Investigating the Impact of Long-Term Alcohol Consumption on Serum Chemical Changes: Fourier Transform Infrared Spectroscopy for Human Blood Serum

Chronic alcohol consumption significantly impacts physiological and neurological functions. This study aimed to investigate the biochemical alterations in serum associated with long-term alcohol use using Fourier Transform Infrared (FTIR) spectroscopy. Serum samples from control and alcohol use disorder (AUD) were analyzed, and their spectra were compared. Multivariate analysis techniques, including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were employed to differentiate the groups. A machine learning model, Grid Search-Support Vector Machine Discriminant Analysis (GS-SVMDA), was developed to classify samples with high accuracy. Significant differences in the absorbance values of specific functional groups, particularly those associated with phospholipids, amides, and fatty acids revealed. The AUD exhibited lower levels of these biomolecules. The models achieved perfect classification, demonstrating the potential ofFTIR spectroscopy as a non-invasive tool for diagnosing AUD. Findings contribute to a better understanding of the biochemical mechanisms underlying alcohol addiction and may aid in the development of novel diagnostic and therapeutic strategies.

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis

Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better clinical practices for monitoring and managing pregnancy and childbirth. Fourier-transform infrared (FT-IR) spectroscopy as a non-invasive optical technique, it has the potential sensibility to detect biochemical components. This technology operates by meansuring the vibrational energy of molecular composition and structural changes occurring in the tissue. A total of 30 pregnant participants undergoing either spontaneous or induced labor were recruited. We detected several biochemical changes during labor, including a significant decrease in FT-IR spectral features associated with collagen and other extracellular matrix (ECM) proteins, attributed to collagen dispersion. Specifically, the amide I and amide II bands, which are indicative of protein secondary structure, showed marked reductions. Our results have demonstrated that FT-IR spectroscopy is sensitive to multiple biochemical remodeling changes in the cervix during labor. Traditional methods have limitations, either due to their invasiveness or insufficient sensitivity to detect subtle biochemical alterations, therefore, FT-IR spectroscopy may be a valuable noninvasive tool for objective cervical assessment to potentially guide clinical labor management.

Exploring the potential of Fourier transform-infrared spectroscopy of urine for non-invasive monitoring of inflammation associated with a kidney transplant

The global rise of end-stage renal disease is leading to an increase in kidney transplants. Graft survival is dependent on the occurrence of inflammation which can lead to cases of rejection. Traditional laboratory analyses often lack accuracy, and graft biopsies - the current gold standard - are considered invasive and risky. This highlights an unmet need for innovative diagnostic and monitoring methods of graft rejection and inflammation. This study explores the potential of Fourier-transform infrared spectroscopy of fresh urine for diagnosing kidney transplant inflammation. Urine samples were collected from kidney transplant patients who were under regular surveillance. An unsupervised method of spectral data analysis, especially Uniform Manifold Approximation and Projection (UMAP), was initially employed. However, it was unable to reveal a clear distinction between control and pathological conditions. Subsequently, two machine learning models - SVM and gradient boosting - were employed to categorise participants into pathologic or control groups, achieving a diagnostic accuracy of 77.78%. This study also evaluated other factors that could affect model performance, including urine biochemical composition, type of inflammation, and patient's medication history. The inherent variability of urine, attributed to factors such as diet and medications, poses challenges to identifying robust spectroscopic markers. Nevertheless, mid-infrared spectroscopy offers a promising, non-invasive approach for diagnosing kidney transplant disorders. Further research is essential to provide more advanced prediction models and meet the criteria for potential clinical deployment.

Fracture-related infection blood-based biomarkers: Diagnostic strategies

Fracture-related infections are significant postoperative complications that carry substantial patient burden and additional healthcare costs. Despite their impact on outcome, early diagnosis of these infections remains challenging due to current available tests lacking acceptable diagnostic parameters. This review compiles existing information on blood-based biomarkers that have been evaluated as early diagnostic tools and highlights the challenges in their reliability. To begin to overcome these challenges new avenues of biomarker discovery utilizing "omics" technologies and novel analytical methods are being investigated in recent years. It appears that, despite their complexity, these newer approaches may be the future in biomarker discovery for fracture-related infection diagnosis.

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.

