Vibrational spectroscopy of biofluids for disease screening or diagnosis: translation from the laboratory to a clinical setting

Efficacy of blood plasma spectroscopy for early liver cancer diagnostics in obese patients

INTRODUCTION AND OBJECTIVES

Hepatocellular carcinoma (HCC) represents one of the most common cancers worldwide. A considerable proportion of HCC is caused by cirrhosis related to metabolic dysfunction-associated steatohepatitis (MASH). Due to the increasing prevalence of metabolic syndrome, it is estimated that MASH-related HCC will become the most prevalent etiology of HCC. Currently, HCC screening is based on liver ultrasonography; however, the sensitivity of ultrasonography for early HCC stages in obese patients only reaches 23 %. To date, no studied biomarker shows sufficient efficacy for screening purposes. Nevertheless, the usage of spectroscopic methods offers a new perspective, as its potential use would provide cheap, fast analysis of samples such as blood plasma.

MATERIAL AND METHODS

We employed a combination of conventional and chiroptical spectroscopic methods to study differences between the blood plasma of obese cirrhotic patients with and without HCC. We included 20 subjects with HCC and 17 without evidence of liver cancer, all of them with body mass index ≥ 30.

RESULTS

Sensitivities and specificities reached values as follows: 0.780 and 0.905 for infrared spectroscopy, 0.700 and 0.767 for Raman spectroscopy, 0.840 and 0.743 for electronic circular dichroism, and 0.805 and 0.923 for Raman optical activity. The final combined classification model based on all spectroscopic methods reached a sensitivity of 0.810 and a specificity of 0.857, with the highest area under the receiver operating characteristic curve among all models (0.961).

CONCLUSIONS

We suggest that this approach can be used effectively as a diagnostic tool in patients who are not examinable by liver ultrasonography.

CLINICAL TRIAL REGISTRATION

NCT04221347.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 μg/mL and >0.5 μg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

Advancements in Mid-Infrared spectroscopy of extracellular vesicles

Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].

Frontline clinical diagnosis-FTIR on pancreatic cancer

OBJECTIVE

Accurate, easily accessible and economically viable cancer diagnostic tools are pivotal in improving the abysmal 5% survival rate of pancreatic cancer.

METHODS

A novel, affordable, non-invasive diagnostic method has been developed by combining measurement precision of infrared spectroscopy with classification using machine learning tools.

RESULTS

Diagnosis accuracy as high as 90% has been achieved. The study investigated urine and blood from pancreas cancer patients and healthy volunteers, and significantly improved accuracy by focusing on sweet-spots within blood plasma fractions containing molecules within a narrow range of molecular weights.

Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures

Sarcophaga peregrina (Robineau-Desvoidy, 1830) has the potential to estimate the minimum postmortem interval (PMI_min). Development data and intra-puparial age estimation are significant for PMI_min estimation. Previous research has focused on constant temperatures, although fluctuating temperatures are a more real scenario at a crime scene. The current study examined the growth patterns of S. peregrina under constant (25.75 °C) and fluctuating temperatures (18-36 °C; 22-30 °C). Furthermore, differentially expressed genes, attenuated total reflectance Fourier-transform infrared spectroscopy, and cuticular hydrocarbons of S. peregrina during the intra-puparial period were used to estimate age. The results indicated that S. peregrina at fluctuating temperatures took longer to develop and had a lower pupariation rate, eclosion rate, and pupal weight than the group at constant temperatures did. Moreover, we found that six DEG expression profiles and ATR-FTIR technology, CHCs detection methods, and chemometrics can potentially estimate the intra-puparial age of S. peregrina at both constant and fluctuating temperatures. The findings of the study support the use of S. peregrina for PMI_min estimation and encourage the use of entomological evidence in forensic practice.

