)─using 75:25 training-testing ratio. Traditional ROC-based achieved an 83.6%, while ML-based significantly improved performance, with SVM, ANN, RF achieving accuracies 86.0%, 100%, respectively. ANN model demonstrated superior performance handling high-variance data, providing robust scalable solution improving point-of-care settings.

Language: Английский

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DNA Aptamer Targeting Zinc Transporters Zip10 and Zip6 on Cancer Cells

Published: Jan. 1, 2025

Language: Английский

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