Assessing the challenges of Fourier transform infrared spectroscopic analysis of blood serum

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomarkers, lecithin (L) and sphingomyelin (S) with the potential for point of care (POC) diagnosis and monitoring. The effects of varying other lipid species present in lung surfactant on the mid IR spectra used to train machine learning models are explored. This study presents a lung lipid model of five lipids present in lung surfactant and varies each in a systematic approach to evaluate the ability of machine learning models to predict the lipid concentrations, the L/S ratio and to quantify the uncertainty in the predictions using the jackknife + -after-bootstrap and variant bootstrap methods. We establish the L/S ratio can be determined with an uncertainty of approximately ±0.3 mol/mol and we further identify the 5 most prominent wavenumbers associated with each machine learning model.

Inverted Transflection Spectroscopy of Live Cells Using Metallic Grating on Elevated Nanopillars

Water absorption of mid-infrared (MIR) radiation severely limits the options for vibrational spectroscopy of the analytes-including live biological cells-that must be probed in aqueous environments. While internal reflection elements, such as attenuated total reflection prisms and metasurfaces, partially overcome this limitation, such devices have their own limitations: ATR prisms are difficult to integrate with multiwell cell culture workflows, while metasurfaces suffer from a limited spectral range and small penetration depth into analytes. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated on top of elevated dielectric pillars. For the MIR wavelengths that are significantly longer than the grating period, reflection-based spectroscopy enables broadband sensing of the analytes inside the trenches separating the dielectric pillars. Because the depth of the analyte twice-traversed by the MIR light excludes the highly absorbing thick water layer above the grating, we refer to the technique as inverted transflection spectroscopy (ITS). The analytic power of ITS is established by measuring a wide range of protein concentrations in solution, with the limit of detection in the single-digit mg mL-1. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is used to characterize their adhesion kinetic.

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed. Furthermore, it was shown that the ratio between α-helix and β-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra. The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

Impinging blood droplets on different wettable surfaces: Impact phenomena, contact line motion, post-impact oscillation and dried stains

Understanding the underlying hydrodynamics of impinging blood droplets and finding out the physical parameters determining the bloodstain characteristics are of great importance in blood related forensic investigations. In this work, the impact of non-Newtonian blood droplets on solid surfaces ranging from lyophilic to superlyophobic was systematically investigated and compared to that of Newtonian droplets with a similar dynamic shear viscosity. We show that impinging blood droplets behave as low-viscosity Newtonian droplets in the short-time spreading, which is dominated by capillary and inertial forces, but their non-Newtonian viscoelasticity would notably affect the droplet retraction and post-impact oscillation occurring in large timescales. Whereas the strong liquid-solid adhesion and the non-Newtonian elongational viscosity hinder droplet recoiling and thus alter the impact phenomena on lyophobic and superlyophobic surfaces, the shear and elongational viscosities are coupled to result in higher damping coefficients of oscillating blood droplets after deposition, in comparison to that of impinging Newtonian droplets. The size of the dried bloodstain was found to be different from both the maximum spreading radius of the droplet that can reach during impact and the final radius of the deposited droplet after oscillation, and their correlations are highly dependent on the impact velocity and surface wettability. Moreover, the morphologic characteristics of the bloodstains would also be changed by varying either the impact velocity or the surface wettability. We envision that these findings can not only find applications in the bloodstain pattern analysis, but also provide useful information for medical diagnosis based on blood droplet test.