The Potential Use of Near- and Mid-Infrared Spectroscopy in Kidney Diseases

Traditional renal biomarkers such as serum creatinine and albuminuria/proteinuria are rather insensitive since they change later in the course of the disease. In order to determine the extent and type of kidney injury, as well as to administer the proper therapy and enhance patient management, new techniques for the detection of deterioration of the kidney function are urgently needed. Infrared spectroscopy is a label-free and non-destructive technique having the potential to be a vital tool for quick and inexpensive routine clinical diagnosis of kidney disorders. The aim of this review is to provide an overview of near- and mid-infrared spectroscopy applications in patients with acute kidney injury and chronic kidney disease (e.g., diabetic nephropathy and glomerulonephritis).

The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy

Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information.

Participants in the Trans-Antarctic Winter Traverse Expedition Showed Increased Bacterial Load and Diversity in Saliva but Maintained Individual Differences within Stool Microbiota and Across Metabolite Fingerprints

Understanding the impact of long-term physiological and environmental stress on the human microbiota and metabolome may be important for the success of space flight. This work is logistically difficult and has a limited number of available participants. Terrestrial analogies present important opportunities to understand changes in the microbiota and metabolome and how this may impact participant health and fitness. Here, we present work from one such analogy: the Transarctic Winter Traverse expedition, which we believe is the first assessment of the microbiota and metabolome from different bodily locations during prolonged environmental and physiological stress. Bacterial load and diversity were significantly higher during the expedition when compared with baseline levels (p < 0.001) in saliva but not stool, and only a single operational taxonomic unit assigned to the Ruminococcaceae family shows significantly altered levels in stool (p < 0.001). Metabolite fingerprints show the maintenance of individual differences across saliva, stool, and plasma samples when analysed using flow infusion electrospray mass spectrometry and Fourier transform infrared spectroscopy. Significant activity-associated changes in terms of both bacterial diversity and load are seen in saliva but not in stool, and participant differences in metabolite fingerprints persist across all three sample types.

Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy

Crohn's disease (CD) and spondyloarthritis (SpA) are two inflammatory diseases sharing many common features (genetic polymorphism, armamentarium). Both diseases lack diagnostic markers of certainty. While the diagnosis of CD is made by a combination of clinical, and biological criteria, the diagnosis of SpA may take several years to be confirmed. Based on the hypothesis that CD and SpA alter the biochemical profile of plasma, the objective of this study was to evaluate the analytical capability of Fourier transform infrared spectroscopy (FTIR) in identifying spectral biomarkers. Plasma from 104 patients was analyzed. After data processing of the spectra by Extended Multiplicative Signal Correction and linear discriminant analysis, we demonstrated that it was possible to distinguish CD and SpA from controls with an accuracy of 97% and 85% respectively. Spectral differences were mainly associated with proteins and lipids. This study showed that FTIR analysis is efficient to identify plasma biosignatures specific to CD or SpA.

Urinalysis of individuals with renal hyperfiltration using ATR-FTIR spectroscopy

Abnormal increased glomerular filtration rate (GFR), otherwise known as renal hyperfiltration (RHf), is associated with an increased risk of chronic kidney disease and cardiovascular mortality. Although it is not considered as a disease alone in medicine today, early detection of RHf is essential to reducing risk in a timely manner. However, detecting RHf is a challenge since it does not have a practical biochemical marker that can be followed or quantified. In this study, we tested the ability of ATR-FTIR spectroscopy to distinguish 17 individuals with RHf (hyperfiltraters; RHf (+)), from 20 who have normal GFR (normofiltraters; RHf(-)), using urine samples. Spectra collected from hyperfiltraters were significantly different from the control group at positions 1621, 1390, 1346, 933 and 783/cm. Intensity changes at these positions could be followed directly from the absorbance spectra without the need for pre-processing. They were tentatively attributed to urea, citrate, creatinine, phosphate groups, and uric acid, respectively. Using principal component analysis (PCA), major peaks of the second derivative forms for the classification of two groups were determined. Peaks at 1540, 1492, 1390, 1200, 1000 and 840/cm were significantly different between the two groups. Statistical analysis showed that the spectra of normofiltraters are similar; however, those of hyperfiltraters show diversity at multiple positions that can be observed both from the absorbance spectra and the second derivative profiles. This observation implies that RHf can simultaneously affect the excretion of many substances, and that a spectroscopic analysis of urine can be used as a rapid and non-invasive pre-screening tool.

