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

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

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.

A portable dry film FTIR instrument for industrial food and bioprocess applications

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

Insights into polycrystalline microstructure of blood films with 3D Mueller matrix imaging approach

This study introduces a novel approach in the realm of liquid biopsies, employing a 3D Mueller-matrix (MM) image reconstruction technique to analyze dehydrated blood smear polycrystalline structures. Our research centers on exploiting the unique optical anisotropy properties of blood proteins, which undergo structural alterations at the quaternary and tertiary levels in the early stages of diseases such as cancer. These alterations manifest as distinct patterns in the polycrystalline microstructure of dried blood droplets, offering a minimally invasive yet highly effective method for early disease detection. We utilized a groundbreaking 3D MM mapping technique, integrated with digital holographic reconstruction, to perform a detailed layer-by-layer analysis of partially depolarizing dry blood smears. This method allows us to extract critical optical anisotropy parameters, enabling the differentiation of blood films from healthy individuals and prostate cancer patients. Our technique uniquely combines polarization-holographic and differential MM methodologies to spatially characterize the 3D polycrystalline structures within blood films. A key advancement in our study is the quantitative evaluation of optical anisotropy maps using statistical moments (first to fourth orders) of linear and circular birefringence and dichroism distributions. This analysis provides a comprehensive characterization of the mean, variance, skewness, and kurtosis of these distributions, crucial for identifying significant differences between healthy and cancerous samples. Our findings demonstrate an exceptional accuracy rate of over 90 % for the early diagnosis and staging of cancer, surpassing existing screening methods. This high level of precision and the non-invasive nature of our technique mark a significant advancement in the field of liquid biopsies. It holds immense potential for revolutionizing cancer diagnosis, early detection, patient stratification, and monitoring, thereby greatly enhancing patient care and treatment outcomes. In conclusion, our study contributes a pioneering technique to the liquid biopsy domain, aligning with the ongoing quest for non-invasive, reliable, and efficient diagnostic methods. It opens new avenues for cancer diagnosis and monitoring, representing a substantial leap forward in personalized medicine and oncology.

Soluto-thermal Marangoni convection in stationary micro-bioreactors on heated substrates: Tool for in vitro diagnosis of PSA

The investigation of antigen-laden droplet deposition patterns on antibody-immobilized substrates has potential for disease detection. Stationary droplets that contain antigens on surfaces immobilized with antibodies can function as microreactors. Temperature modulation enhances reaction efficiency and reduces detection time in droplet-based systems. Thus, the aim of this study is to explore the impact of substrate heating on the structures of protein deposits and the influence of substrate temperature on thermo-solutal Marangoni convection within the droplets. Previous research has explored deposition patterns as diagnostic tools, but limited investigations have focused on the effects of substrate heating on protein deposit structures and the influence of substrate temperature on thermo-solutal Marangoni convection within droplets, creating a knowledge gap. In this study, we conducted experiments to explore how heating the substrate affects the deposition patterns of droplets containing prostate-specific antigen (PSA) on a substrate immobilized with anti-PSA IgG. Additionally, we investigated the thermo-solutal Marangoni convection within these droplets. Our findings reveal distinct deposition patterns classified into dendritic structures (heterogeneous), transitional patterns, and needle-like (homogeneous) structures. The presence of prominent coffee rings and the variation in crystal size across different groups highlight the interplay between thermal and solutal Marangoni advection. Entropy analysis provides insights into structural differences within and between patterns. This work optimizes substrate temperatures for reduced evaporation and detection times while preserving protein integrity, advancing diagnostic tool development, and improving understanding of droplet-based systems.