Raman spectroscopy-based biomarker screening by studying the fingerprint and lipid characteristics of Polycythemıa Vera cases blood serum

This study aimed to develop a novel approach for diagnosing Polycythemia Vera (PV), a stem cell-derived neoplasm of the myeloid lineage. The approach utilized Raman spectroscopy coupled with multivariate analysis to analyze blood serum samples collected from PV patients. The results showed that PV serum exhibited lower protein and lipid levels and structural changes in the functional groups that comprise proteins and lipids. The study also demonstrated differences in lipid biosynthesis and protein levels in PV serum. Using the Partial Least Square Discriminant Analysis (PLS-DA) model, Raman-based multivariate analysis achieved high accuracy rates of 96.49% and 93.04% in the training sets and 93.10% and 89.66% in the test sets for the 800-1800 cm-1 and 2700-3000 cm-1 ranges, respectively. The area under the curve (AUC) values of the test datasets were calculated as 0.92 and 0.89 in the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively, demonstrating the effectiveness of the PLS-DA models for the diagnosis of PV. This study highlights the potential of Raman spectroscopy-based analysis in the early and accurate diagnosis of PV, enabling the application of effective treatment strategies.

Diagnostic Efficiency of Serum-Based Infrared Spectroscopy in Detecting Breast Cancer: A Meta-Analysis

BACKGROUND

The advancement of Fourier transform infrared (FTIR) spectroscopy as a potential diagnostic tool in the clinical setting has been studied over the years, particularly its application in cancer diagnostics.

OBJECTIVE

To summarize previous research on FTIR spectroscopy in detecting breast cancer using serum specimens.

METHODS

Related literature was searched and screened from various databases. Relevant data were then extracted, tabulated, and analyzed using Meta-DiSc 1.4 software.

RESULTS

Sensitivity and specificity rates were 90% to 100% and 80% to 95%, respectively. The area under the receiver operating characteristic curve was at 0.9729, indicating that serum analysis via FTIR spectroscopy can accurately discriminate between healthy individuals and patients with breast cancer.

CONCLUSION

Overall, FTIR spectroscopy for breast cancer diagnosis using serum specimens shows promising results. However, further studies are still needed to validate these claims.

Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma

Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF₂) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma.

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.

Attenuated total reflection FTIR dataset for identification of type 2 diabetes using saliva

Diabetes is one of the top 5 non-communicable diseases that occur worldwide according to the World Health Organization. Despite not being a fatal disease, a late diagnosis as well as poor control can cause a fatal outcome, because of that, several studies have been carried out with the aim of proposing additional techniques to the gold standard to assist in the diagnosis and control of this disease in a non-invasive way. Considering the above, and in order to provide a solid starting point for future researches, we share a primary research dataset with 1040 saliva samples obtained by Fourier Transform Infrared Spectroscopy considering the Attenuated Total Reflectance method. Database include: gender, age, individuals (patients) with/without diabetes, the glucose value, and the result to the A1C test for the diabetic population. We believe that sharing dataset as is could increase experimentation, research, and analysis of spectra through different strategies broaden its range of applicability by chemists, doctors, physicists, computer scientists, among others, to identify the effects that the virus causes in the body and to propose possible clinical treatments as well as to develop devices that allow us to assist in the characterization of possible carriers.

ATR-FTIR spectroscopy as a quality control system for monitoring the storage of blood products

Blood screening is a fundamental part of disease diagnosis and monitoring health. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy offers an innovative solution to streamlining the process, especially for multianalyte detection in aqueous samples. However, samples always undergo a storage phase before they are processed for testing and blood transfusion. In this study, we investigated the effect of standard storage procedures on the macromolecular composition of whole blood, and plasma collected in blood tubes for diagnostic purposes and initial screening of blood products. Periphery blood samples were collected from 10 volunteers and then stored for 14 days at 4 °C. Samples were stored as isolated plasma and whole blood to provide three different datasets, namely: (1) plasma stored independently, (2) plasma stored with other blood components and (3) whole blood. ATR-FTIR spectra of aqueous blood were acquired every 24 h from the time of collection on a portable ATR-FTIR spectrophotometer to monitor the evolution of the macromolecular composition in each blood component. Principal component analysis (PCA), partial least squares regression (PLS-R) and multi-curve resolution alternate least squares (MCR-ALS) models were built to study changes in the spectra with the storage time and identify the key bands. Isolated plasma stored without red blood cells (RBCs) showed no changes over the 14 day period indicating limited degradation. By contrast, plasma stored with the other blood components showed visual and spectroscopic signs of degradation including increasing lipid bands and the amide I and II bands from haemoglobin (Hb). Ideally, for the application of IR spectroscopy in blood diagnostics and for initial screening of blood products, whole blood and isolated red blood cells can be stored for a maximum of 4 days at 4 °C in lithium-heparin anticoagulant tubes prior to spectral analysis before any signs of degradation. Isolated plasma, on the other hand, can be stored for much longer periods and shows no evidence of degradation in the spectra after 14 days.

