Rapid Detection of Clostridium difficile Toxins in Stool by Raman Spectroscopy

Koya, S. Kiran; Yurgelevic, Sally; Brusatori, Michelle; Huang, Changhe; Diebel, Lawrence N.; Auner, Gregory W.

doi:10.1016/j.jss.2019.06.039

Cited by 17 publications

(20 citation statements)

References 21 publications

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“… Mean Raman Spectra: Mean Raman spectra for unspiked stool, TcdA-spiked stool and TcdB-spiked stool with respective standard deviation for 1 ng/mL (top), 100 pg/mL (second from top), 1 pg/mL (third from top) and 0.1 pg/mL (bottom) were plotted on x -axis for Raman shift 300–3200/cm and their intensities in arbitrary units on y -axis [ 119 ]. …”

Section: Figurementioning

confidence: 99%

“…Fecal material expels naturally; therefore, collection is quick, minimally invasive, and can be performed by the patient privately, rather than by a healthcare team member. To date, this biofluid has been used to diagnose steatorrhea, Clostridium difficile (CD) infections, gastrointestinal disorders, and various other diseases using fecal fat [ 118 , 119 , 120 ]. FTIR analysis of fecal fat was compared to the traditional collection method of fecal fat as a diagnostic method for steatorrhea [ 119 ].…”

Section: Introductionmentioning

confidence: 99%

“…To date, this biofluid has been used to diagnose steatorrhea, Clostridium difficile (CD) infections, gastrointestinal disorders, and various other diseases using fecal fat [ 118 , 119 , 120 ]. FTIR analysis of fecal fat was compared to the traditional collection method of fecal fat as a diagnostic method for steatorrhea [ 119 ]. The study identified FTIR as a rapid, reliable, and simple technique for diagnosing and monitoring steatorrhea with adequate precision compared to the traditional method.…”

Section: Introductionmentioning

confidence: 99%

“…Further analysis of fecal material with Near-Infrared Reflectance Spectroscopy (NIRS) demonstrated the ability to reveal the chemical composition of feces [ 120 ]. Recently, Koya et al prepared stool samples with CD toxin A (TxA) and CD toxin B (TxB) to assess the specificity and sensitivity of Raman spectroscopy in stool compared to the traditional CD testing technique [ 119 ]. The researchers found Raman to be moderately to highly sensitive with low to moderate specificity in this study.…”

Section: Introductionmentioning

confidence: 99%

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Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

et al. 2020

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Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical “fingerprint” with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

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Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

et al. 2020

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“…IR and Raman spectroscopy) is gaining considerable interest as an alternative technique in the classification of clinically relevant microorganisms due to its rapid, objective, nondestructive, and cost-effective nature [6,[76][77][78][79][80][81]. Although traditional ML techniques have been widely applied for spectral data analysis [8,[98][99][100][101][102], fewer efforts have been made in developing neural networks and deep learning algorithms [103][104][105]. Lasch et al [106] presented Fourier transform-IR (FT-IR) spectroscopy hyperspectral imaging in combination with a hierarchical system of ANNs for rapid and highly accurate identification of Gram-positive (S. aureus, S. epidermidis, B. cereus, B. subtilis, and E. faecalis) and Gramnegative bacteria (E. coli, P. aeruginosa, and C. freundii).…”

Section: Detection and Identification Of Microorganismsmentioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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Development of an Open‐Access and Explainable Machine Learning Prediction System to Assess the Mortality and Recurrence Risk Factors of Clostridioides Difficile Infection Patients

Lee

et al. 2020

Advanced Intelligent Systems

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Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence facilitates prevention, timely treatment, and improves clinical outcomes. The aim herein is to establish an open‐access web‐based prediction system, which estimates CDI patients’ mortality and recurrence outcomes and explains machine learning prediction with patients’ characteristics. Prognostic models are developed using four various types of machine learning algorithms and the statistical logistics regression model utilizing over 15 000 CDI patients from 41 hospitals in Hong Kong. The boosting‐based machine learning algorithm gradient boosting machine (GBM) (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperforms statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. As the difficulty to interpret complex machine learning results limits their use in the medical area, Shapley additive explanations (SHAP) are adapted to identify which features are crucial to the machine learning models and associate them with clinical findings. SHAP analysis shows that older age, reduced albumin levels, higher creatinine levels, and higher white blood cell count are the most highly associated mortality features, which is consistent with existing clinical findings. The open‐access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/.

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Rapid Detection of Clostridium difficile Toxins in Stool by Raman Spectroscopy

Cited by 17 publications

References 21 publications

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Recent evolutions of machine learning applications in clinical laboratory medicine

Development of an Open‐Access and Explainable Machine Learning Prediction System to Assess the Mortality and Recurrence Risk Factors of Clostridioides Difficile Infection Patients

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