Contributions of vibrational spectroscopy to virology: A review

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2 Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis.

Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer

We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers’ signals. A narrower range molecular weight window of the serum was also investigated that yielded even higher accuracy on diagnosis. The most accurate results were produced in the serum’s 10–30 kDa molecular weight region to distinguish between the two hardest to discern classes, i.e., premalignant and cancer patients. This work promises an avenue for earlier diagnosis with high accuracy as well as greater insight into the molecular origins of these signals by identifying a key molecular weight region to focus on.

Characterizing biological macromolecules with attenuated total reflectance-Fourier transform infrared spectroscopy provides hands-on spectroscopy experiences for undergraduates

Infrared (IR) spectra of biologically derived materials display distinct absorption bands correlating to individual macromolecules: protein, polysaccharide, lipid, and nucleic acids. A series of experiments aimed at teaching qualitative bioorganic spectroscopy using attenuated total reflectance (ATR) Fourier transform infrared (IR) with biological polymers as samples is proposed. Labs targeting 1st and 4th year undergraduate students at St. Francis College are being developed. During 2014 ➔ 2017, an integrated biology/chemistry exercise featuring elementary spectroscopy as an addition to an existing lab on light microscopy was administered to three sections of a 1st year general biology course. Students were taught the concept of a spectral fingerprint and to identify carbohydrate and protein based materials by looking for key vibrational bands. The success of that effort as determined by the results of an assessment quiz became the motivation for developing an advanced 4th year exercise involving four macromolecules. In a trial lab (Spring 2019) students gathered reference spectra from materials homogeneous in a single biopolymer followed by spectra of whole tissues which they were expected to fully characterize. Assessment data suggest that 1st year students benefited most from the experience. A detailed discussion of reference and sample spectra (as obtained by students) and relevant bond vibrations along with suggestions for instructors are presented.

Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach

An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.

Comparative Study of Sample Carriers for the Identification of Volatile Compounds in Biological Fluids Using Raman Spectroscopy

Vibrational spectroscopic techniques and especially Raman spectroscopy are gaining ground in substituting the officially established chromatographic methods in the identification of ethanol and other volatile substances in body fluids, such as blood, urine, saliva, semen, and vaginal fluids. Although a couple of different carriers and substrates have been employed for the biochemical analysis of these samples, most of them are suffering from important weaknesses as far as the analysis of volatile compounds is concerned. For this reason, in this study three carriers are proposed, and the respective sample preparation methods are described for the determination of ethanol in human urine samples. More specifically, a droplet of the sample on a highly reflective carrier of gold layer, a commercially available cuvette with a mirror to enhance backscattered radiation sealed with a lid, and a home designed microscope slide with a cavity coated with gold layer and covered with transparent cling film have been evaluated. Among the three proposed carriers, the last one achieved a quick, simple, and inexpensive identification of ethanol, which was used as a case study for the volatile compound, in the biological samples. The limit of detection (LoD) was found to be 1.00 μL/mL, while at the same time evaporation of ethanol was prevented.

Raman hyperspectral imaging coupled to three-dimensional discriminant analysis: Classification of meningiomas brain tumour grades