Breast-cancer detection using blood-based infrared molecular fingerprints

BACKGROUND

Breast cancer screening is currently predominantly based on mammography, tainted with the occurrence of both false positivity and false negativity, urging for innovative strategies, as effective detection of early-stage breast cancer bears the potential to reduce mortality. Here we report the results of a prospective pilot study on breast cancer detection using blood plasma analyzed by Fourier-transform infrared (FTIR) spectroscopy - a rapid, cost-effective technique with minimal sample volume requirements and potential to aid biomedical diagnostics. FTIR has the capacity to probe health phenotypes via the investigation of the full repertoire of molecular species within a sample at once, within a single measurement in a high-throughput manner. In this study, we take advantage of cross-molecular fingerprinting to probe for breast cancer detection.

METHODS

We compare two groups: 26 patients diagnosed with breast cancer to a same-sized group of age-matched healthy, asymptomatic female participants. Training with support-vector machines (SVM), we derive classification models that we test in a repeated 10-fold cross-validation over 10 times. In addition, we investigate spectral information responsible for BC identification using statistical significance testing.

RESULTS

Our models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve (AUC) of the receiver operating characteristic (ROC). In addition, we uncover a relationship between the effect size of the measured infrared fingerprints and the tumor progression.

CONCLUSION

This pilot study provides the foundation for further extending and evaluating blood-based infrared probing approach as a possible cross-molecular fingerprinting modality to tackle breast cancer detection and thus possibly contribute to the future of cancer screening.

Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer

Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78-0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition.

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm^-1 were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.

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.

Infrared Spectroscopy of Blood

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Assessment of structural protein expression by FTIR and biochemical assays as biomarkers of metabolites response in gastric and colon cancer

Colon and gastric cancers are the widespread benign types of cancers which are synchronous and metachronous neoplasms. In terms of the progression and progress of the disease, metabolic processes and differentiation in protein structures have an important role in for treatment of the disease. In this study we proposed to investigate the metabolic process and the differentiation of protein secondary structure among colon and gastric cancer as well as healthy controls using biochemistry and Fourier Transform InfraRed spectroscopy (FTIR) methods. For this purpose, we measured blood serum of 133 patients, which were conducted upon oncology department (45 colon cancer, 45 gastric cancer and 43 control individuals). The obtained spectroscopic results and biochemical assays showed significant reduction in the amount of functional groups in cancer groups contrary with total protein measurements and structure of protein differences between colon and gastric cancers. Differentiations were visible in serum levels of CEA, CA-125, CA-15-3, CA-19-9 AFP (Alpha fetoprotein) of gastric and colon cancer patients as well as in amide III and secondly described amide I regions. Our findings suggest that amide I bonds in colon cancer cells can be helpful in diagnosis of colon cancer. Indeed, our results showed that metabolic processes were higher in gastric cancer group than in colon cancer. Hence, FTIR spectroscopy and curve-fitting analysis of amide I profile can be successfully applied as tools for identifying quantitative and qualitative changes of proteins in human cancerous blood serum. However, what is very important, in PCA analysis we see, that the scatter plot of PC1 (variability 80%) and PC2 (variability 15%) show that the data related to the control and two cancer groups are clustered together with different magnitudes and directions.

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.

