Piercing the Shadows: Exploring the Influence of Signal Preprocessing on Interpreting Ultrasensitive Bioelectronic Sensor Data.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
37 patients with pancreatic precursor cyst lesions was analyzed.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
The findings revealed that standard signal preprocessing can produce misleading conclusions due to artifacts introduced by mathematical transformations. The study offers strategies to mitigate these effects, ensuring that data interpretation remains accurate and reflective of the underlying biochemical information in the samples.
The development of ultrasensitive electronic sensors for in vitro diagnostics is essential for the reliable monitoring of asymptomatic individuals before illness proliferation or progression.
APA
Caputo M, Sarcina L, et al. (2025). Piercing the Shadows: Exploring the Influence of Signal Preprocessing on Interpreting Ultrasensitive Bioelectronic Sensor Data.. ChemPlusChem, 90(2), e202400520. https://doi.org/10.1002/cplu.202400520
MLA
Caputo M, et al.. "Piercing the Shadows: Exploring the Influence of Signal Preprocessing on Interpreting Ultrasensitive Bioelectronic Sensor Data.." ChemPlusChem, vol. 90, no. 2, 2025, pp. e202400520.
PMID
39319362 ↗
Abstract 한글 요약
The development of ultrasensitive electronic sensors for in vitro diagnostics is essential for the reliable monitoring of asymptomatic individuals before illness proliferation or progression. These platforms are increasingly valued for their potential to enable timely diagnosis and swift prognosis of infectious or progressive diseases. Typically, the responses from these analytical tools are recorded as digital signals, with electronic data offering simpler processing compared to spectral and optical data. However, preprocessing electronic data from potentiometric biosensor arrays is still in its infancy compared to more established optical technologies. This study utilized the Single-Molecule with a Large Transistor (SiMoT) array, which has achieved a Technology Readiness Level of 5, to explore the impact of data preprocessing on electronic biosensor outcomes. A dataset consisting of plasma and cyst fluid samples from 37 patients with pancreatic precursor cyst lesions was analyzed. The findings revealed that standard signal preprocessing can produce misleading conclusions due to artifacts introduced by mathematical transformations. The study offers strategies to mitigate these effects, ensuring that data interpretation remains accurate and reflective of the underlying biochemical information in the samples.
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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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