Meningiomas remains a clinical dilemma. They are the commonest "benign" types of brain tumours and, although being typically benign, they are divided into three WHO grades categories (I, II and III) which are associated with the tumour growth rate and likelihood of recurrence. Recurrence depends on extend of surgery as well as histopathological diagnosis. There is a marked variation amongst surgeons in the follow-up arrangements for their patients even within the same unit which has a significant clinical, and financial implication. Knowing the tumour grade rapidly is an important factor to predict surgical outcomes and adequate patient treatment. Clinical follow up sometimes is haphazard and not based on clear evidence. Spectrochemical techniques are a powerful tool for cancer diagnostics. Raman hyperspectral imaging is able to generate spatially-distributed spectrochemical signatures with great sensitivity. Using this technique, 95 brain tissue samples (66 meningiomas WHO grade I, 24 meningiomas WHO grade II and 5 meningiomas that reoccurred) were analysed in order to discriminate grade I and grade II samples. Newly-developed three-dimensional discriminant analysis algorithms were used to process the hyperspectral imaging data in a 3D fashion. Three-dimensional principal component analysis quadratic discriminant analysis (3D-PCA-QDA) was able to distinguish grade I and grade II meningioma samples with 96% test accuracy (100% sensitivity and 95% specificity). This technique is here shown to be a high-throughput, reagent-free, non-destructive, and can give accurate predictive information regarding the meningioma tumour grade, hence, having enormous clinical potential with regards to being developed for intra-operative real-time assessment of disease.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.

Application of FTIR Spectroscopy for Quantitative Analysis of Blood Serum: A Preliminary Study

The aim of this study was to analyze the possibility of simultaneous determination of the concentration of components from the characteristics of FTIR spectra using the example of a model blood serum. To prepare model solutions, a set of freeze-dried control sera based on bovine blood serum was used, certified for approximately 38 parameters. Based on the values of the absorbance and areas of absorption bands in the FTIR spectra of model solutions, a regression equation was constructed by solving a nonlinear problem using the generalized reduced gradient method. By using the absorbance of the absorption bands at 1717 and 3903 cm⁻¹ and the areas of the absorption bands at 616, 3750, and 3903 cm⁻¹, it is possible to simultaneously determine the concentrations of 38 components with an error of less than 0.1%. The results obtained confirm the potential clinical use of FTIR spectroscopy as a reagent-free express method for the analysis of blood serum. However, its practical implementation requires additional research, in particular, analysis of real blood serum samples and validation of the method.

Molecular Origin of Blood-Based Infrared Spectroscopic Fingerprints*

Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.

Urine analysis using FTIR spectroscopy: A study on healthy adults and children

Urine spectra from 108 healthy volunteers are studied by attenuated total refraction-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral features are correlated with observable urine components. The variation of spectra within a healthy population is quantified and a library of reference spectra is constructed. Using the band assignments, these spectra are compared with both age-wise and gender-wise. Children show the least intensity variations compared to both adult groups. Young adults show the highest variation, particularly in the 1650 to 1400 cm^-1 and 1200 to 900 cm^-1 regions. These results indicate the importance of the size of the control group in comparative studies utilizing FTIR. Age-wise comparisons reveal that phosphate and sulfate excretion decreases with age, and that the variance of phosphate among individuals is higher with adults. As for gender-wise comparisons, females show a slightly higher citrate content at 1390 cm^-1 regardless of the age and they show a higher variance in the 1200 to 1000 cm^-1 region when compared to men.

Potential of FTIR Spectroscopy Applied to Exosomes for Alzheimer's Disease Discrimination: A Pilot Study

Alzheimer's disease (AD) diagnosis is based on psychological and imaging tests but can also include monitoring cerebrospinal fluid (CSF) biomarkers. However, CSF based-neurochemical approaches are expensive and invasive, limiting their use to well-equipped settings. In contrast, blood-based biomarkers are minimally invasive, cost-effective, and a widely accessible alternative. Blood-derived exosomes have recently emerged as a reliable AD biomarker source, carrying disease-specific cargo. Fourier-transformed infrared (FTIR) spectroscopy meets the criteria for an ideal diagnostic methodology since it is rapid, easy to implement, and has high reproducibility. This metabolome-based technique is useful for diagnosing a broad range of diseases, although to our knowledge, no reports for FTIR spectroscopy applied to exosomes in AD exist. In this ground-breaking pilot study, FTIR spectra of serum and serum-derived exosomes from two independent cohorts were acquired and analyzed using multivariate analysis. The regional UA-cohort includes 9 individuals, clinically diagnosed with AD, mean age of 78.7 years old; and the UMG-cohort comprises 12 individuals, clinically diagnosed with AD (based on molecular and/or imaging data), mean age of 73.2 years old. Unsupervised principal component analysis of FTIR spectra of serum-derived exosomes revealed higher discriminatory value for AD cases when compared to serum as a whole. Consistently, the partial least-squares analysis revealed that serum-derived exosomes present higher correlations than serum. In addition, the second derivative peak area calculation also revealed significant differences among Controls and AD cases. The results obtained suggest that this methodology can discriminate cases from Controls and thus be potential useful to assist in AD clinical diagnosis.

Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas

Glioma has a low cure rate and a high mortality rate. Therefore, correct diagnosis and treatment are essential for patients. This research aims to use mid-infrared spectroscopy combined with machine learning algorithms to identify patients with glioma. The glioma infrared spectra and the control group serum are smoothed and normalized, then the principal component analysis (PCA) algorithm is used to reduce the data dimensionality, and finally, the particle swarm optimization-support vector machine (PSO-SVM), backpropagation (BP) neural network and decision tree (DT) model are established. The classification accuracy of the three models was 92.00 %, 91.83 %, 87.20 %, and the AUC values were 0.919, 0.945, and 0.866, respectively. The results show that PCA-PSO-SVM has a better classification effect. This study shows that mid-infrared spectroscopy combined with machine learning algorithms has great potential in the application of non-invasive, rapid and accurate identification of glioma patients.

Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity

There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15-30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results (n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.

Detection of caffeine intake by means of EC-SERS spectroscopy of human saliva

This work presents the application of EC-SERS spectroscopy for the detection of caffeine consumption from human saliva. Caffeine and paraxanthine as the major metabolite of caffeine were tested. Model samples of saliva spiked with caffeine were investigated, and detection of caffeine in real-life saliva samples was tested in order to ensure the viability of the method for clinical applications. Two doses of caffeine (2 mg/kg and 3.5 mg/kg) were ingested by volunteers, and their saliva samples were taken at different time periods ranging from 1 h to 10 h after the consumption. Density functional theory calculations of caffeine and paraxanthine adsorbed on the silver surface were performed in order to better understand the adsorption of the investigated molecules and to make a correct assignment of the experimental spectral bands of the EC-SERS spectra. It was determined that a low dose caffeine consumption can be detected by the appearance of the SERS spectral marker band of caffeine and paraxanthine at 692 cm^-1. The intensity of this band is mostly reasoned by the paraxanthine concentration since the intensity changes of the band over time correlates to the concentration changes of paraxanthine determined by the pharmacokinetic studies of paraxanthine and caffeine in the human saliva. It was found that the limit of detection paraxanthine in saliva by means of EC-SERS is as low as 15 μM and can be further improved.

Quantitative analysis of human blood serum using vibrational spectroscopy

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State of the art of quantitative Vibrational Spectroscopic analysis of human blood serum is reviewed.

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Technical considerations for infrared absorption and Raman analysis are discussed.

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Quantitative analyses of Endogenous and Exogenous constituents are presented.

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The potential for clinical translation of spectroscopic serology is argued.

Analysis of bodily fluids using vibrational spectroscopy has attracted increasing attention in recent years. In particular, infrared spectroscopic screening of blood products, particularly blood serum, for disease diagnostics has been advanced considerably, attracting commercial interests. However, analyses requiring quantification of endogenous constituents or exogenous agents in blood are less well advanced. Recent advances towards this end are reviewed, focussing on infrared and Raman spectroscopic analyses of human blood serum. The importance of spectroscopic analysis in the native aqueous environment is highlighted, and the relative merits of infrared absorption versus Raman spectroscopy are considered, in this context. It is argued that Raman spectroscopic analysis is more suitable to quantitative analysis in liquid samples, and superior performance for quantification of high and low molecular weight components, is demonstrated. Applications for quantitation of viral loads, and therapeutic drug monitoring are also discussed.

