Monitoring glucose levels in urine using FTIR spectroscopy combined with univariate and multivariate statistical methods

Unlocking body-surface physiological evolution via IR-temperature dual sensing with single chalcogenide fiber

Improvements to body-surface physiological monitoring ability including real-time, accuracy and integration, are essential to meet the expansive demands for personal healthcare. As part of this, simultaneous monitoring of sweat metabolites and body temperature offers an exciting path to maximizing diagnostic precision and minimizing morbidity rates. Herein, we report a high-performance biomarker-temperature sensor made of a single As3Se5Te2 chalcogenide glass fiber to monitor physiology evolution on body-surface. The sensor integrates efficient thermal resistance and fiber evanescent wave effects, permitting the independent sensing of temperature and biomarkers with an ultrahigh temperature coefficient of resistance (-5.84% K-1), rapid temperature response (0.3 s) and excellent IR sensing sensitivity. Moreover, by attaching a fiber to the wrist, we demonstrate simultaneous observation of both sweat metabolite (urea and lactate) and temperature changes during exercise. This illuminating sensing method will provide crucial capabilities in physiological monitoring and pave the way for advanced personalized diagnostic.

A portable and ecological paper-based device for glucose monitoring in peripheral blood mononuclear cell lysates

The increasing need for point-of-care (POC) testing has prompted a rise in the popularity of affordable biosensors that are eco-friendly, especially paper-based electrochemical sensors. This research introduces a biodegradable paper-based enzymatic biosensor for detecting glucose levels in intricate biological samples, such as cell lysates. This biosensor uses Prussian Blue (PB) as a mediator and glucose oxidase to detect glucose with excellent accuracy using direct electrochemical signals. Screen printing using Whatman filter paper produced a better biosensor than other substrates. The PB concentration of 12.5 mmol L-1 was found to be optimal and resulted in an operating potential of -0.1 V, which helped decrease interference from other active substances and improved its selectivity. Calibration was found to be linear up to a concentration of 2 mmol L-1 with a detection limit of 40 μmol L-1 and a limit of quantification of 120 μmol L-1. Moreover, experiments performed on cell lysates obtained from peripheral blood mononuclear cells (PBMCs) suggest the possible application of biosensors to measure glucose levels in vitro in both stimulated and unstimulated cells. This feature underscores its promise for use in monitoring metabolism and conducting diagnostic applications. The paper-based biosensor is an alternative to the current platform for the development of an eco-friendly, portable glucose-sensitive biosensor for point-of-care monitoring of glucose. Its flexibility and efficiency make it a strong candidate for use in the field of POC diagnostics, especially in areas of limited resources and in conditions where there is a problem with glucose dysregulation including diabetes and other related metabolic disorders.

Fourier Transform Infrared (FTIR) Spectroscopy as a Tool to Characterize Exercise and Physical Activity: A Systematic Review

BACKGROUND

Over the past few decades, the scientific community has recognized the impact of physical activity on health and performance. In parallel, researchers have been actively exploring novel methodologies to analyze the physiological and metabolic responses to exercise. Fourier transform infrared spectroscopy has emerged as a powerful tool in this effort, offering the potential to provide unique insights into exercise-related changes at the molecular level.

OBJECTIVE

The primary goal of this systematic review is to confirm the viability of utilizing Fourier transform infrared spectroscopy for the analysis of the biochemical changes associated with physical exercise and its potential applications.

METHODS

This systematic review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and examined studies employing Fourier transform infrared spectroscopy to analyze exercise and physical activity, focusing on a biological sample collection and spectral analysis. Four databases (PubMed, SPORTDiscus, Web of Science, and Scopus) were searched, and inclusion criteria encompassed original English-language studies involving human participants aged 18-50 years, a biological sample collection (urine, saliva, and blood), and the use of Fourier transform infrared spectroscopy. The studies were analyzed considering the type of exercise or sport that was investigated, and also the type of spectral analysis conducted.