Investigating pre-analytical requirements for serum and plasma based infrared spectro-diagnostic

Infrared spectroscopy is a rapid, easy-to-operate, label-free and therefore cost-effective technique. Many studies performed on biofluids (eg, serum, plasma, urine, sputum, bile and cerebrospinal fluid) have demonstrated its promising application as a clinical diagnostic tool. Given all these characteristics, infrared spectroscopy appears to be an ideal candidate to be implemented into the clinics. However, before considering its translation, a clear effort is needed to standardise protocols for biofluid spectroscopic analysis. To reach this goal, careful investigations to identify and track errors that can occur during the pre-analytical phase is a crucial step. Here, we report for the first time, results of investigations into pre-analytical factors that can affect the quality of the spectral data acquired on serum and plasma, such as the impact of long-term freezing time storage of samples as well as the month-to-month reproducibility of the spectroscopic analysis. The spectral data discrimination has revealed to be majorly impacted by a residual water content variation in serum and plasma dried samples.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy’s multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Determining the Age of Spoiled Milk from Dried Films Using Attenuated Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy

Milk spoilage is an inevitable occurrence, which generates waste and can result in food poisoning. When milk spoils, the off-flavor and curdling are due to excessive proliferation of various bacteria which causes pH changes. Time, temperature, environment, and previous handling practice all affect the spoilage rate. There is a need for a fast reliable and accurate method that can identify in situ early spoilage of milk. Here we show the ability of attenuated total reflection Fourier transformed infrared spectroscopy (ATR FT-IR) in conjunction with multivariate data analysis to predict the age of milk. We found that dried films vastly increased the absorbance of important biomolecules within milk such as lipids, proteins, and sugars, compared to an unchanged milk sample. This allowed us to note the minor discrepancies that happened in spoilage. Spoilt milk was characterized by bands associated with increased lipids, proteins, and lactic acid and a decrease in carbohydrates. A semi-quantitative prediction model for milk spoilage at room temperature demonstrated ATR FT-IR spectroscopy can predict milk age with a root mean square error of prediction of approximately 14 h. The model showed poor performance in the first 40 h but the predictions improved significantly after this time. The experimental procedure proposed for detecting biomolecules within milk has the potential to improve common practice. Furthermore, the model would be a starting point for newer and improved methods to predict the spoilage date of milk, with potential commercial uses to reduce food waste and costs to the milk industry.

Applicability of FTIR-ATR Method to Measure Carbonyls in Blood Plasma after Physical and Mental Stress

Introduction

Oxidative stress is a state of imbalance between the production of reactive oxygen species and antioxidant defenses. It results in the oxidation of all cellular elements and, to a large extent, proteins, causing inter alia the formation of carbonyl groups in their structures. The study focused on assessment of changes in the plasma protein-bound carbonyls in police horses after combat training and after rest and the applicability of infrared spectroscopy with a Fourier transform, utilizing the attenuated total reflectance (FTIR-ATR) in detecting plasma protein oxidation.

Methods

We evaluated the influence of both the different concentrations of hydrogen peroxide and combat training on protein carbonylation in horse blood plasma. The oxidation of plasma proteins was assessed using a spectrophotometric method based on the carbonyl groups derivatization with 2,4-dinitrophenylhydrazine (DNPH). The measured values were correlated with the carbonyl groups concentrations determined by means of the FTIR-ATR method.

Results

The linear correlation between the DNPH and FTIR-ATR methods was shown. The concentration of plasma protein-bound carbonyls significantly deceased in police horses after one-day rest when compared to the values measured directly after the combat training (a drop by 23%, p<0.05 and 29%, p<0.01 measured by DNPH and FTIR-ATR methods, respectively). These results were consistent with the proteins phosphorylation analysis.

Conclusion

The FTIR-ATR method may be applied to measure the level of plasma proteins peroxidation.

Optimised spectral pre-processing for discrimination of biofluids via ATR-FTIR spectroscopy

Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients, the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.

Spectral classification for diagnosis involving numerous pathologies in a complex clinical setting: A neuro-oncology example

Much effort is currently being placed into developing new blood tests for cancer diagnosis in the hope of moving cancer diagnosis earlier and by less invasive means than current techniques, e.g., biopsy. Current methods are expected to diagnose and begin treatment of cancer within 62 days of patient presentation, though due to high volume and pressures within the NHS in the UK any technique that can reduce time to diagnosis would allow reduction in the time to treat for patients. The use of vibrational spectroscopy, notably infrared (IR) spectroscopy, has been under investigation for many years with varying success. This technique holds promise as is would combine a generally well accepted test (a blood test) with analysis that is reagent free and cheap to run. It has been demonstrated that, when asked simple clinical questions (i.e., cancer vs. no cancer), results from spectroscopic studies are promising. However, in order to become a clinically useful tool, it is important that the test differentiates a variety of cancer types from healthy patients. This study has analysed plasma samples with attenuated total reflection Fourier-transform IR spectroscopy (ATR-FTIR), to establish if the technique is able to distinguish normal from primary or metastatic brain tumours. We have shown that when asked specific questions, i.e., high-grade glioma vs. low-grade glioma, the results show a significantly high accuracy (100%). Crucially, when combined with meningiomas and metastatic lesions, the accuracy remains high (88-100%) with only minimal overlap between the two metastatic adenocarcinoma groups. Therefore in a clinical setting, this novel technique demonstrates potential benefit when used in conjuction with existing diagnostic methods.

MIR-biospectroscopy coupled with chemometrics in cancer studies

This review focuses on chemometric techniques applied in MIR-biospectroscopy for cancer diagnosis and analysis over the last ten years of research. Experimental applications of chemometrics coupled with biospectroscopy are discussed throughout this work. The advantages and drawbacks of this association are also highlighted. Chemometric algorithms are evidenced as a powerful tool for cancer diagnosis, classification, and in different matrices. In fact, it is shown how chemometrics can be implemented along all different types of cancer analyses.

Comments on recent reports on infrared spectral detection of disease markers in blood components

The search for disease markers in whole blood, or easily accessible blood components by spectral methods is a highly important aspect in the field of biophotonic research for disease diagnostics and screening, since it promises a minimally invasive approach to assess an individual's state of health. Fourier transform infrared spectroscopy, in particular, promises to be a fast, inexpensive method to search for markers of disease, since it detects variation in the proteome, lipidome and metabolome of biofluids, or activation of immune cells. However, the analysis of any materials by spectral methods is confounded by external factors such as those related to sample deposition and data acquisition, and by inherent variations in blood plasma concentration of small molecules (lactate, carbonate, phosphate, glucose) that varies between individual subjects and even for a given individual, as a function of time. Furthermore, observed differences in spectral patterns between patient samples and the control group may be due to the body's immune response (in particular, to the albumin to globulin ratio) and therefore, may not be specific to disease. These factors need to be accounted for in any effort to reliably detect much smaller variations in the concentration of disease-specific markers.

Biofluid infrared spectro-diagnostics: pre-analytical considerations for clinical applications

Several proof-of-concept studies on the vibrational spectroscopy of biofluids have demonstrated that the methodology has promising potential as a clinical diagnostic tool. However, these studies also show that there is a lack of a standardised protocol in sample handling and preparation prior to spectroscopic analysis. One of the most important sources of analytical errors is the pre-analytical phase. For the technique to be translated into clinics, it is clear that a very strict protocol needs to be established for such biological samples. This study focuses on some of the aspects of the pre-analytical phase in the development of the high-throughput Fourier Transform Infrared (FTIR) spectroscopy of some of the most common biofluids such as serum, plasma and bile. Pre-analytical considerations that can impact either the samples (solvents, anti-coagulants, freeze-thaw cycles…) and/or spectroscopic analysis (sample preparation such as drying, deposit methods, volumes, substrates, operators dependence…) and consequently the quality and the reproducibility of spectral data will be discussed in this report.

Biofluid spectroscopic disease diagnostics: A review on the processes and spectral impact of drying

The complex patterns observed from evaporated liquid drops have been examined extensively over the last 20 years. Complete understanding of drop deposition is vital in many medical processes, and one which is essential to the translation of biofluid spectroscopic disease diagnostics. The promising use of spectroscopy in disease diagnosis has been hindered by the complicated patterns left by dried biological fluids which may inhibit the clinical translation of this technology. Coffee-ring formation, cracking and gelation patterns have all been observed in biofluid drops, and with surface homogeneity being a key element to many spectroscopic techniques, experimental issues have been found to arise. A better understanding of the fundamental processes involved in a drying droplet could allow efficient progression in this research field, and ultimately benefit the population with the development of a reliable cancer diagnostic.

