3D plasmonic coral nanoarchitecture paper for label-free human urine sensing and deep learning-assisted cancer screening

Linh, Vo Thi Nhat; Lee, Min Young; Mun, Jungho; Kim, Yeseul; Kim, Hongyoon; Han, In Woong; Park, Sung‐Gyu; Choi, Samjin; Kim, Dongho; Rho, Junsuk; Jung, Ho Sang

doi:10.1016/j.bios.2023.115076

Cited by 21 publications

(10 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a technique is crucial for developing on-site urine diagnostic platforms and identifying unknown metabolite biomarkers for cancer detection. In a study conducted by Ling et al [ 92 ], the researchers applied an integrated on-site detection system based on SERS sensor technology and deep learning models to diagnose prostate and pancreatic cancer. The sensor is based on a 3D plasmonic coral nanoarchitecture (3D-PCN) synthesized on a paper substrate, which was integrated with a handheld Raman spectrometer to create an on-site diagnostic platform.…”

Section: Lab-on-a-chip In Cancer Detectionmentioning

confidence: 99%

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Kokabi,

Tahir,

Singh

et al. 2023

Biosensors

View full text Add to dashboard Cite

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

show abstract

Section: Lab-on-a-chip In Cancer Detectionmentioning

confidence: 99%

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Kokabi,

Tahir,

Singh

et al. 2023

Biosensors

View full text Add to dashboard Cite

show abstract

“…Human biofluid metabolomics, involving the measurement of all metabolites in body fluids, can instantly reflect the physiological status of human bodies [1][2][3] and has become an important tool for disease biomarker discovery. [4][5][6] Mass spec-trometry (MS) with distinctive detection specificity, sensitivity and a wide dynamic range has been recognized as a promising method to discover metabolic biomarkers in human biofluids for early screening, staging and prognosis evaluation of various diseases. [7][8][9] A recent development in the UPLC/MS method enabled urine metabolomics analysis in a fast manner.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical superstructure aerogels for in situ biofluid metabolomics

Chen,

Pu,

Wang

et al. 2024

Nanoscale

View full text Add to dashboard Cite

show abstract

“…Although some advances have been made with these methods in terms of sensitivity and stability, some inherent methodological drawbacks still limit the further dissemination of individual screening procedures in terms of rapid urine testing, such as time-consuming test procedures and pre-treatment steps, expensive instrumentation support, and the involvement of well-trained testers. [17,18] As the gold standard in pointof-care (POC) testing, colorimetric analysis enables rapid and cost-effective batch analysis in most scenarios, which has piqued the interest of analytical chemists. [19] The NeusJornet group pioneered the integration of sarcosine oxidase (SOx) and peroxidase in a silica reactor for fluorescence monitoring in the presence of amplex ultrared.…”

Section: Introductionmentioning

confidence: 99%

Enzyme‐Encapsulated Protein Trap Engineered Metal–Organic Framework‐Derived Biomineral Probes for Non‐Invasive Prostate Cancer Surveillance

Tang

Gong

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

A paper‐based naked‐eye recognition assay with enzyme‐encapsulated protein engineered metal–organic framework‐derived biominerals is developed for direct quantification of sarcosine in urine samples for screening of prostate cancer individuals. The detection strategy stems from the successful construction of a cascade response model, which involves the introduction of a cascade enzymatic catalytic reaction on Pt nanoparticles (NPs)‐loaded porous CeO2 by integrating a sarcosine oxidase as a special recognition unit and a chromogenic substrate as a signal molecule reporter. Pt NPs‐loaded CeO2 is subjected to a one‐step thermal treatment based on multilayered mesoporous Ce‐based metal–organic framework, and the calcined CeO2 exhibits the same distinct porous graded structure. Importantly, introduction of Pt NPs sharply enhances the peroxidase‐like activity of CeO2, which is considered to be caused by the difference in the adsorption behavior of hydrogen peroxide on the CeO2 surface and Pt/CeO2 obtained by density functional theory calculations. On the basis of this, the probe is used on a mass‐producible paper‐based working platform and 3D‐printed device to specifically screen for minor differences in sarcosine between urine samples from cancer patients and normal individuals. Enzyme‐assisted cascade catalytic reaction can be extended by replacing different recognition units for multiple analytes.

show abstract

3D plasmonic coral nanoarchitecture paper for label-free human urine sensing and deep learning-assisted cancer screening

Cited by 21 publications

References 73 publications

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Hierarchical superstructure aerogels for in situ biofluid metabolomics

Enzyme‐Encapsulated Protein Trap Engineered Metal–Organic Framework‐Derived Biomineral Probes for Non‐Invasive Prostate Cancer Surveillance

Contact Info

Product

Resources

About