Providing potential solutions by using FT-IR spectroscopy for biofluid analysis: Clinical impact of optical screening and diagnostic tests

BACKGROUND

Currently, the potential of FT-IR spectroscopy for rapid diagnosis of many pathologies has been demonstrated by numerous research studies including those targeting COVID-19 detection. However, the number of clinicians aware of this potential and who are willing to use spectroscopy in their clinics and hospitals is still negligible. In addition, lack of awareness creates a huge gap between clinicians and researchers involved in clinical translation of current FT-IR technology hence hindering initiatives to bring basic and applied research together for the direct benefit of patients.

METHODS

Knowledge and medical training on FT-IR on the side of clinicians should be one of the first steps to be able to integrate it into the list of complementary exams which may be requested by health professionals. Countless FT-IR applications could have a life-changing impact on patients' lives, especially screening and diagnostic tests involving biofluids such as blood, saliva and urine which are routinely non-invasively or minimally-invasively.

RESULTS

Blood may be the most difficult to obtain by the invasive method of collection, but much can be evaluated in its components, and areas such as hematology, infectiology, oncology and endocrinology can be directly benefited. Urine with a relatively simple collection method can provide pertinent information from the entire urinary system, including the actual condition of the kidneys. Saliva collection can be simpler for the patient and can provide information on diseases affecting the mouth and digestive system and can be used to diagnose diseases such as oral cancer in its early-stages. An unavoidable second step is the active involvement of industries to design robust and portable instruments for specific purposes, as the medical community requires user-friendly instruments of advanced computational algorithms. A third step resides in the legal situation involving the global use of the technique as a new diagnostic modality.

CONCLUSIONS

It is important to note that decentralized funds for variety of technologies hinders the training of clinical and medical professionals for the use of newly arising technologies and affect the engagement of these professionals with technology developers. As a result of decentralized funding, research efforts are spread out over a range of technologies which take a long time to get validated and translated to the clinic. Partnership over similar groups of technologies and efforts to test the same technologies while overcoming barriers posed to technology validation in different areas around the globe may benefit the clinical/medical, research and industry community globally.

A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders.

A spectroscopic liquid biopsy for the earlier detection of multiple cancer types

BACKGROUND

A rapid, low-cost blood test that can be applied to reliably detect multiple different cancer types would be transformational.

METHODS

In this large-scale discovery study (n = 2092 patients) we applied the Dxcover® Cancer Liquid Biopsy to examine eight different cancers. The test uses Fourier transform infrared (FTIR) spectroscopy and machine-learning algorithms to detect cancer.

RESULTS

Area under the receiver operating characteristic curve (ROC) values were calculated for eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We assessed the test performance when all eight cancer types were pooled to classify 'any cancer' against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of Stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%).

CONCLUSIONS

This spectroscopic blood test can effectively detect early-stage disease and can be fine-tuned to maximise either sensitivity or specificity depending on the requirements from different healthcare systems and cancer diagnostic pathways. This low-cost strategy could facilitate the requisite earlier diagnosis, when cancer treatment can be more effective, or less toxic.

STATEMENT OF TRANSLATIONAL RELEVANCE

The earlier diagnosis of cancer is of paramount importance to improve patient survival. Current liquid biopsies are mainly focused on single tumour-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed enough genetic material. This pan-omic liquid biopsy analyses the full complement of tumour and immune-derived markers present within blood derivatives and could facilitate the earlier detection of multiple cancer types. There is a low barrier to integrating this blood test into existing diagnostic pathways since the technology is rapid, simple to use, only minute sample volumes are required, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy described in this study has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.

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

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

Diagnosis and monitoring of hepatocellular carcinoma in Hepatitis C virus patients using attenuated total reflection Fourier transform infrared spectroscopy

BACKGROUND

Current diagnostic methods for assessment of hepatitis C virus related hepatocellular carcinoma and subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, to establish appropriate treatment strategies, are costly, invasive and requires multiple screening steps. This demands alternative diagnostic approaches that are cost-effective, time-efficient, and minimally invasive, while maintaining their efficacy for screening of hepatitis c virus related hepatocellular carcinoma. In this study, we propose that attenuated total reflection Fourier transform infrared in conjunction with principal component analysis - linear discriminant analysis and support vector machine multivariate algorithms holds a potential as a sensitive tool for the detection of hepatitis C virus-related hepatocellular carcinoma and the subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma.