Applications of mid-infrared spectroscopy in the clinical laboratory setting

Fourier transform mid-infrared (MIR-FTIR) spectroscopy is a nondestructive, label-free, highly sensitive and specific technique that provides complete information on the chemical composition of biological samples. The technique both can offer fundamental structural information and serve as a quantitative analysis tool. Therefore, it has many potential applications in different fields of clinical laboratory science. Although considerable technological progress has been made to promote biomedical applications of this powerful analytical technique, most clinical laboratory analyses are based on spectroscopic measurements in the visible or ultraviolet (UV) spectrum and the potential role of FTIR spectroscopy still remains unexplored. In this review, we present some general principles of FTIR spectroscopy as a useful method to study molecules in specimens by MIR radiation together with a short overview of methods to interpret spectral data. We aim at illustrating the wide range of potential applications of the proposed technique in the clinical laboratory setting with a focus on its advantages and limitations and discussing the future directions. The reviewed applications of MIR spectroscopy include (1) quantification of clinical parameters in body fluids, (2) diagnosis and monitoring of cancer and other diseases by analysis of body fluids, cells, and tissues, (3) classification of clinically relevant microorganisms, and (4) analysis of kidney stones, nails, and faecal fat.

Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules

Hepatocellular carcinoma (HCC), a complex public health issue that is the most common primary hepatic malignancy, remains the highest incidence in developing countries and is showing sustained growth across the developed world.

Ultra-filtration of human serum for improved quantitative analysis of low molecular weight biomarkers using ATR-IR spectroscopy

Monitoring of changes in the concentrations of the low molecular weight constituents enhanced by abundant proteins depletion.

Screening the low molecular weight fraction of human serum using ATR-IR spectroscopy

Vibrational spectroscopic techniques can detect small variations in molecular content, linked with disease, showing promise for screening and early diagnosis. Biological fluids, particularly blood serum, are potentially valuable for diagnosis purposes. The so-called Low Molecular Weight Fraction (LMWF) contains the associated peptidome and metabolome and has been identified as potentially the most relevant molecular population for disease-associated biomarker research. Although vibrational spectroscopy can deliver a specific chemical fingerprint of the samples, the High Molecular Weight Fraction (HMWF), composed of the most abundant serum proteins, strongly dominates the response and ultimately makes the detection of minor spectral variations a challenging task. Spectroscopic detection of potential serum biomarkers present at relatively low concentrations can be improved using pre-analytical depletion of the HMWF. In the present study, human serum fractionation by centrifugal filtration was used prior to analysis by Attenuated Total Reflection infrared spectroscopy. Using a model sample based on glycine spiked serum, it is demonstrated that the screening of the LMWF can be applied to quantify blinded concentrations up to 50 times lower. Moreover, the approach is easily transferable to different bodily fluids which would support the development of more efficient and suitable clinical protocols exploring vibrational spectroscopy based ex-vivo diagnostic tools. Revealing serum LMWF for spectral serological diagnostic applications.

A review of novel analytical diagnostics for liquid biopsies: spectroscopic and spectrometric serum profiling of primary and secondary brain tumors

INTRODUCTION

Spectroscopic and spectrometric analysis of biological samples is regarded as quick, cost effective, easy to operate, and spectroscopic sample preparation involves minimal sample preparation.

RESULTS

Techniques like infrared (IR) spectroscopy, surface-enhanced laser desorption/ionization (SELDI)-mass spectroscopy (MS), and matrix-assisted laser desorption/ionization (MALDI) -MS could enable early diagnosis of cancer, disease monitoring, and assessment of treatment responses allowing refinement, if required.