RESULTS

The review encompassed 15 studies that demonstrated the versatility of Fourier transform infrared spectroscopy in assessing various aspects of exercise, including metabolism, cardiovascular responses, and muscular fatigue. The largest study evaluated 57 athletes from several different sports. On average, almost all the studies were performed with around 20 athletes. Notably, the technique's holistic approach allows for a comprehensive analysis of the complex network of metabolites and proteins within the human body. Data analysis methodologies, particularly when coupled with machine learning, show great potential for advancing the field of sports science.

CONCLUSIONS

Fourier transform infrared spectroscopy emerges as a promising tool for monitoring and enhancing the performance of high-level athletes, preventing overtraining or even over-reaching, and assessing metabolism. Its accuracy, efficiency, and affordability also make it a candidate for broader applications in assessing the health and fitness of the general population. Future research should explore its applicability across diverse exercise modalities and demographic groups, aiming to prescribe exercise plans that consider a multitude of parameters for larger, more intricate exercise cohorts.

CLINICAL TRIAL REGISTRATION

The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the ID number CRD42023441965.

Simultaneous determination of glucose and albumin in human urine using attenuated total reflection Fourier-transform infrared spectroscopy

The ability to quantify albuminuria and glucose is important in identifying conditions such as cardiovascular disease (CVD), chronic kidney disease (CKD), and diabetes. This study utilized Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy to analyze aqueous urine samples spiked with bovine serum albumin (BSA) and glucose at different concentrations. The aim was to determine the limit of detection of the technology using aqueous samples for the future development of a pathological prediction model. The ATR-FTIR spectra of the co-spiked samples exhibited pronounced amide I (1662 cm-1) and amide II (1545 cm-1) bands indicative of elevated protein along with glucose-associated bands at 1155, 1107, 1079 and 1036 cm-1. Partial Least Squares (PLS) yielded promising R2 values of more than 0.9 for BSA, glucose, and co-spiked glucose/protein. The limit of detection of the technique was 40.28 mg L-1 for protein and 291.65 mg L-1 for glucose, demonstrating the potential of the technique as a tool to identify analytes associated with pathological conditions including cardiovascular disease (CVD), chronic kidney disease (CKD), and diabetes.

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (IInd) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for IInd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from IInd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm-1 and 2936 cm-1 can be good candidates of lipids marker of diabetes.

Optical biomarker analysis for renal cell carcinoma obtained from preoperative and postoperative patients using ATR-FTIR spectroscopy

Renal cell carcinoma (RCC) is the most common malignant tumor in the urinary system, accounting for 80 % to 90 % for all renal malignancies. Traditional diagnostic methods like magnetic resonance imaging (MRI) and computed tomography (CT) lack the sensitivity and specificity as they lack specific biomarkers. These limitations impede effective monitoring of tumor recurrence. This study aims to employ Attenuated Total Reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy, an optical technology sensitive to molecular groups, to analyze the potential optical biomarkers in urine and plasma samples from RCC patients pre- and post-surgery. The results reveal distinctive spectral information from both plasma and urine samples. Post-surgery urine spectra exhibit complexity compared to plasma, showing reduced content at 1072 cm-1, 1347 cm-1 and 1654 cm-1 bands, while increased content at 1112 cm-1, 1143 cm-1, 1447 cm-1, 3334 cm-1 and 3420 cm-1 bands. Utilizing machine learning models such as eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), partial least squares (PLS), and artificial neural network (ANN), the study evaluated plasma and urine samples pre- and post-surgery. Remarkably, the XGBoost method excelled in distinguishing between tumor conditions and recovery, achieving an impressive AUC value of 0.99. These results underscore the potential of ATR-FTIR technology in identifying RCC optical biomarkers, with XGBoost showing promise as a valuable screening tool for RCC recurrence diagnosis.

