Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Mo, Fan; Liu, Xiuwei; Yu, Xiaoming; Cui, Xiaoyu; Cai, Wensheng; Shao, Xueguang

doi:10.1007/s11426-016-0092-6

Cited by 9 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 1310 cm −1 (Amide III) and 1453 cm −1 (proteins/amino acids, lipids) regions were found using the PCA loadings plot (Figure S3B) connected with 1200–1500 cm −1 region [48]. The primary discriminating wavenumbers linked with proteins, amino acids, and lipids are 2873, 2929, and 2958 cm −1 , which were determined from the loadings plot of the ester area (2800–3080 cm −1 ; Figure S4B) [49]. The protein region's PCA loadings plot (Figure S5B) revealed the segregating wavenumbers 3284 and 3400 cm −1 .…”

Section: Resultsmentioning

confidence: 99%

Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier‐transform infrared spectroscopy combined with machine learning algorithms

Dawuti

Dou

Zheng

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data.Among all the bands, 1500-1700 cm À1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.

show abstract

Section: Resultsmentioning

confidence: 99%

Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier‐transform infrared spectroscopy combined with machine learning algorithms

Dawuti

Dou

Zheng

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

“…Fast and accurate diagnosis is crucial for treatment in clinical medicine. Fan et al (2017) aimed at developing a strategy for fast and cost-effective diagnosis of kidney disease using NIR spectra of human serum. Chen et al (2020b) introduced a second near infrared fluorescence imaging strategy based on lead sulfide quantum dots dynamically monitoring bacterial infection in a real-time manner.…”

Section: Biological Applicationmentioning

confidence: 99%

Near-Infrared Inorganic Nanomaterials for Precise Diagnosis and Therapy

Zhang

Liu

2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Traditional wavelengths (400–700 nm) have made tremendous inroads in vivo fluorescence imaging. However, the ability of visible light photon penetration hampered the bio-applications. With reduced photon scattering, minimal tissue absorption and negligible autofluorescence properties, near-infrared light (NIR 700–1700 nm) demonstrates better resolution, high signal-to-background ratios, and deep tissue penetration capability, which will be of great significance for in-vivo determination in deep tissue. In this review, we summarized the latest novel NIR inorganic nanomaterials and the emission mechanism including single-walled carbon nanotubes, rare-earth nanoparticles, quantum dots, metal nanomaterials. Subsequently, the recent progress of precise noninvasive diagnosis in biomedicine and cancer therapy utilizing near-infrared inorganic nanomaterials are discussed. In addition, this review will highlight the concerns, challenges and future directions of near-infrared light utilization.

show abstract

Chemometric Studies in Near-Infrared Spectroscopy

Han

Sun

et al. 2022

Sense the Real Change: Proceedings of the 20th International Conference on Near Infrared Spectroscopy

View full text Add to dashboard Cite

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Cited by 9 publications

References 37 publications

Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier‐transform infrared spectroscopy combined with machine learning algorithms

Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier‐transform infrared spectroscopy combined with machine learning algorithms

Near-Infrared Inorganic Nanomaterials for Precise Diagnosis and Therapy

Chemometric Studies in Near-Infrared Spectroscopy

Contact Info

Product

Resources

About