METHODS

Freeze-dried sera samples collected from 31 hepatitis c virus related hepatocellular carcinoma patients and 30 healthy individuals, were used to acquire mid-infrared absorbance spectra (3500-900 cm-1) using attenuated total reflection Fourier transform infrared. Chemometric machine learning techniques were utilized to build principal component analysis - linear discriminant analysis and support vector machine discriminant models for the spectral data of hepatocellular carcinoma patients and healthy individuals. Sensitivity, specificity, and external validation on blind samples were calculated.

RESULTS

Major variations were observed in the two spectral regions i.e., 3500-2800 and 1800-900 cm-1. IR spectral signatures of hepatocellular carcinoma were reliably different from healthy individuals. Principal component analysis - linear discriminant analysis and support vector machine models computed 100% accuracy for diagnosing hepatocellular carcinoma. To classify the non-angio-invasive hepatocellular carcinoma/ angio-invasive hepatocellular carcinoma status, diagnostic accuracy of 86.21% was achieved for principal component analysis - linear discriminant analysis. While the support vector machine showed a training accuracy of 98.28% and a cross-validation accuracy of 82.75%. External validation for support vector machine based classification observed 100% sensitivity and specificity for accurately classifying the freeze-dried sera samples for all categories.

CONCLUSIONS

We present the specific spectral signatures for non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, which were prominently differentiated from healthy individuals. This study provides an initial insight into the potential of attenuated total reflection Fourier transform infrared to diagnose hepatitis C virus related hepatocellular carcinoma but also to further categorize into non-angio-invasive and angio-invasive hepatocellular carcinoma.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Application of Fourier transform infrared spectroscopy to detect biochemical changes in blood serum of obese patients

Obesity is frequently a significant risk factor for multiple obesity-associated diseases that have been increasing in prevalence worldwide. Anthropometric data such as body mass index, fat, and fat mass values are assessed for obesity. Therefore, we aimed to propose two Fourier transform infrared (FT-IR) spectral regions, 800-1800 cm^-1 and 2700-3000 cm^-1 , as sensitive potential band assignments for obesity-related biochemical changes. A total of 134 obese (n = 89) and controls (n = 45) biochemical characteristics and clinical parameters indicative of obesity were evaluated. The FT-IR spectra of dried blood serum were measured. Anthropometric data of the obese have the highest body mass index, %fat, and fat mass values compared to the healthy group (p < 0.01). Also, the triglyceride and high-density lipoprotein cholesterol levels were higher than in healthy subjects (p < 0.01). Principal component analysis (PCA) technique successfully distinguished obese and control groups in the fingerprint, accounting for 98.5% and 99.9% of the total variability (800-1800 cm^-1 ) and lipids (2700-3000 cm^-1 ) regions presented as 2D and 3D score plots. The loading results indicated that peaks corresponding to phosphonate groups, glucose, amide I, and lipid groups were shifted in the obese group, indicating their potential as biomarkers of obesity. This study suggests that FTIR analysis based on PCA can provide a detailed and reliable method for the analysis of blood serum in obese patients.

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

Use of ATR-FTIR spectroscopy to differentiate between cirrhotic/non-cirrhotic HCV patients

BACKGROUND

Conventional techniques to diagnose (HCV) and assess non-cirrhotic/cirrhotic status of the patient for appropriate treatment regime are expensive and invasive. Present available diagnostic tests are expensive as they include multiple screening steps. Therefore, there is a need of cost-effective, less time consuming and minimally invasive alternative diagnostic approaches can be used for effective screening. We propose that (ATR-FTIR) in conjunction with (PCA-LDA),(PCA-QDA) and (SVM) multivariate algorithms can be used as a sensitive tool for detection of HCV infection and to assess non-cirrhotic/cirrhotic status of patients.

METHODS

We used 105 sera samples, of which, 55 were from healthy and 50 were from HCV positive individuals. These 50 HCV positive patients were further classified into cirrhotic and non-cirrhotic categories using serum markers and imaging techniques. These samples were freeze dried prior to spectral acquisition then multivariate data classification algorithms were employed to classify these sample types.

