ABSTRACT
The prevalence of neglected tropical diseases (NTDs) is advancing at an alarming rate. The NTD leishmaniasis is now endemic in over 90 tropical and sub-tropical low socioeconomic countries. Current diagnosis for this disease involves serological assessment of infected tissue by either light microscopy, antibody tests, or culturing with in vitro or in vivo animal inoculation. Furthermore, co-infection by other pathogens can make it difficult to accurately determine Leishmania infection with light microscopy. Herein, for the first time, we demonstrate the potential of combining synchrotron Fourier-transform infrared (FTIR) microspectroscopy with powerful discrimination tools, such as partial least squares-discriminant analysis (PLS-DA), support vector machine-discriminant analysis (SVM-DA), and k-nearest neighbors (KNN), to characterize the parasitic forms of Leishmania major both isolated and within infected macrophages. For measurements performed on functional infected and uninfected macrophages in physiological solutions, the sensitivities from PLS-DA, SVM-DA, and KNN classification methods were found to be 0.923, 0.981, and 0.989, while the specificities were 0.897, 1.00, and 0.975, respectively. Cross-validated PLS-DA models on live amastigotes and promastigotes showed a sensitivity and specificity of 0.98 in the lipid region, while a specificity and sensitivity of 1.00 was achieved in the fingerprint region. The study demonstrates the potential of the FTIR technique to identify unique diagnostic bands and utilize them to generate machine learning models to predict Leishmania infection. For the first time, we examine the potential of infrared spectroscopy to study the molecular structure of parasitic forms in their native aqueous functional state, laying the groundwork for future clinical studies using more portable devices.
Subject(s)
Leishmania major , Leishmaniasis , Animals , Synchrotrons , Spectrophotometry, Infrared , Leishmaniasis/diagnosis , Macrophages/parasitologyABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.
Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Saliva/chemistry , Animals , Chlorocebus aethiops , Cohort Studies , Discriminant Analysis , Humans , Least-Squares Analysis , Monte Carlo Method , Point-of-Care Testing , Proof of Concept Study , SARS-CoV-2 , Sensitivity and Specificity , Specimen Handling , Spectrophotometry, Infrared , Vero CellsABSTRACT
New diagnostic tools that can detect malaria parasites in conjunction with other diagnostic parameters are urgently required. In this study, Attenuated Total Reflection Fourier transform infrared (ATR-FTIR) spectroscopy in combination with Partial Least Square Discriminant Analysis (PLS-DA) and Partial Least Square Regression (PLS-R) have been applied as a point-of-care test for identifying malaria parasites, blood glucose, and urea levels in whole blood samples from thick blood films on glass slides. The specificity for the PLS-DA was found to be 98% for parasitemia levels >0.5%, but a rather low sensitivity of 70% was achieved because of the small number of negative samples in the model. In PLS-R the Root Mean Square Error of Cross Validation (RMSECV) for parasite concentration (0-5%) was 0.58%. Similarly, for glucose (0-400 mg/dL) and urea (0-250 mg/dL) spiked samples, relative RMSECVs were 16% and 17%, respectively. The method reported here is the first example of multianalyte/disease diagnosis using ATR-FTIR spectroscopy, which in this case, enabled the simultaneous quantification of glucose and urea analytes along with malaria parasitemia quantification using one spectrum obtained from a single drop of blood on a glass microscope slide.
Subject(s)
Glucose/chemistry , Malaria/diagnosis , Plasmodium/cytology , Spectroscopy, Fourier Transform Infrared/methods , Urea/chemistry , Area Under Curve , Discriminant Analysis , Dried Blood Spot Testing , Glass/chemistry , Humans , Least-Squares Analysis , Plasmodium/chemistry , ROC CurveABSTRACT
The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.