Harnessing a Self‐Regenerated Hybridization Circuit for Differentiating Heart Failure Patients of Varied Severity

Heart failure (HF) is a globally threatening cardiovascular disease associated with poor quality of life and high mortality, therefore, timely diagnosis and risk prediction for HF disease are urgently needed. Herein, a compact yet robust self-regenerated hybridization circuit (SHC) aptasensor is developed for the amplified detection of N-terminal pro-brain natriuretic peptide (NT-proBNP), a "gold standard biomarker" for HF. The aptamer transduction module can specifically recognize NT-proBNP, thus initiating the cascade hybridization reaction with the successively self-regenerated triggers that reversely initiate the cross-hybridization reaction. Benefiting from the aptamer-specific recognition and the self-replicated signal amplification, the robust SHC aptasensor demonstrated a more impressive diagnostic performance for HF in elderly patients than the clinical fluorescence immunochromatography assay (FICA) in terms of positive predictive value (21 vs 17), specificity (39 vs 32), and diagnostic accuracy (37 vs 36). Furthermore, this approach allows for differentiation among HF patients with varying disease severities, achieving a sufficiently high accuracy of 78.3%, thus facilitating more timely and accurate therapeutic intervention. The versatile and reliable SHC system offers a new approach to analyzing low-abundance biomarkers from clinical rare specimens, which is highly important for early disease diagnosis and prognosis assessment.

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Publication Details

Journal
Small
Published
2025-02-05
DOI
https://doi.org/10.1002/smll.202408384
Citations
2
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
1.61

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article

Harnessing a Self‐Regenerated Hybridization Circuit for Differentiating Heart Failure Patients of Varied Severity

Qinglian Liu, Fuan Wang, Jianglin Wu, Jiajia Zhang et al.
2 citations
Small
Advanced biosensing and bioanalysis techniques
1.61
article

Harnessing a Self‐Regenerated Hybridization Circuit for Differentiating Heart Failure Patients of Varied Severity

Qinglian Liu, Fuan Wang, Jianglin Wu, Jiajia Zhang, Pu Zhang, Xue Gong, Yi Wang, Zhongwei Jiang
article en
2 citations

Abstract

Heart failure (HF) is a globally threatening cardiovascular disease associated with poor quality of life and high mortality, therefore, timely diagnosis and risk prediction for HF disease are urgently needed. Herein, a compact yet robust self-regenerated hybridization circuit (SHC) aptasensor is developed for the amplified detection of N-terminal pro-brain natriuretic peptide (NT-proBNP), a "gold standard biomarker" for HF. The aptamer transduction module can specifically recognize NT-proBNP, thus initiating the cascade hybridization reaction with the successively self-regenerated triggers that reversely initiate the cross-hybridization reaction. Benefiting from the aptamer-specific recognition and the self-replicated signal amplification, the robust SHC aptasensor demonstrated a more impressive diagnostic performance for HF in elderly patients than the clinical fluorescence immunochromatography assay (FICA) in terms of positive predictive value (21 vs 17), specificity (39 vs 32), and diagnostic accuracy (37 vs 36). Furthermore, this approach allows for differentiation among HF patients with varying disease severities, achieving a sufficiently high accuracy of 78.3%, thus facilitating more timely and accurate therapeutic intervention. The versatile and reliable SHC system offers a new approach to analyzing low-abundance biomarkers from clinical rare specimens, which is highly important for early disease diagnosis and prognosis assessment.

SmallVol. 21(9)
Chongqing Normal University (CN), Wuhan University (CN), Zhongnan Hospital of Wuhan University (CN), Chongqing Medical University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Chongqing
Good health and well-being
Openalex Percentile: Top 19%
Advanced biosensing and bioanalysis techniques
1.61
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