RESULTS

PCA-LDA and SVM model computed the diagnostic accuracy of 100% for detection of HCV infection. To further classify the non-cirrhotic/cirrhotic status of a patient, diagnostic accuracy of 90.91% for PCA-QDA and 100% for SVM was observed. Internal and external validation for SVM based classifications observed 100% sensitivity and specificity. The confusion matrix generated by PCA-LDA model computed the validation and calibration accuracy showed 100% sensitivity and specificity, by using 2 PCs for HCV infected and healthy individuals. However, when the PCA QDA analysis was done to classify the non-cirrhotic sera samples from cirrhotic sera samples the diagnostic accuracy achieved was 90.91 % based on 7 PC's. SVM was also employed for classification and developed model showed the best results with 100% sensitivity and specificity when external validation was applied.

CONCLUSIONS

This study provides an initial insight that ATR-FTIR spectroscopy in conjugation with multivariate data classification tools holds a potentialnot onlytoeffectively diagnosis HCV infection but also to assess non-cirrhotic/cirrhotic status of patients.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

ATR-FTIR spectroscopy of plasma supported by multivariate analysis discriminates multiple sclerosis disease

Multiple sclerosis (MS) is one of the most common neurodegenerative diseases showing various symptoms both of physical and cognitive type. In this work, we used attenuated total reflection Fourier transformed infrared (ATR-FTIR) spectroscopy to analyze plasma samples for discriminating MS patients from healthy control individuals, and identifying potential spectral biomarkers helping the diagnosis through a quick non-invasive blood test. The cohort of the study consists of 85 subjects, including 45 MS patients and 40 healthy controls. The differences in the spectral features both in the fingerprint region (1800-900 cm^-1) and in the high region (3050-2800 cm^-1) of the infrared spectra were highlighted also with the support of different chemometric methods, to capture the most significant wavenumbers for the differentiation. The results show an increase in the lipid/protein ratio in MS patients, indicating changes in the level (metabolism) of these molecular components in the plasma. Moreover, the multivariate tools provided a promising rate of success in the diagnosis, with 78% sensitivity and 83% specificity obtained through the random forest model in the fingerprint region. The MS diagnostic tools based on biomarkers identification on blood (and blood component, like plasma or serum) are very challenging and the specificity and sensitivity values obtained in this work are very encouraging. Overall, the results obtained suggest that ATR-FTIR spectroscopy on plasma samples, requiring minimal or no manipulation, coupled with statistical multivariate approaches, is a promising analytical tool to support MS diagnosis through the identification of spectral biomarkers.

The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery

In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

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.

Liquid Biopsy for Pancreatic Cancer Detection Using Infrared Spectroscopy

Pancreatic cancer claims over 460,000 victims per year. The carbohydrate antigen (CA) 19-9 test is the blood test used for pancreatic cancer's detection; however, its levels can be raised in symptomatic patients with other non-malignant diseases, or with other tumors in the surrounding area. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has demonstrated exceptional potential in cancer diagnostics, and its clinical implementation could represent a significant step towards early detection. This proof-of-concept study, investigating the use of ATR-FTIR spectroscopy on dried blood serum, focused on the discrimination of both cancer versus healthy control samples, and cancer versus symptomatic non-malignant control samples, as a novel liquid biopsy approach for pancreatic cancer diagnosis. Machine learning algorithms were applied, achieving results of up to 92% sensitivity and 88% specificity when discriminating between cancers (n = 100) and healthy controls (n = 100). An area under the curve (AUC) of 0.95 was obtained through receiver operating characteristic (ROC) analysis. Balanced sensitivity and specificity over 75%, with an AUC of 0.83, were achieved with cancers (n = 35) versus symptomatic controls (n = 35). Herein, we present these results as demonstration that our liquid biopsy approach could become a simple, minimally invasive, and reliable diagnostic test for pancreatic cancer detection.

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.