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Total Reflection (ATR-FTIR) platform was applied to discriminate systemic diseases using saliva, however, the salivary diagnostic application in viral diseases is less explored. The study aimed to identify unique vibrational modes of salivary infrared profiles to detect CHIKV infection using chemometrics and artificial intelligence algorithms. Thus, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with CHIKV (20 µl, 1 X 105 PFU/ml, n = 6) or vehicle (20 µl, n = 7). Saliva and serum samples were collected on day 3 (due to the peak of viremia). CHIKV infection was confirmed by Real-time PCR in the serum of CHIKV-infected mice. The best pattern classification showed a sensitivity of 83%, specificity of 86%, and accuracy of 85% using support vector machine (SVM) algorithms. Our results suggest that the salivary ATR-FTIR platform can discriminate CHIKV infection with the potential to be applied as a non-invasive, sustainable, and cost-effective detection tool for this emerging disease.

Perspectives of FTIR as Promising Tool for Pathogen Diagnosis, Sanitary and Welfare Monitoring in Animal Experimentation Models: A Review Based on Pertinent Literature

Currently, there is a wide application in the literature of the use of the Fourier Transform Infrared Spectroscopy (FTIR) technique. This basic tool has also proven to be efficient for detecting molecules associated with hosts and pathogens in infections, as well as other molecules present in humans and animals' biological samples. However, there is a crisis in science data reproducibility. This crisis can also be observed in data from experimental animal models (EAMs). When it comes to rodents, a major challenge is to carry out sanitary monitoring, which is currently expensive and requires a large volume of biological samples, generating ethical, legal, and psychological conflicts for professionals and researchers. We carried out a survey of data from the relevant literature on the use of this technique in different diagnostic protocols and combined the data with the aim of presenting the technique as a promising tool for use in EAM. Since FTIR can detect molecules associated with different diseases and has advantages such as the low volume of samples required, low cost, sustainability, and provides diagnostic tests with high specificity and sensitivity, we believe that the technique is highly promising for the sanitary and stress and the detection of molecules of interest of infectious or non-infectious origin.

Recognition of breast cancer subtypes using FTIR hyperspectral data

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Exploration of macromolecular phenotype of human skeletal muscle in diabetes using infrared spectroscopy

Introduction

The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents.

Materials and methods

In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach.

Results

A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles.

Conclusion

We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.

Oral Infection, Oral Pathology and Salivary Diagnostics of Mpox Disease: Relevance in Dentistry and OMICs Perspectives

In this narrative review, we aim to point out the close relationship between mpox virus (MPXV) infection and the role of saliva as a diagnostic tool for mpox, considering the current molecular approach and in the perspective of OMICs application. The MPXV uses the host cell's rough endoplasmic reticulum, ribosomes, and cytoplasmic proteins to replicate its genome and synthesize virions for cellular exit. The presence of oral mucosa lesions associated with mpox infection is one of the first signs of infection; however, current diagnostic tools find it difficult to detect the virus before the rashes begin. MPXV transmission occurs through direct contact with an infected lesion and infected body fluids, including saliva, presenting a potential use of this fluid for diagnostic purposes. Currently available diagnostic tests for MPXV detection are performed either by real-time quantitative PCR (RT-qPCR) or ELISA, which presents several limitations since they are invasive tests. Despite current clinical trials with restricted sample size, MPXV DNA was detected in saliva with a sensitivity of 85%-100%. In this context, the application of transcriptomics, metabolomics, lipidomics, or proteomics analyses coupled with saliva can identify novel disease biomarkers. Thus, it is important to note that the identification and quantification of salivary DNA, RNA, lipid, protein, and metabolite can provide novel non-invasive biomarkers through the use of OMICs platforms aiding in the early detection and diagnosis of MPXV infection. Untargeted mass spectrometry (MS)-based proteomics reveals that some proteins also expressed in saliva were detected with greater expression differences in blood plasma when comparing mpox patients and healthy subjects, suggesting a promising alternative to be applied in screening or diagnostic platforms for mpox salivary diagnostics coupled to OMICs.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.