Recognition of chronic renal failure based on Raman spectroscopy and convolutional neural network

Gao, Rui; Yang, Bo; Chen, Cheng; Chen, Fangfang; Chen, Chen; Zhao, Deyi; Lv, Xiaoyi

doi:10.1016/j.pdpdt.2021.102313

Cited by 22 publications

(11 citation statements)

References 31 publications

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“…The generalization ability of CNN is also improved compared to traditional neural networks. Therefore, CNN combined with Raman spectroscopy is widely used in biological classification 20 , 21 .…”

Section: Introductionmentioning

confidence: 99%

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

Chen

et al. 2022

Sci Rep

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Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.

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Section: Introductionmentioning

confidence: 99%

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

Chen

et al. 2022

Sci Rep

Self Cite

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“…Therefore, we have used specialized preprocessing methods before using the DL model. The convolutional neural network (CNN) is one of the prominent DL classes that has become the norm over conventional chemometrics data analysis in Raman spectroscopy for classification and identification. − The basic layout and workflow of CNN are briefly explained in the Supporting Information. Many CNN architectures can be modified for application-specific use, and residual network (or ResNet) is the current state-of-the-art .…”

Section: Introductionmentioning

confidence: 99%

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

et al. 2022

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The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

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“…Chronic renal failure refers to chronic progressive renal parenchymal damage caused by various factors, resulting in irreversible atrophy of the kidneys [1]. The clinical symptoms of this disease mainly include retention of metabolites in the body, imbalance of water, electrolytes, and acid-base balance [2].…”

Section: Introductionmentioning

confidence: 99%

Controllable Fabrication of Molecularly Imprinted Microspheres with Nanoporous and Multilayered Structure for Dialysate Regeneration

Zhang

Liu

et al. 2022

Nanomaterials

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Adsorption of urea from dialysate is essential for wearable artificial kidneys (WRK). Molecularly imprinted microspheres with nanoporous and multilayered structures are prepared based on liquid–liquid phase separation (LLPS), which can selectively adsorb urea. In addition, we combine the microspheres with a designed polydimethylsiloxane (PDMS) chip to propose an efficient urea adsorption platform. In this work, we propose a formulation of LLPS including Tripropylene glycol diacrylate (TPGDA), ethanol, and acrylic acid (30% v/v), to prepare urea molecularly imprinted microspheres in a simple and highly controllable method. These microspheres have urea molecular imprinting sites on the surface and inside, allowing selective adsorption of urea and preservation of other essential constituents. Previous static studies on urea adsorption have not considered the combination between urea adsorbent and WRK. Therefore, we design the platform embedded with urea molecular imprinted microspheres, which can disturb the fluid motion and improve the efficiency of urea adsorption. These advantages enable the urea absorption platform and are highly promising for dialysate regeneration in WRK.

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Recognition of chronic renal failure based on Raman spectroscopy and convolutional neural network

Cited by 22 publications

References 31 publications

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

Controllable Fabrication of Molecularly Imprinted Microspheres with Nanoporous and Multilayered Structure for Dialysate Regeneration

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