Vibrational spectroscopy for the triage of traumatic brain injury CT priority and hospital admissions

Computed Tomogram (CT) brain imaging is routinely used to support clinical decision-making in patients with traumatic brain injury (TBI). However, only 7% of scans demonstrate evidence of TBI. The other 93% of scans contribute a significant cost to the healthcare system and a radiation risk to patients. There may be better strategies to identify which patients, particularly those with mild TBI, are at risk of deterioration and require hospital admission. We introduce a blood serum liquid biopsy that utilises attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy with machine learning algorithms as a decision-making tool to identify which mild TBI patients will most benefit from CT brain imaging. Serum samples were obtained from (n=298) patients who had acquired a TBI and were enrolled in CENTER-TBI, and from asymptomatic control patients (n=87). Injury patients (all severities) were stratified against non-injury controls. The mild TBI cohort was further examined by stratifying those who had at least one CT abnormality against those who had no CT abnormalities. The test performed exceptionally well in classifications of mild injury patients versus non-injury controls (sensitivity = 96.4% and specificity = 98.0%) and also provided a sensitivity of 80.2% when stratifying mild patients with at least one CT abnormality against those without. The results provided illustrate the test ability to identify 4 out of every 5 CT abnormalities and shows great promise to be introduced as a triage tool for CT priority in mild TBI patients.

Bovine Respiratory Syncytial Virus (BRSV) Infection Detected in Exhaled Breath Condensate of Dairy Calves by Near-Infrared Aquaphotomics

Bovine respiratory syncytial virus (BRSV) is a major contributor to respiratory disease in cattle worldwide. Traditionally, BRSV infection is detected based on non-specific clinical signs, followed by reverse transcriptase-polymerase chain reaction (RT-PCR), the results of which can take days to obtain. Near-infrared aquaphotomics evaluation based on biochemical information from biofluids has the potential to support the rapid identification of BRSV infection in the field. This study evaluated NIR spectra (n = 240) of exhaled breath condensate (EBC) from dairy calves (n = 5) undergoing a controlled infection with BRSV. Changes in the organization of the aqueous phase of EBC during the baseline (pre-infection) and infected (post-infection and clinically abnormal) stages were found in the WAMACS (water matrix coordinates) C1, C5, C9, and C11, likely associated with volatile and non-volatile compounds in EBC. The discrimination of these chemical profiles by PCA-LDA models differentiated samples collected during the baseline and infected stages with an accuracy, sensitivity, and specificity >93% in both the calibration and validation. Thus, biochemical changes occurring during BRSV infection can be detected and evaluated with NIR-aquaphotomics in EBC. These findings form the foundation for developing an innovative, non-invasive, and in-field diagnostic tool to identify BRSV infection in cattle.

ATR-Spin: an open-source 3D printed device for direct cytocentrifugation onto attenuated total reflectance crystals

Infrared spectroscopy (IR) enables the direct and rapid characterization of cells at the molecular level. Achieving a rapid and consistent cell preparation is critical for the development of point-of-care diagnostics for cell analysis. Here we introduce an open-source, 3D printed device for integrating the isolation, preconcentration, and measurement of attenuated total reflectance IR spectra of cells from biofluids. The tool comprises a disposable card for cytocentrifugation, equipped with magnets, which allows reproducible integration into the pathlength of the IR spectrophotometer. Preliminary results using cell culture media containing A549 cells indicate that this system enables a qualitative and quantitative characterization of cells down to 10 cells μL^-1 by using a single and cost-effective device and within a few minutes.

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.

Rapid Spectroscopic Liquid Biopsy for the Universal Detection of Brain Tumours

Simple Summary

Due to the non-specific symptoms of brain cancer (e.g., headaches or memory changes), gliomas will often remain undetected until they are larger or at a higher grade, reducing the patient’s likelihood of a good clinical outcome. Earlier detection and diagnosis of brain tumours is vital to improve patient outcomes, leading to safer surgeries and earlier treatments. A liquid biopsy for brain tumour would prove revolutionary however in order to detect disease earlier the liquid biopsy needs to be able to detect smaller tumours; and current liquid biopsies perform worse when detecting smaller or earlier stage tumours. Here, for the first time, we confirm the applicability of a validated spectroscopic liquid biopsy approach to detect both small and low-grade gliomas proving that the spectroscopic liquid biopsy approach is insensitive to tumour volume unlike other liquid biopsies.

Abstract

Background: To support the early detection and diagnosis of brain tumours we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumour diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumour genetic material) report a lower classification accuracy for early-stage tumours. In this manuscript we present an investigation into the link between brain tumour volume and liquid biopsy test performance. Methods: In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligoastrocytoma and oligodendroglioma)) tumour volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumour patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Results: Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumour volumes as small as 0.2 cm³ were correctly identified. Conclusions: Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis.