Comparison of FTIR spectrum with chemometric and machine learning classifying analysis for differentiating guan-mutong a nephrotoxic and carcinogenic traditional chinese medicine with chuan-mutong

Tan, Chu Shan; Leow, Shin Yee; Chen, Ying; Tan, Choo Jun; Yoon, Tiem Leong; Chen, Jingying; Yam, Mun Fei

doi:10.1016/j.microc.2020.105835

Cited by 27 publications

(16 citation statements)

References 12 publications

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“…The model was tested with a separate blinded data set with 90% accuracy. To further substantiate this modality, the authors used additional analysis with PLSDA, a machine learning approach widely used for spectroscopy. ,, As shown in Figure E, we were able to achieve detection of cancer directly from patient blood plasma with 100% sensitivity and 100% specificity. The combinatorial analysis of proteins as well as genomic features enabled us to tackle the complexity of blood plasma environment.…”

Section: Resultsmentioning

confidence: 94%

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

2022

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Liquid biopsy for determining the presence of cancer and the underlying tissue of origin is crucial to overcome the limitations of existing tissue biopsy and imaging-based techniques by capturing critical information from the dynamic tumor heterogeneity. A newly emerging liquid biopsy with extracellular vesicles (EVs) is gaining momentum, but its clinical relevance is in question due to the biological and technical challenges posed by existing technologies. The biological barriers of existing technologies include the inability to generate fundamental details of molecular structure, chemical composition as well as functional variations in EVs by gathering simultaneous information on multiple intra-EV molecules, unavailability of holistic qualitative analysis, in addition to the inability to identify tissue of origin. Technological barriers include reliance on EV isolation with a few labeled biomarkers, resulting in the inability to generate comprehensive information on the disease. A more favorable approach would be to generate holistic information on the disease without the use of labels. Such a marker-free diagnosis is impossible with the existing liquid biopsy due to the unavailability clinically validated cancer stem cells (CSC)-specific markers and dependence of existing technologies on EV isolation, undermining the clinical relevance of EV-based liquid biopsy. Here, CSC EVs were employed as an independent liquid biopsy modality. We hypothesize that tracking the signals of CSCs in peripheral blood with CSC EVs will provide a reliable solution for accurate cancer diagnosis, as CSC are the originators of tumor contributing to tumor heterogeneity. We report nanoengineered 3D sensors of extremely small nano-scaled probes self-functionalized for SERS, enabling integrative molecular and functional profiling of otherwise undetectable CSC EVs. A substantially enhanced SERS and ultralow limit of detection (10 EVs per 10 μL) were achieved. This was attributed to the efficient probe–EV interaction due to the 3D networks of nanoprobes, ensuring simultaneous detection of multiple EV signals. We experimentally demonstrate the crucial role of CSC EVs in cancer diagnosis. We then completed a pilot validation of this modality for cancer detection as well as for identification of the tissue of origin. An artificial neural network distinguished cancer from noncancer with 100% sensitivity and 100% specificity for three hard to detect cancers (breast, lung, and colorectal cancer). Binary classification to distinguish one tissue of origin against all other achieved 100% accuracy, while simultaneous identification of all three tissues of origin with multiclass classification achieved up to 79% accuracy. This noninvasive tool may complement existing cancer diagnostics, treatment monitoring as well as longitudinal disease monitoring by validation with a large cohort of clinical samples.

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Section: Resultsmentioning

confidence: 94%

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

2022

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“…1 indicates that the peak at 1637 cm À1 in the as-prepared sample is due to C]C in the aromatic ring. 39 The peaks at 1081 cm À1 and 882 cm À1 are respectively designated to the P-O bond and B-O bond, which clearly demonstrates that ultrathin organic polymers are connected by ether bonds. 40 The peak at 1251 cm À1 in the as-prepared sample is ascribed to the P]N bond of cyclotriphosphazene.…”

Section: Resultsmentioning

confidence: 95%

Integrating boron atoms into ultrathin organic polymer for visible light photocatalytic CO₂reduction to CH₄with nearly 100% selectivity

et al. 2022

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“…In 2021, Tan et al authored a research paper on the use of machine learning and FTIR spectroscopy to develop an efficient method of automated differentiation of the Chinese Herb Chuan-Mutong, widely known for its diuretic effects, from its look-alike herb Guan-Mutong [7]. The samples that composed the dataset in this study were acquired from 13 different states in both Malaysia and China, with a total of 164 samples of Mutong.…”

Section: Related Workmentioning

confidence: 99%

The Classification of FTIR Plastic Bag Spectra via Label Spreading and Stacking

Almanifi

Ng²,

Majeed³

2021

MEKATRONIKA

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Whereas plastics are a group of the most useful materials, widely used in all walks of life, the plastic waste that is produced daily poses a great threat towards wildlife and the planet as a whole. The use of biodegradable plastics is an important step in combating the plastic crisis. FTIR spectroscopy is a non-destructive method used for identifying different types of materials, however interpreting spectra produced by such spectrometers is both susceptible to human error, and time-consuming, not to mention that the industry suffers from a great of specialists, in the field of spectroscopy. Utilising machine learning as a method of filling the mentioned issue is suggested by this paper. Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. The pipelines studied seemed to show great predictivity at 100% classification accuracy acquired by the SVD-Stacked pipeline when data was sampled using an Agilent Cary 660 FTIR Spectrometer, and 99.18% by the same model when IDIR BP10 spectrometer was employed for sampling instead. The semi-supervised learning model (Label Spreading) seemed to achieve close enough accuracy at 99.82% in the case of the former dataset, and 97.54% for the latter, at a labelling rate of only 10% of the full datasets.

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Comparison of FTIR spectrum with chemometric and machine learning classifying analysis for differentiating guan-mutong a nephrotoxic and carcinogenic traditional chinese medicine with chuan-mutong

Cited by 27 publications

References 12 publications

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

Integrating boron atoms into ultrathin organic polymer for visible light photocatalytic CO₂reduction to CH₄with nearly 100% selectivity

The Classification of FTIR Plastic Bag Spectra via Label Spreading and Stacking

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Comparison of FTIR spectrum with chemometric and machine learning classifying analysis for differentiating guan-mutong a nephrotoxic and carcinogenic traditional chinese medicine with chuan-mutong

Cited by 27 publications

References 12 publications

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

Cancer Stem Cell Derived Extracellular Vesicles with Self-Functionalized 3D Nanosensor for Real-Time Cancer Diagnosis: Eliminating the Roadblocks in Liquid Biopsy

Integrating boron atoms into ultrathin organic polymer for visible light photocatalytic CO2reduction to CH4with nearly 100% selectivity

The Classification of FTIR Plastic Bag Spectra via Label Spreading and Stacking

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Integrating boron atoms into ultrathin organic polymer for visible light photocatalytic CO₂reduction to CH₄with nearly 100% selectivity