A Near-Infrared "Matchbox Size" Spectrometer to Detect and Quantify Malaria Parasitemia

Point-of-care diagnosis of tissue fibrosis: a review of advances in vibrational spectroscopy with machine learning

Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by the need for additional stains, time, personnel and resources. Vibrational spectroscopy is a novel technique that offers an alternative atraumatic approach, with short scan times, while providing metabolic and morphological data. This review evaluates vibrational spectroscopy for the assessment of fibrosis, with a focus on point-of-care capabilities. OVID Medline, Embase and Cochrane databases were systematically searched using PRISMA guidelines for search terms including vibrational spectroscopy, human tissue and fibrosis. Studies were stratified based on imaging modality and tissue type. Outcomes recorded included tissue type, machine learning technique, metrics for accuracy and author conclusions. Systematic review yielded 420 articles, of which 14 were relevant. Ten of these articles considered mid-infrared spectroscopy, three dealt with Raman spectroscopy and one with near-infrared spectroscopy. The metrics for detecting fibrosis were Pearson correlation coefficients ranging from 0.65-0.98; sensitivity from 76-100%; specificity from 90-99%; area under receiver operator curves from 0.83-0.98; and accuracy of 86-99%. Vibrational spectroscopy identified fibrosis in myeloproliferative neoplasms in bone, cirrhotic and hepatocellular carcinoma in liver, end-stage heart failure in cardiac tissue and following laser ablation for acne in skin. It also identified interstitial fibrosis as a predictor of early renal transplant rejection in renal tissue. Vibrational spectroscopic techniques can therefore accurately identify fibrosis in a range of human tissues. Emerging data show that it can be used to quantify, classify and provide data about the nature of fibrosis with a high degree of accuracy with potential scope for point-of-care use.

Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning

BACKGROUND

Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination.

METHODS

We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection.

FINDINGS

Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively.

INTERPRETATION

These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies.

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis

Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning

Introduction

Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3-s scans using a handheld near-infrared-spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples.

Methods

We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors.

Results

Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20-60) and mature fibrosis of 30% (10%-50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%-15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial-least square regression machine learning, this study predicted the percentage of both immature (R 2 = 0.842) and mature (R 2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively).

Conclusion

This study demonstrates that a point-of-care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

Near-infrared spectroscopy for structural bone assessment

Aims

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds.

Methods

A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp).

Results

NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R² = 0.91, outer R² = 0.83), thickness (Tb.Th, inner R² = 0.9, outer R² = 0.79), and cortical thickness (Ct.Th, inner and outer both R² = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality.

Conclusion

We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use.

Cite this article: Bone Jt Open 2023;4(4):250–261.

Malaria absorption peaks acquired through the skin of patients with infrared light can detect patients with varying parasitemia

To eliminate malaria, scalable tools that are rapid, affordable, and can detect patients with low parasitemia are required. Non-invasive diagnostic tools that are rapid, reagent-free, and affordable would also provide a justifiable platform for testing malaria in asymptomatic patients. However, non-invasive surveillance techniques for malaria remain a diagnostic gap. Here, we show near-infrared Plasmodium absorption peaks acquired non-invasively through the skin using a miniaturized hand-held near-infrared spectrometer. Using spectra from the ear, these absorption peaks and machine learning techniques enabled non-invasive detection of malaria-infected human subjects with varying parasitemia levels in less than 10 s.

A Combined Near-Infrared and Mid-Infrared Spectroscopic Approach for the Detection and Quantification of Glycine in Human Serum

Serum is an important candidate in proteomics analysis as it potentially carries key markers on health status and disease progression. However, several important diagnostic markers found in the circulatory proteome and the low-molecular-weight (LMW) peptidome have become analytically challenging due to the high dynamic concentration range of the constituent protein/peptide species in serum. Herein, we propose a novel approach to improve the limit of detection (LoD) of LMW amino acids by combining mid-IR (MIR) and near-IR spectroscopic data using glycine as a model LMW analyte. This is the first example of near-IR spectroscopy applied to elucidate the detection limit of LMW components in serum; moreover, it is the first study of its kind to combine mid-infrared (25-2.5 μm) and near-infrared (2500-800 nm) to detect an analyte in serum. First, we evaluated the prediction model performance individually with MIR (ATR-FTIR) and NIR spectroscopic methods using partial least squares regression (PLS-R) analysis. The LoD was found to be 0.26 mg/mL with ATR spectroscopy and 0.22 mg/mL with NIR spectroscopy. Secondly, we examined the ability of combined spectral regions to enhance the detection limit of serum-based LMW amino acids. Supervised extended wavelength PLS-R resulted in a root mean square error of prediction (RMSEP) value of 0.303 mg/mL and R² value of 0.999 over a concentration range of 0-50 mg/mL for glycine spiked in whole serum. The LoD improved to 0.17 mg/mL from 0.26 mg/mL. Thus, the combination of NIR and mid-IR spectroscopy can improve the limit of detection for an LMW compound in a complex serum matrix.

Visible microspectrophotometry coupled with machine learning to discriminate the erythrocytic life cycle stages of P. falciparum malaria parasites in functional single cells

Visible microspectroscopy combined with machine learning is able to detect and quantify functional malaria infected erythrocytes at different stages of the P. falciparum erythrocytic life cycle.

Ultraviolet/Visible and Near-Infrared Dual Spectroscopic Method for Detection and Quantification of Low-Level Malaria Parasitemia in Whole Blood

The scourge of malaria infection continues to strike hardest against pregnant women and children in Africa and South East Asia. For global elimination, testing methods that are ultrasensitive to low-level ring-staged parasitemia are urgently required. In this study, we used a novel approach for diagnosis of malaria infection by combining both electronic ultraviolet-visible (UV/vis) spectroscopy and near infrared (NIR) spectroscopy to detect and quantify low-level (1-0.000001%) ring-staged malaria-infected whole blood under physiological conditions uisng Multiclass classification using logistic regression, which showed that the best results were achieved using the extended wavelength range, providing an accuracy of 100% for most parasitemia classes. Likewise, partial least-squares regression (PLS-R) analysis showed a higher quantification sensitivity (R² = 0.898) for the extended spectral region compared to UV/vis and NIR (R² = 0.806 and 0.556, respectively). For quantifying different-stage blood parasites, the extended wavelength range was able to detect and quantify all thePlasmodium falciparum accurately compared to testing each spectral component separately. These results demonstrate the potential of a combined UV/vis-NIR spectroscopy to accurately diagnose malaria-infected patients without the need for elaborate sample preparation associated with the existing mid-IR approaches.

Detection and Identification of Wolbachia pipientis Strains in Mosquito Eggs Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy

The global fight against mosquito-borne viral diseases has in recent years been bolstered by the introduction of the endosymbiotic bacteria Wolbachia to vector populations, which in host mosquitoes suppresses the transmissibility of several viruses. Researchers engaged on this front of the battle often need to know the Wolbachia infection status of individual mosquitoes, as the intervention progresses and the mosquitoes become established in the target population. Previously, we successfully applied attenuated total reflection Fourier transform infrared spectroscopy to the detection of Wolbachia in adult Aedes aegypti mosquitoes; here we apply the same principles to Aedes eggs, with sensitivity and selectivity > 0.95. Further, we successfully distinguish between infections in eggs of the wMel and wMelPop strains of Wolbachia pipientis, with a classification error of 3%. The disruption of host lipid profile by Wolbachia is found to be a key driver in spectral differences between these sample classes.