A comparative analysis of different biofluids towards ovarian cancer diagnosis using Raman microspectroscopy

Biofluids, such as blood plasma or serum, are currently being evaluated for cancer detection using vibrational spectroscopy. These fluids contain information of key biomolecules, such as proteins, lipids, carbohydrates and nucleic acids, that comprise spectrochemical patterns to differentiate samples. Raman is a water-free and practically non-destructive vibrational spectroscopy technique, capable of recording spectrochemical fingerprints of biofluids with minimum or no sample preparation. Herein, we compare the performance of these two common biofluids (blood plasma and serum) together with ascitic fluid, towards ovarian cancer detection using Raman microspectroscopy. Samples from thirty-eight patients were analysed (n = 18 ovarian cancer patients, n = 20 benign controls) through different spectral pre-processing and discriminant analysis techniques. Ascitic fluid provided the best class separation in both unsupervised and supervised discrimination approaches, where classification accuracies, sensitivities and specificities above 80% were obtained, in comparison to 60–73% with plasma or serum. Ascitic fluid appears to be rich in collagen information responsible for distinguishing ovarian cancer samples, where collagen-signalling bands at 1004 cm⁻¹ (phenylalanine), 1334 cm⁻¹ (CH₃CH₂ wagging vibration), 1448 cm⁻¹ (CH₂ deformation) and 1657 cm⁻¹ (Amide I) exhibited high statistical significance for class differentiation (P < 0.001). The efficacy of vibrational spectroscopy, in particular Raman spectroscopy, combined with ascitic fluid analysis, suggests a potential diagnostic method for ovarian cancer.

Vibrational Spectroscopy for In Vitro Monitoring Stem Cell Differentiation

Stem cell technology has attracted considerable attention over recent decades due to its enormous potential in regenerative medicine and disease therapeutics. Studying the underlying mechanisms of stem cell differentiation and tissue generation is critical, and robust methodologies and different technologies are required. Towards establishing improved understanding and optimised triggering and control of differentiation processes, analytical techniques such as flow cytometry, immunohistochemistry, reverse transcription polymerase chain reaction, RNA in situ hybridisation analysis, and fluorescence-activated cell sorting have contributed much. However, progress in the field remains limited because such techniques provide only limited information, as they are only able to address specific, selected aspects of the process, and/or cannot visualise the process at the subcellular level. Additionally, many current analytical techniques involve the disruption of the investigation process (tissue sectioning, immunostaining) and cannot monitor the cellular differentiation process in situ, in real-time. Vibrational spectroscopy, as a label-free, non-invasive and non-destructive analytical technique, appears to be a promising candidate to potentially overcome many of these limitations as it can provide detailed biochemical fingerprint information for analysis of cells, tissues, and body fluids. The technique has been widely used in disease diagnosis and increasingly in stem cell technology. In this work, the efforts regarding the use of vibrational spectroscopy to identify mechanisms of stem cell differentiation at a single cell and tissue level are summarised. Both infrared absorption and Raman spectroscopic investigations are explored, and the relative merits, and future perspectives of the techniques are discussed.

Biochemical assay and spectroscopic analysis of oxidative/antioxidative parameters in the blood and serum of substance use disorders patients. A methodological comparison study

Substance abuse such as opioids, cannabis, and alcohol causes activation on the immune system and the release of reactive oxygen species (ROS) into the blood and serum. These substances cause an effect on oxidant and antioxidant status in patients with substance abuse. Mainly, wide-open to the ROS are lipids and proteins included blood, which suffers peroxidation. In this study, for the first-time differentiation of the effects of cannabis, alcohol and other synthetic substances on blood and serum samples, were performed. For this purpose, the level of the malondialdehyde (MDA) and glutathione (GSH) in serum and red blood cells, was measured using biochemical assay methods and Fourier Transform InfraRed spectroscopy (FTIR). The results showed, that peroxidation which is dignified as the production of MDA was increased for substance use disorder (SUD) patients (18.01 ± 2.97) compared to the control group (10.75 ± 2.28) (p < 0.001) and for antioxidant capacity, GSH level were significantly increased for SUD patients (p < 0.001). For the discrimination of protein and lipid region obtained from FTIR spectroscopy, we extracted features by principal component analyze (PCA) of protein (1800 cm^-1 to 900 cm^-1) and lipid (3200 cm^-1 to 2800 cm^-1) regions for blood and serum samples collected from patients with different types of SUD and healthy control (HC) participants. For the consideration of lipid oxidation, lipid saturation, lipid desaturation and protein aggregation the peak heights at 1740 cm^-1 to 2960 cm^-1, 2920 cm^-1 to 2960 cm^-1, 3012 cm^-1 to 2960 cm^-1, and 1630 cm^-1 to 1650 cm^-1 regions were studied for SUD and HC. Moreover, more visible changes were noticed for proteins region, than for lipids. The most notice structural changes were observed in amide II in serum spectra. Then we classified protein and lipid region's PCA results of blood and serum by Linear discriminant analysis (LDA) and Support vector machine (SVM). Confidence of the specificity, sensitivity and accuracy of blood and serum were obtained as 100%, 100% and 100% individually. This is the first comparative study on the spectrochemical tool and biochemical assay on SUD. Our results presented 100% discrimination of disorder region compared to healthy subjects.

Towards screening Barrett's oesophagus: current guidelines, imaging modalities and future developments

Barrett's oesophagus is the only known precursor to oesophageal adenocarcinoma (OAC). Although guidelines on the screening and surveillance exist in Barrett's oesophagus, the current strategies are inadequate. Oesophagogastroduodenoscopy (OGD) is the gold standard method in screening for Barrett's oesophagus. This invasive method is expensive with associated risks negating its use as a current screening tool for Barrett's oesophagus. This review explores current definitions, epidemiology, biomarkers, surveillance, and screening in Barrett's oesophagus. Imaging modalities applicable to this condition are discussed, in addition to future developments. There is an urgent need for an alternative non-invasive method of screening and/or surveillance which could be highly beneficial towards reducing waiting times, alleviating patient fears and reducing future costs in current healthcare services. Vibrational spectroscopy has been shown to be promising in categorising Barrett's oesophagus through to high-grade dysplasia (HGD) and OAC. These techniques need further validation through multicentre trials.

Spectrochemical analysis of liquid biopsy harnessed to multivariate analysis towards breast cancer screening

Mortality due to breast cancer could be reduced via screening programs where preliminary clinical tests employed in an asymptomatic well-population with the objective of identifying cancer biomarkers could allow earlier referral of women with altered results for deeper clinical analysis and treatment. The introduction of well-population screening using new and less-invasive technologies as a strategy for earlier detection of breast cancer is thus highly desirable. Herein, spectrochemical analyses harnessed to multivariate classification techniques are used as a bio-analytical tool for a Breast Cancer Screening Program using liquid biopsy in the form of blood plasma samples collected from 476 patients recruited over a 2-year period. This methodology is based on acquiring and analysing the spectrochemical fingerprint of plasma samples by attenuated total reflection Fourier-transform infrared spectroscopy; derived spectra reflect intrinsic biochemical composition, generating information on nucleic acids, carbohydrates, lipids and proteins. Excellent results in terms of sensitivity (94%) and specificity (91%) were obtained using this method in comparison with traditional mammography (88-93% and 85-94%, respectively). Additional advantages such as better disease prognosis thus allowing a more effective treatment, lower associated morbidity, fewer false-positive and false-negative results, lower-cost, and higher analytical frequency make this method attractive for translation to the clinical setting.

Differentiating Between Malignant Mesothelioma and Other Pleural Lesions Using Fourier Transform Infrared Spectroscopy

Histopathology, despite being the gold standard as a diagnostic tool, does not always provide a correct diagnosis for different pleural lesions. Although great progress was made in this field, the problem to differentiate between reactive and malignant pleural lesions still stimulates the search for additional diagnostic tools. Our research using vibrational spectroscopy and principal component analysis (PCA) statistical modeling represents a potentially useful tool to approach the problem. The objective method this paper explores is based on the correlation between different types of pleural lesions and their vibrational spectra. Obtained tissue spectra recorded by infrared spectroscopy allowed us to categorize spectra in different groups using a created PCA statistical model. The PCA model was built using tissues of known pathology as the model group. The validation samples were then used to confirm the functionality of our PCA model. Student's t-test was also used for comparing samples in paired groups. The PCA model was able to clearly differentiate the spectra of mesothelioma, metastasis and reactive changes (inflammation), and place them in discrete groups. Thus, we showed that Fourier transform infrared spectroscopy combined with PCA can differentiate pleural lesions with high sensitivity and specificity. This new approach could contribute in objectively differentiating specific pleural lesions, thus helping pathologists to better diagnose difficult pleural samples but also could shed additional light into the biology of malignant pleural mesothelioma.

Protein Conformational Changes in Breast Cancer Sera Using Infrared Spectroscopic Analysis

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600-1700 cm-1) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140-1000 cm-1. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and β-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I1556/I1295) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

Liquid biopsy for cancer diagnosis using vibrational spectroscopy: systematic review

Background

Vibrational spectroscopy (VS) is a minimally invasive tool for analysing biological material to detect disease. This study aimed to review its application to human blood for cancer diagnosis.

Methods

A systematic review was undertaken using a keyword electronic database search (MEDLINE, Embase, PubMed, TRIP and Cochrane Library), with all original English‐language manuscripts examining the use of vibrational spectral analysis of human blood for cancer detection. Studies involving fewer than 75 patients in the cancer or control group, animal studies, or where the primary analyte was not blood were excluded.

Results

From 1446 results, six studies (published in 2010–2018) examining brain, bladder, oral, breast, oesophageal and hepatic cancer met the criteria for inclusion, with a total population of 2392 (1316 cancer, 1076 control; 1476 men, 916 women). For cancer detection, reported mean sensitivities in each included study ranged from 79·3 to 98 per cent, with specificities of 82·8–95 per cent and accuracies between 81·1 and 97·1 per cent. Heterogeneity in reporting strategies, methods and outcome measures made meta‐analysis inappropriate.

Conclusion

VS shows high potential for cancer diagnosis, but until there is agreement on uniform standard reporting methods and studies with adequate sample size for valid classification models have been performed, its value in clinical practice will remain uncertain.

Age and Gender Characteristics of the Infrared Spectra of Normal Human Saliva

The comparison of the characteristics of the infrared (IR) spectra of saliva of healthy volunteers was carried out based on gender and age. It is shown that statistically significant differences between male and female groups are observed for the absorption bands of proteins and lipids. At the same time, the absorbance of the bands assigned to proteins and nucleic acids is higher for males, whereas the absorbance of the bands assigned to lipids is higher in the group of females. It is established that the correlation relationships of the characteristics of the spectra and age are weakly expressed. Thus, when forming the criteria of the norm and pathology for saliva, it is necessary to take into account the gender of the subjects, while there are no strict requirements for taking into account age periodization. Nevertheless, the revealed patterns are valid only for the composition of the saliva of healthy volunteers, the extension of the results to groups of patients with various diseases, as well as other biological fluids, requires additional testing.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

A New Artificial Urine Protocol to Better Imitate Human Urine

Artificial urine has many advantages over human urine for research and educational purposes. By closely mimicking healthy individuals’ urine, it may also be important in discovering novel biomarkers. However, up until now, there has not been any specific protocol to prove the similarity in terms of the chemical composition at the molecular level. In this study, a new artificial urine protocol is established to mimics the urine of healthy individuals. The multi-purpose artificial urine (MP-AU) presented here is compared with two other protocols most cited in literature. Furthermore, these three protocols are also compared with samples from 28 healthy young individuals. To do so, attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is used, according to which MP-AU shows a significantly close similarity with human urine. In formulating MP-AU, the infrared spectra of nine compounds is provided, making possible the band assignment of some absorption bands to certain compounds. Given its properties, the MP-AU protocol introduced here is both economical and practical, making it useful when designing comparative-controlled experiments.