'When is a hotspot a good nanospot' - review of analytical and hotspot-dominated surface enhanced Raman spectroscopy nanoplatforms

Substrate development in surface-enhanced Raman spectroscopy (SERS) continues to attract research interest. In order to determine performance metrics, researchers in foundational SERS studies use a variety of experimental means to characterize the nature of substrates. However, often this process would appear to be performed indiscriminately without consideration for the physical scale of the enhancement phenomena. Herein, we differentiate between SERS substrates whose primary enhancing structures are on the hundreds of nanometer scale (analytical SERS nanosubstrates) and those whose main mechanism derives from nanometric-sized gaps (hot-spot dominated SERS substrates), assessing the utility of various characterization methods for each substrate class. In this context, characterization approaches in white-light spectroscopy, electron beam methods, and scanning probe spectroscopies are reviewed. Tip-enhanced Raman spectroscopy, wavelength-scanned SERS studies, and the impact of surface hydrophobicity are also discussed. Conclusions are thus drawn on the applicability of each characterization technique regarding amenability for SERS experiments that have features at different length scales. For instance, while white light spectroscopy can provide an indication of the plasmon resonances associated with 10 s-100 s nm-scale structures, it may not reveal information about finer surface texturing on the true nm-scale, critical for SERS' sensitivity, and in need of investigation via scanning probe techniques.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

Conducting Gold Nanoparticle Films via Sessile Drop Evaporation

The deposit patterns obtained from the evaporation of drops containing insoluble solute particles are vital for several technologies, including inkjet printing and optical and electronic device manufacturing. In this work, we consider the evaporation of an aqueous reaction mixture typically used for gold nanoparticle (AuNP) synthesis. The patterns obtained from the evaporation-driven assembly of in situ generated AuNPs are studied using optical microscopy and SEM analyses. The evaporation of drops withdrawn at different reaction times is found to significantly influence the distribution of AuNPs in the dried patterns. The evolution of the deposit patterns is also explored by drying multiple drops on the solid substrate, wherein a drop of a fresh reaction mixture is introduced over the deposit pattern left by the evaporation of the drop dispensed at an earlier time. Using quantitative image analysis, we show that the interparticle separation between the AuNPs in the dried patterns left on the solid substrate decreases when the number of drops is increased. We find optimal conditions to achieve solid-supported AuNP films, wherein the particles are in close physical contact, leading to a conducting deposit. The current through the AuNP deposit is found to increase with increase in the number of drops due to evaporation-driven self-assembly of AuNPs into branch-like structures with reduced interparticle separation. In addition, we also show that it is possible to produce conducting AuNP deposits by drying multiple drops withdrawn from the same reaction mixture. The evaporation-driven assembly of the in situ grown nanoparticles from a reaction mixture presented in this work can be further exploited in optical and electronic device fabrication.

Dried blood drops on vertical surfaces

The analysis of structures in dried droplets has made it possible to detect the presence and conformational state of macromolecules in relevant biofluids. Therefore, the implementation of novel drying strategies for pattern formation could facilitate the identification of biomarkers for the diagnosis of pathologies. We present an experimental study of patterns formed by evaporating water-diluted blood droplets on a vertical surface. Three significant morphological features were observed in vertical droplet deposits: (1) The highest concentration of non-volatile molecules is consistently deposited in the lower part of the droplet, regardless of erythrocyte concentration. (2) The central region of deposits decreases rapidly with hematocrit; (3) At high erythrocyte concentrations (36-40% HCT), a broad coating of blood serum is produced in the upper part of the deposit. These findings are supported by the radial intensity profile, the relative thickness of the crown, the aspect ratio of the deformation, the relative area of the central region, and the Entropy of the Gray Level Co-occurrence Matrix Entropy (GLCM). Moreover, we explore the pattern formation during the drying of vertical blood drops. We found that hematocrit concentration has a significant impact on droplet drying dynamics. Finally, we conducted a proof-of-concept test to investigate the impact of vertical droplet evaporation on blood droplets with varying lipid concentrations. The results revealed that it is possible to differentiate between deposits with normal, slightly elevated, and moderately elevated lipid levels using only the naked eye.

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer

Bladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects. The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity. Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer.

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

The identification of multidrug-resistant strains from E. coli species responsible for diarrhea in calves still faces many laboratory limitations and is necessary for adequately monitoring the microorganism spread and control. Then, there is a need to develop a screening tool for bacterial strain identification in microbiology laboratories, which must show easy implementation, fast response, and accurate results. The use of FTIR spectroscopy to identify microorganisms has been successfully demonstrated in the literature, including many bacterial strains; here, we explored the FTIR potential for multi-resistant E. coli identification. First, we applied principal component analysis to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution; then, we improved these results by adequately selecting the main principal components which most contribute to group separation. Finally, using machine learning algorithms, a predicting model showed 75% overall accuracy, demonstrating the method's viability as a screaming test for microorganism identification.

