Rapid label-free analysis of Opisthorchis viverrini eggs in fecal specimens using confocal Raman spectroscopy

Chuchuen, Oranat; Thammaratana, Thani; Sanpool, Oranuch; Rodpai, Rutchanee; Maleewong, Wanchai; Intapan, Pewpan M.

doi:10.1371/journal.pone.0226762

Cited by 4 publications

(1 citation statement)

References 19 publications

(21 reference statements)

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“…Therefore, it has been employed to detect and quantitatively measure small-molecule drugs in various pharmaceutical and biological matrices [13][14][15]. Raman spectroscopy can also identify biomolecules such as proteins, nucleic acids, lipids, and biomarkers, which are important for detecting the biochemical alterations in cells, tissues, or biofluids of various diseases [16][17][18]. It has been used to investigate and identify microbial phenotypic changes, such as detecting phenotypic differences in Escherichia coli enriched for 1-butanol tolerance [19], developing phenotypic profiles of Escherichia coli for antibiotic drug development research [20], and isolating carotenoid-containing cells using single-cell genomics based on Raman sorting [21].…”

Section: Introductionmentioning

confidence: 99%

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Suksuratin

Rodpai²,

Luvira³

et al. 2022

PLoS ONE

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Cholangiocarcinoma (CCA) is highly prevalent in the northeastern region of Thailand. Current diagnostic methods for CCA are often expensive, time-consuming, and require medical professionals. Thus, there is a need for a simple and low-cost CCA screening method. This work developed a rapid label-free technique by Raman spectroscopy combined with the multivariate statistical methods of principal component analysis and linear discriminant analysis (PCA-LDA), aiming to analyze and classify between CCA (n = 30) and healthy (n = 30) serum specimens. The model’s classification performance was validated using k-fold cross validation (k = 5). Serum levels of cholesterol (548, 700 cm-1), tryptophan (878 cm-1), and amide III (1248,1265 cm-1) were found to be statistically significantly higher in the CCA patients, whereas serum beta-carotene (1158, 1524 cm-1) levels were significantly lower. The peak heights of these identified Raman marker bands were input into an LDA model, achieving a cross-validated diagnostic sensitivity and specificity of 71.33% and 90.00% in distinguishing the CCA from healthy specimens. The PCA-LDA technique provided a higher cross-validated sensitivity and specificity of 86.67% and 96.67%. To conclude, this work demonstrated the feasibility of using Raman spectroscopy combined with PCA-LDA as a helpful tool for cholangiocarcinoma serum-based screening.

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Section: Introductionmentioning

confidence: 99%

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Suksuratin

Rodpai²,

Luvira³

et al. 2022

PLoS ONE

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Cholangiocarcinoma

Brindley

Bachini

Ilyas

et al. 2021

Nat Rev Dis Primers

443

315

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Cholangiocarcinoma (CCA) is a highly lethal, epithelial cell malignancy that occurs anywhere along the biliary tree and/or within the hepatic parenchyma. CCA displays features of cholangiocyte differentiation and probably arises predominantly from the epithelial cells lining the bile ducts, which are termed cholangiocytes; however, the cancers may also develop from peribiliary glands and hepatocytes, depending on the underlying liver disease and location [1][2][3][4] . These cancers are heterogeneous and are best classified according to the primary, anatomic subtype as intrahepatic CCA (iCCA), perihilar CCA (pCCA) or distal CCA (dCCA) 5,6 (Fig. 1). iCCA is located proximally to the second-order bile ducts within the liver parenchyma, pCCA is localized between the second-order bile ducts and the insertion of the cystic duct into the common bile duct, and dCCA is confined to the common bile duct below the cystic duct insertion. The true incidence of pCCA and iCCA is unclear owing to the extensive misclassification of pCCA as iCCA in national databases 6,7 . In addition, enhanced diagnostic capabilities have enabled increased clinical distinction between carcinoma of unknown primary and iCCA 8,9 . These factors have, in part, contributed to the reported increase in incidence of iCCA over the past two or three decades. Each of the anatomic subtypes is characterized by unique genetic aberrations, clinical presentations and management options 10 . However, many databases categorize both pCCA and dCCA as extrahepatic CCA. Most CCAs are adenocarcinomas and other histological subtypes, such as adenosquamous carcinoma or clear cell carcinoma, are encountered rarely 11 . These cancers are highly desmoplastic and are enmeshed in dense networks of inflammatory cells and matrix termed the tumour immune microenvironment [12][13][14] .The epidemiology of these cancers varies worldwide. Infections with specific trematodes (flatworm parasites, commonly called flukes) are a major cause of CCA in some regions. For example, in Southeast Asia, the liver fluke Opisthorchis viverrini is the leading cause of CCA 15 . CCA occurring secondary to fluke infestation can arise anywhere within the biliary tree and present as any one of the three anatomic subsets. Fluke-related CCA may have a specific pathogenesis, especially genetic aberrations, but the diagnosis and management are not different from non-fluke-related CCA. In the Western world, most patients with CCA do not have an identifiable risk factor, except for some with primary sclerosing cholangitis (PSC) 7,10 . Further insights into the epidemiology, risk factors and biology of CCA are needed to improve its prevention and therapy.In this Primer, we discuss the epidemiology and pathophysiological mechanisms of liver-fluke-related and non-liver-fluke-related CCA and associated risk factors and summarize diagnosis and management of C holangiocarcinoma

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An Approach for Egg Parasite Classification Based on Ensemble Deep Learning

Butploy,

Kanarkard,

Intapan

et al. 2023

JACIII

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Opisthorchis viverrini and minute intestinal fluke (MIF) infections are heavily epidemic in northeastern Thailand. Their primary cause is eating raw or undercooked cyprinid fishes, and they cause health problems in the human digestive system. In cases of liver fluke, these parasites can go through the bile duct system, which may cause cholangiocarcinoma (bile duct cancer). When a medical doctor suspects that a patient is infected with parasites, they typically request a stool analysis to determine the type of egg parasites using microscopy. Both parasites have similar characteristics, thus, it is necessary for a specialist to identify the specific type of egg parasites present. Many automatic systems have been developed using deep learning to assist doctors in diagnosing the type of egg parasite. In this study, we proposed three models of deep learning architectures and created voting ensembles to analyze egg parasite images. Images of similar liver fluke eggs and MIF eggs were taken from the Parasitology Laboratory, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand. Image data augmentation is used to expand images from different perspectives and assist the system in acquiring a greater variety of images. Three models performed effectively, by employing the hard voting ensemble, the accuracy increased to 86.67%, while for the second group, the accuracies reached 68.00%, 76.00%, and 77.33%, respectively. Using the soft voting ensemble, the accuracy improved to 79.33%. These outcomes highlight the potential of ensemble deep learning in image classification. Furthermore, these results align closely with those achieved by several experts in image classification. Hence, a promising ensemble approach can aid doctors in accurately classifying images of egg parasites.

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Rapid label-free analysis of Opisthorchis viverrini eggs in fecal specimens using confocal Raman spectroscopy

Cited by 4 publications

References 19 publications

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Cholangiocarcinoma

An Approach for Egg Parasite Classification Based on Ensemble Deep Learning

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