DISCUSSION

Carrying out analytical testing within outpatient clinics would dramatically cut the time spent by patients attending different appointments, at different locations, save hospital time and resources but importantly would theoretically enable a reduction in mortality and morbidity. While the advantages of such a prospect seem obvious, this review aims to evaluate the use of human serum spectroscopic and spectrometric analysis as a diagnostic tool for brain cancers, creating a platform for the future of cancer diagnostics.

Introducing Discrete Frequency Infrared Technology for High-Throughput Biofluid Screening

Accurate early diagnosis is critical to patient survival, management and quality of life. Biofluids are key to early diagnosis due to their ease of collection and intimate involvement in human function. Large-scale mid-IR imaging of dried fluid deposits offers a high-throughput molecular analysis paradigm for the biomedical laboratory. The exciting advent of tuneable quantum cascade lasers allows for the collection of discrete frequency infrared data enabling clinically relevant timescales. By scanning targeted frequencies spectral quality, reproducibility and diagnostic potential can be maintained while significantly reducing acquisition time and processing requirements, sampling 16 serum spots with 0.6, 5.1 and 15% relative standard deviation (RSD) for 199, 14 and 9 discrete frequencies respectively. We use this reproducible methodology to show proof of concept rapid diagnostics; 40 unique dried liquid biopsies from brain, breast, lung and skin cancer patients were classified in 2.4 cumulative seconds against 10 non-cancer controls with accuracies of up to 90%.

Developing and understanding biofluid vibrational spectroscopy: a critical review

Biofluid vibrational spectroscopy, a promising tool for rapid disease diagnosis.

Transmission versus transflection mode in FTIR analysis of blood plasma: is the electric field standing wave effect the only reason for observed spectral distortions?

Fourier transform infrared (FTIR) microspectroscopy is assessed in terms of two techniques (i.e., transmission and transflection) as a method for rapid measurements of blood plasma.

Metabolomics insights into pathophysiological mechanisms of interstitial cystitis

Interstitial cystitis (IC), also known as painful bladder syndrome or bladder pain syndrome, is a chronic lower urinary tract syndrome characterized by pelvic pain, urinary urgency, and increased urinary frequency in the absence of bacterial infection or identifiable clinicopathology. IC can lead to long-term adverse effects on the patient's quality of life. Therefore, early diagnosis and better understanding of the mechanisms underlying IC are needed. Metabolomic studies of biofluids have become a powerful method for assessing disease mechanisms and biomarker discovery, which potentially address these important clinical needs. However, limited intensive metabolic profiles have been elucidated in IC. The article is a short review on metabolomic analyses that provide a unique fingerprint of IC with a focus on its use in determining a potential diagnostic biomarker associated with symptoms, a response predictor of therapy, and a prognostic marker.

Characterization of biofluids prepared by sessile drop formation

Sessile drop formation, also called drop deposition, has been studied as a potential medical diagnostic, but the effects of complex biofluid rheology on the final deposition pattern are not well understood. We studied two model biofluids, blood plasma and synovial fluid, when deposited onto slightly hydrophilic substrates forming a contact angle of 50-90°. Drops were imaged during the evaporation process and geometric properties of the drop, such as contact angle and drop height, were calculated from the images. The resulting dried biofluid drops were then examined using light microscopy and Raman spectroscopy to assess morphological and chemical composition of the dried drop. The effect of substrate contact angle (surface wetting) and fluid concentration was examined. We found that when biofluids are deposited onto slightly hydrophilic surfaces, with a contact angle of 50-90°, a ring-shaped deposit was formed. Analysis of the drying drop's geometric properties indicates that biofluid dynamics follow the piling model of drop formation, as proposed by Deegan et al. The final deposition pattern varied with substrate surface and concentration, as shown by light microscopy photos of dried drops. The chemical composition of the outer ring was minimally affected by substrate surface, but the spatial heterogeneity of protein distribution within the ring varied with concentration. These results indicate that biofluid drop deposition produces ring-shaped deposits which can be examined by multiple analytical techniques.