Influence of aluminum and iron chlorides on the parameters of zigzag patterns on films dried from BSA solutions

The relationships between the structural and aggregational state of bovine serum albumin (BSA) and the specific length and total number of zigzag pattern segments of the film textures formed upon drying biopolymer solutions with aluminum and iron chlorides have been shown. To obtain films, saline solutions of BSA were dried in a glass cuvette under thermostatically controlled conditions. It is shown that the formation of zigzag structures is sensitive to the influence of aluminum chlorides Al³⁺ and iron chlorides Fe³⁺ and depend on the concentration of AlCl₃ and FeCl₃. This may be due to a change in the charge and size of BSA particles and due to a change in conformation or a violation of the structure of BSA. These factors, in turn, affect the hydration of the solution components and the structural state of free water in solution, which presumably also affects the formation of zigzag structures. It is established that the analysis of the specific length and the number of segments of zigzag patterns makes it possible to evaluate changes in the state of biopolymers in the initial solution during structural changes and aggregation.

Recent Advances in Two-Dimensional MXene-Based Electrochemical Biosensors for Sweat Analysis

Sweat, a biofluid secreted naturally from the eccrine glands of the human body, is rich in several electrolytes, metabolites, biomolecules, and even xenobiotics that enter the body through other means. Recent studies indicate a high correlation between the analytes' concentrations in the sweat and the blood, opening up sweat as a medium for disease diagnosis and other general health monitoring applications. However, low concentration of analytes in sweat is a significant limitation, requiring high-performing sensors for this application. Electrochemical sensors, due to their high sensitivity, low cost, and miniaturization, play a crucial role in realizing the potential of sweat as a key sensing medium. MXenes, recently developed anisotropic two-dimensional atomic-layered nanomaterials composed of early transition metal carbides or nitrides, are currently being explored as a material of choice for electrochemical sensors. Their large surface area, tunable electrical properties, excellent mechanical strength, good dispersibility, and biocompatibility make them attractive for bio-electrochemical sensing platforms. This review presents the recent progress made in MXene-based bio-electrochemical sensors such as wearable, implantable, and microfluidic sensors and their applications in disease diagnosis and developing point-of-care sensing platforms. Finally, the paper discusses the challenges and limitations of MXenes as a material of choice in bio-electrochemical sensors and future perspectives on this exciting material for sweat-sensing applications.

Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases

Autoimmune diseases (AIDs) are poorly understood clinical syndromes due to breakdown of immune tolerance towards specific types of self-antigens. They are generally associated with an inflammatory response mediated by lymphocytes T, autoantibodies or both. Ultimately, chronic inflammation culminates in tissue damages and clinical manifestations. AIDs affect 5% of the world population, and they represent the main cause of fatality in young to middle-aged females. In addition, the chronic nature of AIDs has a devastating impact on the patient's quality of life. It also places a heavy burden on the health care system. Establishing a rapid and accurate diagnosis is considered vital for an ideal medical management of these autoimmune disorders. However, for some AIDs, this task might be challenging. Vibrational spectroscopies, and more particularly Fourier-transform infrared (FTIR) spectroscopy, have emerged as universal analytical techniques with promising applications in the diagnosis of various types of malignancies and metabolic and infectious diseases. The high sensitivity of these optical sensing techniques and their minimal requirements for test reagents qualify them to be ideal analytical techniques. The aim of the current review is to explore the potential applications of FTIR spectroscopy in the diagnosis and management of most common AIDs. It also aims to demonstrate how this technique has contributed to deciphering the biochemical and physiopathological aspects of these chronic inflammatory diseases. The advantages that can be offered by this optical sensing technique over the traditional and gold standard methods used in the diagnosis of these autoimmune disorders have also been extensively discussed.

Periodontal disease in chronic kidney disease patients: salivomics by Fourier-transform infrared spectroscopy

It has been reported that 58% of individuals with chronic kidney disease (CKD) have moderate to advanced periodontitis due to alterations of pH and biochemical composition in the saliva. In fact, the composition of this important biofluid may be modulated by systemic disorders. Here we investigate the micro-reflectance Fourier-transform infrared spectroscopy (FTIR) spectra of saliva that CKD patients submitted to periodontal treatment, aiming to identify spectral biomarkers of kidney disease evolution and the effectiveness of periodontal treatment, proposing possible biomarkers of disease evolution. Saliva from 24 CKD patients-stage-5 men, 29 to 64 years old-was evaluated in (i) patients starting periodontal treatment; (ii) patients 30 days after periodontal treatment; and (iii) patients 90 days after periodontal treatment. Our findings indicated that there are statistically relevant changes among the groups after 30 and 90 days of periodontal treatment, when considering the overall spectra in the fingerprint region (800-1800cm^-1). The key bands presenting good prediction power (area under the receiver operating characteristic curve >0.70) were related to poly (ADP-ribose) polymerase (PARP) conjugated to DNA at 883, 1031, and 1060cm^-1 (carbohydrates at 1043 and 1049cm^-1) and triglycerides (1461cm^-1). Interestingly when analyzing the derivative spectra in the secondary structure region (1590-1700cm^-1), we detected over-expression of the β-sheet class of secondary structures in 90 days of periodontal treatment, possibly related to over-expression of human B-defensins. Conformational changes in ribose sugar in this region corroborate the interpretation concerning PARP detection. To our knowledge, PARP was detected for the first time in saliva samples of stage-5 CKD patients by FTIR. All observed changes were correctly interpreted in terms of intensive apoptosis and dyslipidemia due to kidney disease progression. Biomarkers due to CKD predominate in saliva, and the relative improvement in the periodontal state did not cause remarkable changes in the spectra of saliva.

Drying of bio-colloidal sessile droplets: Advances, applications, and perspectives

Drying of biologically-relevant sessile droplets, including passive systems such as DNA, proteins, plasma, and blood, as well as active microbial systems comprising bacterial and algal dispersions, has garnered considerable attention over the last decades. Distinct morphological patterns emerge when bio-colloids undergo evaporative drying, with significant potential in a wide range of biomedical applications, spanning bio-sensing, medical diagnostics, drug delivery, and antimicrobial resistance. Consequently, the prospects of novel and thrifty bio-medical toolkits based on drying bio-colloids have driven tremendous progress in the science of morphological patterns and advanced quantitative image-based analysis. This review presents a comprehensive overview of bio-colloidal droplets drying on solid substrates, focusing on the experimental progress during the last ten years. We provide a summary of the physical and material properties of relevant bio-colloids and link their native composition (constituent particles, solvent, and concentrations) to the patterns emerging due to drying. We specifically examined the drying patterns generated by passive bio-colloids (e.g., DNA, globular, fibrous, composite proteins, plasma, serum, blood, urine, tears, and saliva). This article highlights how the emerging morphological patterns are influenced by the nature of the biological entities and the solvent, micro- and global environmental conditions (temperature and relative humidity), and substrate attributes like wettability. Crucially, correlations between emergent patterns and the initial droplet compositions enable the detection of potential clinical abnormalities when compared with the patterns of drying droplets of healthy control samples, offering a blueprint for the diagnosis of the type and stage of a specific disease (or disorder). Recent experimental investigations of pattern formation in the bio-mimetic and salivary drying droplets in the context of COVID-19 are also presented. We further summarized the role of biologically active agents in the drying process, including bacteria, algae, spermatozoa, and nematodes, and discussed the coupling between self-propulsion and hydrodynamics during the drying process. We wrap up the review by highlighting the role of cross-scale in situ experimental techniques for quantifying sub-micron to micro-scale features and the critical role of cross-disciplinary approaches (e.g., experimental and image processing techniques with machine learning algorithms) to quantify and predict the drying-induced features. We conclude the review with a perspective on the next generation of research and applications based on drying droplets, ultimately enabling innovative solutions and quantitative tools to investigate this exciting interface of physics, biology, data sciences, and machine learning.

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.

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.

Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns

The development of simple and accurate methods to predict mutations in proteins remains an unsolved challenge in modern biochemistry. It is discovered that critical information about primary and secondary peptide structures can be inferred from the stains left behind by their drying droplets. To analyze the complex stain patterns, deep-learning neuronal networks are challenged with polarized light microscopy images derived from the drying droplet deposits of a range of amyloid beta (1-42) (Aβ₄₂ ) peptides. These peptides differ in a single amino acid residue and represent hereditary mutants of Alzheimer's disease. Stain patterns are not only reproducible but also result in comprehensive stratification of eight amyloid beta (Aβ) variants with predictive accuracies above 99%. Similarly, peptide stains of a range of distinct Aβ₄₂ peptide conformations are identified with accuracies above 99%. The results suggest that a method as simple as drying a droplet of a peptide solution onto a solid surface may serve as an indicator of minute, yet structurally meaningful differences in peptides' primary and secondary structures. Scalable and accurate detection schemes for stratification of conformational and structural protein alterations are critically needed to unravel pathological signatures in many human diseases such as Alzheimer's and Parkinson's disease.

Subtype discrimination of acute myeloid leukemia based on plasma SERS technique

Acute myeloid leukemia (AML) is a common hematologic malignancy. To this day, diagnose of AML and its genetic mutation still rely on invasive and time-consuming methods. In this study, 222 plasma samples were collected to discuss the performance of surface-enhanced Raman spectroscopy (SERS) to discriminate AML subtype acute promyelocytic leukemia and acute monocytic leukemia based on plasma. The Ag nanoparticles-based SERS technique was used to explore the biochemical differences among different AML subtypes. With the help of powerful supervised and unsupervised algorithms, the performance using the whole spectra and band intensities was confirmed to identify different subtypes of AML. The results demonstrated the intensities of several bands and band-intensity ratios were significantly different between groups, thus related to the discrimination of several AML subtypes and control. Combining indexes of band-intensity ratios, the result of multi-indexes ROC has excellent performance in differentiating AML patient with healthy control. Our work demonstrated the great potential of SERS technique as a rapid and micro detection method in clinical laboratory field, it's a new and powerful tool for analyzing human blood 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.

Disposable Coverslip for Rapid Throughput Screening of Malaria Using Attenuated Total Reflection Spectroscopy

Malaria is considered to be one of the most catastrophic health issues in the whole world. Vibrational spectroscopy is a rapid, robust, label-free, inexpensive, highly sensitive, nonperturbative, and nondestructive technique with high diagnostic potential for the early detection of disease agents. In particular, the fingerprinting capability of attenuated total reflection spectroscopy is promising as a point-of-care diagnostic tool in resource-limited areas. However, improvements are required to expedite the measurements of biofluids, including the drying procedure and subsequent cleaning of the internal reflection element to enable high throughput successive measurements. As an alternative, we propose using an inexpensive coverslip to reduce the sample preparation time by enabling multiple samples to be collectively dried together under the same temperature and conditions. In conjunction with partial least squares regression, attenuated total reflection spectroscopy was able to detect and quantify the parasitemia with root mean square error of cross-validation and R² values of 0.177 and 0.985, respectively. Here, we characterize an inexpensive, disposable coverslip for the high throughput screening of malaria parasitic infections and thus demonstrate an alternative approach to direct deposition of the sample onto the internal reflection element.

Clinical validation of a spectroscopic liquid biopsy for earlier detection of brain cancer

Background

Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reducing the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most.

Methods

Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover®Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease.

Results

Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields sensitivity of 47% with 90% specificity (PPV 28.4%).

Conclusions

This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients, while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care.

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.

Patterns from dried drops as a characterisation and healthcare diagnosis technique, potential and challenges: A review

When particulate-laden droplets evaporate, they leave behind complex patterns on the substrate depending on their composition and the dynamics of their evaporation. Over the past two decades, there has been an increased interest in interpreting these patterns due to their numerous applications in biomedicine, forensics, food quality analysis and inkjet printing. The objective of this review is to investigate the use of patterns from dried drops as a characterisation and diagnosis technique. The patterns left behind by dried drops of various complex fluids are categorised. The potential applications of these patterns are presented, focussing primarily on healthcare, where the future impact could be greatest. A discussion on the limitations which must be overcome and prospective works that may be carried out to allow for widespread implementation of this technique is presented in conclusion.

Extracellular Vesicles and Their Relationship with the Heart-Kidney Axis, Uremia and Peritoneal Dialysis

Cardiorenal syndrome (CRS) is described as primary dysfunction in the heart culminating in renal injury or vice versa. CRS can be classified into five groups, and uremic toxin (UT) accumulation is observed in all types of CRS. Protein-bound uremic toxin (PBUT) accumulation is responsible for permanent damage to the renal tissue, and mainly occurs in CRS types 3 and 4, thus compromising renal function directly leading to a reduction in the glomerular filtration rate (GFR) and/or subsequent proteinuria. With this decrease in GFR, patients may need renal replacement therapy (RRT), such as peritoneal dialysis (PD). PD is a high-quality and home-based dialysis therapy for patients with end-stage renal disease (ESRD) and is based on the semi-permeable characteristics of the peritoneum. These patients are exposed to factors which may cause several modifications on the peritoneal membrane. The presence of UT may harm the peritoneum membrane, which in turn can lead to the formation of extracellular vesicles (EVs). EVs are released by almost all cell types and contain lipids, nucleic acids, metabolites, membrane proteins, and cytosolic components from their cell origin. Our research group previously demonstrated that the EVs can be related to endothelial dysfunction and are formed when UTs are in contact with the endothelial monolayer. In this scenario, this review explores the mechanisms of EV formation in CRS, uremia, the peritoneum, and as potential biomarkers in peritoneal dialysis.

A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning

Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis. In this study was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate, and easy canine visceral leishmaniasis diagnosis. Canine blood sera of 20 noninfected, 20 Leishmania infantum, and eight Trypanosoma evansi infected dogs were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.

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.

Diagnostic tests based on pattern formation in drying body fluids - A mapping review

There are numerous diagnostic tests based on pattern formation in desiccating body fluids, where the pattern or some of its characteristics constitute the diagnostic test outcome. However, partially due to the development in different time periods, and partially due to publications in languages different from English, most of these diagnostic tests exist as separate approaches and have never been grouped, systematized, nor compared with each other. In the present mapping review, we performed a wide literature search with the aim to collect all diagnostic tests based on pattern formation in desiccating body fluids. Furthermore, we grouped the identified diagnostic tests according to their experimental protocols, type of body fluids investigated, and target conditions, and propose so for the first time a classification of different diagnostic tests based on pattern formation in desiccating body fluids. The literature search revealed 1603 publications, out of which 141 were included into the review. Following three main classification criteria (way of deposition of the fluid for desiccation, addition of reagents, and spatial restrictions during evaporation), we identified six different methods; following a further classification concerning the analyzed body fluid we identified 30 different diagnostic tests based on pattern formation in evaporating body fluids. Amongst these tests are well-known procedures such as ferning tests (tear ferning for the assessment of tear film quality, saliva and cervical mucus ferning for the detection of the fertile period, and amniotic fluid ferning for the diagnosis of fetal membrane rupture), whereas other tests are less well-established. In the latter group, the most frequently investigated body fluids were serum, saliva, and blood; the most frequently addressed target conditions were cancer, inflammation, and benign tumors. We recommend conducting further systematic reviews and meta-analyses concerning groups of methods addressing the same target condition.

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.

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.

The potential of FT-IR spectroscopy for improving healthcare in sepsis - An animal model study

Fourier Transform-Infrared (FT-IR) absorption spectroscopy has been used to investigate pathophysiological changes caused by sepsis. Sepsis has been defined as a potentially fatal organic dysfunction caused by a dysregulated host response to infection and can lead a patient to risk of death. This study used samples consisting of the blood plasma of mice which were induced to sepsis state, compared to a healthy group using FT-IR associated with attenuated total reflectance (ATR) spectroscopy. For statistical analysis, principal components analysis (PCA) and linear discriminant analysis (LDA) were applied, independently, to the second derivative spectra of both the fingerprint (900-1800 cm^-1) and the high wavenumber (2800-3100 cm^-1) regions. The technique efficiently differentiated the blood plasma of the two groups, sepsis and healthy mice, the analysis indicating that fatty acids and lipids in the blood samples could be an important biomarker of sepsis.

Effects of substrate temperature on patterns produced by dried droplets of proteins

Rapid diagnosis provides better clinical management of patients, helps control possible outbreaks, and increases survival. The study of deposits produced by the evaporation of droplets is a useful tool in the diagnosis of some health problems. With the aim to improve diagnostic time in clinical practice where we use the evaporation of droplets, we explored the effects of substrate temperature on pattern formation of dried droplets in globular protein solutions. Three deposit groups were observed: "functional" patterns (from 25 to 37 ^∘C), "transition" patterns (from 44 to 50 ^∘C), and "eye" patterns (from 58 to 63 ^∘C). The dried droplets of the first two groups show a ring structure ("coffee-ring") that confines a great diversity of aggregates such as needle-like structures, tiny blade-shape crystals, highly symmetrical crystallization patterns, and amorphous salt aggregates. In contrast, the "eye" patterns are deposits with a large inner aggregate surrounded by a coffee ring, and they can appear from the evaporation of droplets in protein binary mixtures and blood serum. Interestingly, the unfolding proteins correlates with the formation of "eye" patterns. We measured stain diameter, "coffee-ring" thickness, radial density profile, and entropy computed by GLCM-statistics to quantify the structural differences among deposit groups. We found that "functional" patterns are structurally indistinguishable among them, but they are clearly different from elements of the other deposit groups. An exponential decay function describes pattern formation time as a function of substrate temperature, which is independent from protein concentration. Patterns formation at 32 ^∘C takes place up to 63% less time and preserves the structural characteristics of dried droplets in proteins formed at room temperature. Therefore, we argue that droplet evaporation at this substrate temperature could be an excellent candidate to make a more efficient diagnosis based on droplet evaporation of biofluids.

Early diagnosis of brain tumours using a novel spectroscopic liquid biopsy

Early diagnosis of brain tumours is challenging and a major unmet need. Patients with brain tumours most often present with non-specific symptoms more commonly associated with less serious diagnoses, making it difficult to determine which patients to prioritize for brain imaging. Delays in diagnosis affect timely access to treatment, with potential impacts on quality of life and survival. A test to help identify which patients with non-specific symptoms are most likely to have a brain tumour at an earlier stage would dramatically impact on patients by prioritizing demand on diagnostic imaging facilities. This clinical feasibility study of brain tumour early diagnosis was aimed at determining the accuracy of our novel spectroscopic liquid biopsy test for the triage of patients with non-specific symptoms that might be indicative of a brain tumour, for brain imaging. Patients with a suspected brain tumour based on assessment of their symptoms in primary care can be referred for open access CT scanning. Blood samples were prospectively obtained from 385 of such patients, or patients with a new brain tumour diagnosis. Samples were analysed using our spectroscopic liquid biopsy test to predict presence of disease, blinded to the brain imaging findings. The results were compared to the patient’s index brain imaging delivered as per standard care. Our test predicted the presence of glioblastoma, the most common and aggressive brain tumour, with 91% sensitivity, and all brain tumours with 81% sensitivity, and 80% specificity. Negative predictive value was 95% and positive predictive value 45%. The reported levels of diagnostic accuracy presented here have the potential to improve current symptom-based referral guidelines, and streamline assessment and diagnosis of symptomatic patients with a suspected brain tumour.

Advances in the development paradigm of biosample-based biosensors for early ultrasensitive detection of alzheimer's disease

This review highlights current developments, challenges, and future directions for the use of invasive and noninvasive biosample-based small biosensors for early diagnosis of Alzheimer's disease (AD) with biomarkers to incite a conceptual idea from a broad number of readers in this field. We provide the most promising concept about biosensors on the basis of detection scale (from femto to micro) using invasive and noninvasive biosamples such as cerebrospinal fluid (CSF), blood, urine, sweat, and tear. It also summarizes sensor types and detailed analyzing techniques for ultrasensitive detection of multiple target biomarkers (i.e., amyloid beta (Aβ) peptide, tau protein, Acetylcholine (Ach), microRNA137, etc.) of AD in terms of detection ranges and limit of detections (LODs). As the most significant disadvantage of CSF and blood-based detection of AD is associated with the invasiveness of sample collection which limits future strategy with home-based early screening of AD, we extensively reviewed the future trend of new noninvasive detection techniques (such as optical screening and bio-imaging process). To overcome the limitation of non-invasive biosamples with low concentrations of AD biomarkers, current efforts to enhance the sensitivity of biosensors and discover new types of biomarkers using non-invasive body fluids are presented. We also introduced future trends facing an infection point in early diagnosis of AD with simultaneous emergence of addressable innovative technologies.

Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application

In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient's plasma and serum sample using vibrational spectroscopy.

Silver-coated silicon nanowire platform discriminates genomic DNA from normal and malignant human epithelial cells using label-free Raman spectroscopy

Genomic deoxyribonucleic acid (DNA) stores and carries the information required to maintain and replicate cellular life. While much efforts have been devoted in decoding the sequence of DNA basis to detect the genetic mutations related to cancer disease, it is becoming clear that physical properties, like structural conformation, stiffness and shape, can play an important role to recognize DNA modifications. Here, silver-coated silicon nanowires (Ag/SiNWs) are exploited as Raman spectroscopic platform to easily discriminate healthy and cancer genomic DNA, extracted from human normal skin and malignant melanoma cells, respectively. In particular, aqueous DNA droplets are directly deposited onto a forest of Ag/SiNWs and Raman maps are acquired after sample dehydration. By applying principal component analysis (PCA) to the Raman spectra collected within the droplets, healthy and cancer cell DNA can be distinguished without false negative identifications and with few false positive results (< 2%). The discrimination occurs regardless the analysis of specific DNA sequencing, but through Raman bands strictly related to the interfacing of the DNA and the NWs. The observed phenomenon can be ascribed to conformational differences and/or diverse charge properties between healthy and cancer cell DNA determining a different arrangement of the molecules adsorbed onto the NWs upon water evaporation. The unique interaction with DNA and facile fabrication technology make Ag/SiNWs an effective platform for a robust, rapid and label-free cancer diagnosis, as well as a potential tool to investigate physical properties of DNA.

Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy

Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

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.

Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying"

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies >80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients.

Optimum Sample Thickness for Trace Analyte Detection with Field-Resolved Infrared Spectroscopy

The strong absorption of liquid water in the infrared (IR) molecular fingerprint region constitutes a challenge for applications of vibrational spectroscopy in chemistry, biology, and medicine. While high-power IR laser sources enable the penetration of ever thicker aqueous samples, thereby mitigating the detrimental effects of strong attenuation on detection sensitivity, a basic advantage of heterodyne-measurement-based methods has—to the best of our knowledge—not been harnessed in broadband IR measurements to date. Here, employing field-resolved spectroscopy (FRS), we demonstrate in theory and experiment fundamental advantages of techniques whose signal-to-noise ratio (SNR) scales linearly with the electric field over those whose SNR scales linearly with radiation intensity, including conventional Fourier-transform infrared (FTIR) and direct absorption spectroscopy. Field-scaling brings about two major improvements. First, it squares the measurement dynamic range. Second, we show that the optimum interaction length with samples for SNR-maximized measurements is twice the value usually considered to be optimum for FTIR devices. In order to take full advantage of these properties, the measurement must not be significantly affected by technical noise, such as intensity fluctuations, which are common for high-power sources. Recently, it has been shown that subcycle, nonlinear gating of the molecular fingerprint signal renders FRS robust against intensity noise. Here, we quantitatively demonstrate this advantage of FRS for thick aqueous samples. We report sub-μg/mL detection sensitivities for transmission path lengths up to 80 μm and a limit of detection in the lower μg/mL range for transmission paths as long as 200 μm.

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.

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets

One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.