Raman spectroscopy on blood serum samples of patients with end-stage liver disease

Staritzbichler, René; Hunold, Pascal; Irina, Estrela-Lopis; Hildebrand, Peter W.; Isermann, Berend; Kaiser, Thorsten

doi:10.1371/journal.pone.0256045

Cited by 19 publications

(12 citation statements)

References 30 publications

(23 reference statements)

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“…Staritzbichler et al (26) analyzed 234 blood samples for 38 biomarkers using Raman spectroscopy at 785nm excitation. They included creatinine and cystatin C as markers of renal function and C-reactive protein (CRP) and interleukin 6 as biomarkers of inflammation.…”

Section: Discussionmentioning

confidence: 99%

Raman spectroscopy, used transcutaneously and non-invasively from a finger, to predict COVID-19: A feasibility, proof-of-concept study

Chefitz¹,

Birch

Yang³

et al. 2023

Preprint

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BACKGROUND A definitive COVID-19 infection typically is diagnosed by laboratory tests, including real-time, reverse-transcriptase Polymerase Chain Reaction (PCR)-based testing. These currently available COVID-19 tests require the patient to provide an extra-corporeal specimen and the results may not be immediate. Consequently, a variety of rapid antigen tests for COVID-19, all with a wide range of accuracy in terms of sensitivity and specificity, has proliferated (1,2). These rapid tests now represent a significantly larger proportion of all testing done for COVID-19, yet suffer from requiring a physical specimen from the nose or mouth and waiting 15 minutes for most. As a solution, we propose a non-invasive, trans-cutaneous, real-time viral detection device, based on the principles of Raman spectroscopy and machine learning. It does not require any extra-corporeal specimens and can be configured for self-administration. It can be easily used by non-experts and does not require medical training. Our approach suggests that our non-invasive, transcutaneous method may be broadly useful not only in COVID-19 diagnosis, but also in other diagnoses. METHODS 160 COVID positive (+) patients and 316 COVID negative (-) patients prospectively underwent nasal PCR testing concurrently with testing using our non-invasive, transcutaneous, immediate viral detector. Both the PCR and our experimental viral detector tests were performed side-by-side on outpatients (N=389) as well as inpatients (N= 87) at Holy Name Medical Center in Teaneck, NJ between June 2021 and August, 2022. The spectroscopic data were generated using an 830nm Raman System with SpectraSoft (W2 Innovations)and then, using machine learning, processed to provide an immediate prediction. A unique patient- interface for finger insertion enabled the application of Raman spectroscopy to viral detection in humans. RESULTS The data analysis algorithm demonstrates that there is an informative Raman spectrum output from the device, and that individual Raman peaks vary between cases and controls. Our proof-of-concept study yields encouraging results, with a specificity for COVID-19 of 0.75, and a sensitivity (including asymptomatic patients) of 0.80. CONCLUSIONS The combination of Raman spectroscopy, artificial intelligence, and our unique patient-interface admitting only a patient finger achieved test results of 0.75 specificity and 0.80 sensitivity for COVID-19 testing in this first in human proof-of-concept study. More significantly, the predictability improved with increasing data.

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Section: Discussionmentioning

confidence: 99%

Raman spectroscopy, used transcutaneously and non-invasively from a finger, to predict COVID-19: A feasibility, proof-of-concept study

Chefitz¹,

Birch

Yang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Their properties have made spectroscopy a leading technology not only in physical science but also in industries, medicine, art, etc. For example, Raman spectroscopy finds applications in everything: from medicine 8 , 9 , by allowing even cancer diagnosis 10 , to art and cultural heritage 11 , from food science to environmental and explosive monitoring 12 – 15 . Laser-Induced Breakdown Spectroscopy (LIBS) is also widely used to analyse the atomic composition of matter and recently, also due to the development of machine learning algorithms, it has been applied in several fields of science and industries 16 – 18 , such as food 19 , chemical 20 , pharmaceutical 20 , and medicine 20 .…”

Section: Introductionmentioning

confidence: 99%

Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning

Puleio

Rossi

Gaudio

2023

Sci Rep

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Calibration is a key part of the development of a diagnostic. Standard approaches require the setting up of dedicated experiments under controlled conditions in order to find the calibration function that allows one to evaluate the desired information from the raw measurements. Sometimes, such controlled experiments are not possible to perform, and alternative approaches are required. Most of them aim at extracting information by looking at the theoretical expectations, requiring a lot of dedicated work and usually involving that the outputs are extremely dependent on some external factors, such as the scientist experience. This work presents a possible methodology to calibrate data or, more generally, to extract the information from the raw measurements by using a new unsupervised physics-informed deep learning methodology. The algorithm allows to automatically process the data and evaluate the searched information without the need for a supervised training by looking at the theoretical expectations. The method is examined in synthetic cases with increasing difficulties to test its potentialities, and it has been found that such an approach can also be used in very complex behaviours, where human-drive results may have huge uncertainties. Moreover, also an experimental test has been performed to validate its capabilities, but also highlight the limits of this method, which, of course, requires particular attention and a good knowledge of the analysed phenomena. The results are extremely interesting, and this methodology is believed to be applied to several cases where classic calibration and supervised approaches are not accessible.

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“…Previous research suggests the advantage of Raman spectroscopy as a screening tool for a wide variety of biomarkers in serum. Precise predictions for the following markers could already be achieved for total bilirubin, conjugated bilirubin, unconjugated bilirubin, and total cholesterol, among others [13]. However, although much research is being conducted in this field, Raman spectroscopy is not yet incorporated into routine laboratory diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

Hunold

Fischer

Olthoff

et al. 2023

IJMS

Self Cite

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In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight days, stored one day at room temperature, and stored subsequently at 4 °C. The spectra were then used to train Convolutional Neural Networks (CNN) to predict the delay to sample examination. We achieved 90% accuracy for binary classification of the serum samples in the groups “without delay” versus “delayed”. Spectra recorded on the first day could be distinguished clearly from all subsequent measurements. Distinguishing between spectra taken in the range from the second to the last day seems to be possible as well, but currently, with an accuracy of approximately 70% only. Importantly, filtering out the fluorescent background significantly reduces the precision of detection.

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Raman spectroscopy on blood serum samples of patients with end-stage liver disease

Cited by 19 publications

References 30 publications

Raman spectroscopy, used transcutaneously and non-invasively from a finger, to predict COVID-19: A feasibility, proof-of-concept study

Raman spectroscopy, used transcutaneously and non-invasively from a finger, to predict COVID-19: A feasibility, proof-of-concept study

Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

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