Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva for Breast Cancer Diagnosis

Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning

The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy technique for non-invasive tests of blood-glucose, both with state-of-the-art instrumentation-retrofits and with intelligent spectral-datamining tools. First, the signal-to-noise performance of FTIR in measuring the spectral features of a glucose solution containing bovine serum albumin is improved by 2-3 times with the common single-pass attenuated total-reflection setup replaced by a multi-passes-reflections setup. Second, replacing the ordinary infrared lamp with a quantum cascade laser further improves the signal-to-noise by 3 times. The performance of the upgraded spectrometer in screening hyperglycemia is gauged by the accuracy of such screens derived from 100 repetitive spectral-measurements per glucose concentration, for 24 glucose concentrations spanning the range of 70-300 mg/dL, a range which covers the blood-glucose contents of all non-diabetic and diabetic human-subjects. Third, intelligent datamining methods are exploited to implement decision trees for screening hyperglycemia. Their decisions are mapped into a confusion matrix and the matrix-elements are used to calculate the accuracy merits of each method. Evidently, the accuracy of the multi-passes-FTIR with the standard principal-components datamining method is 80 %. The adoptions of the quantum cascade laser and two-dimensional correlation spectroscopy datamining technique raises this to 96.3 %. Finally, a novel machine learning method, which comprises three different decision-tree tools to generate trial screening decisions and a "majority-voting" datamining tool to reach a final screening decision, yields the best accuracy of 98.8 % ever reported in the literature.

Salivary spectral signature using ATR-FTIR spectroscopy in different exercise protocols

The Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) has been applied to determine salivary biomarkers with high sensitivity and cost-effectiveness. Our study aimed to test the hypothesis that the spectral profile of saliva demonstrates distinct vibrational modes corresponding to different exercise protocols, thereby facilitating exercise monitoring. Saliva samples were collected from trained male subjects at three intervals: pre-exercise, post-exercise, and 3 h post-exercise. The protocols included acute sessions of continuous exercise (CE), high-intensity interval exercise (HIIE), and resistance exercise (RE). ATR-FTIR analysis revealed that salivary biochemical components changed uniquely with each exercise protocol. Specific spectral vibrational modes were identified as potential biomarkers for each exercise type. Notably, the salivary spectrum pattern of CE closely resembled that of HIIE, whereas RE showed minor alterations. Furthermore, we attempted to apply an algorithm capable of distinguishing the spectral range that differentiates the exercise modalities. This pioneering study is the first to compare changes in saliva spectra following different exercise protocols and to suggest spectrum peaks of vibrational modes as markers for specific types of exercises. We emphasize that the spectral wavenumbers identified by FTIR could serve as practical markers in distinguishing between different exercise modalities, with sensitivity, specificity, and accuracy correlating with the metabolic changes induced by exercise. Therefore, this study contributes a panel of ATR-FTIR spectral wavenumbers that can be referenced as a spectral signature capable of distinguishing between resistance and endurance exercises.

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses

Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surgery or not, because in some patients, carotid artery stenosis, for example at the level of 70 %, does not cause ischemic stroke in others yes. Therefore, there is a need for new methods that will clearly indicate the marker qualifying the patient for surgery. In this article we used Fourier Transform InfraRed Attenuated Total Reflectance (FTIR-ATR) spectra of serum collected from healthy and patients suffering from ACS, which had surgery were analyzed by machine learning and Principal Component Analysis (PCA) to determine chemical differences and spectroscopy marker of ACS. PCA demonstrated clearly differentiation between serum collected from healthy and non-healthy patients. Obtained results showed that in serum collected from ACS patients, higher absorbances of PO2- stretching symmetric, CH2 and CH3 symmetric and asymmetric and amide I vibrations were noticed than in control group. Moreover, lack of peak at 1106 cm-1 was observed in spectrum of serum from non-control group. As a result of spectral shifts analysis was found that the most important role in distinguishing between healthy and unhealthy patients is played by FTIR ranges caused by vibrations of PO2- phospholipids, amides III, II and CO lipid vibrations. Continuing, peaks at 1636 cm-1 and 2963 cm-1 were proposed as a potential spectroscopy markers of ACS. Finally, accuracy of obtained results higher than 90 % suggested, that FTIR-ATR can be used as an additional diagnostic tool in ACS qualifying for surgery.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods

BACKGROUND & AIM

Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples.

METHODS & MATERIALS

Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy.

RESULTS

The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively.

CONCLUSION

The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning

Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recognition of different breast tissues. In parallel, the one-dimensional convolutional neural network (1D-CNN) stands out in the field of deep learning for its ability to efficiently process sequential data, such as spectroscopic signals. In this study, MIR spectra of breast tissue were collected in situ by coupling the self-developed MIR hollow optical fiber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrared spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the changes in macromolecular content and structure in breast cancer tissues. For the first time, a trinary classification model was established based on 1D-CNN for recognizing normal, paracancerous and cancerous tissues. The final predication results reveal that the 1D-CNN model based on baseline correction (BC) and data augmentation yields more precise classification results, with a total accuracy of 95.09%, exhibiting superior discrimination ability than machine learning models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67.78%) and PCA-KNN (70.00%). The experimental results suggest that the application of 1D-CNN enables accurate classification and recognition of different breast tissues, which can be considered as a precise, efficient and intelligent novel method for breast cancer diagnosis.

Oral squamous cell carcinoma identification by FTIR spectroscopy of oral biofluids

OBJECTIVES

This case study evaluated the efficacy of mid-infrared spectroscopy on the identification of oral squamous cell carcinoma, following the assessment of unstimulated whole saliva.

STUDY DESIGN AND METHODS

The trial follows a matched case-control design. Saliva samples were characterized through mid-infrared spectroscopy, and chemometric tools were applied to distinguish between case and control participants, further identifying the spectral regions that played a pivotal role in the successful identification of oral squamous cell carcinoma.

RESULTS

Mid-infrared spectroscopy was capable to discriminate between cancer patients and matched controls with 100% of correct predictions. Additionally, the spectral regions mostly contributing to the successful prediction were identified and found to be potentially associated with significant molecular changes crucial to the carcinogenic process.

CONCLUSION

The application of mid-infrared spectroscopy in saliva analysis may be regarded as an innovative, noninvasive, low cost, and sensitive technique contributing to the identification of oral squamous cell carcionma.

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

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

IR-EcoSpectra: Exploring sustainable ex situ and in situ FTIR applications for green chemical and pharmaceutical analysis

In various industries, particularly in the chemical and pharmaceutical fields, Fourier transform infrared spectroscopy (FTIR) spectroscopy provides a unique capacity to detect and characterise complex chemicals while minimising environmental damage by minimal waste generation and reducing the need for extensive sample preparation or use of harmful reagents. This review showcases the versatility of ex situ and in situ FTIR applications for substance identification, analysis, and dynamic monitoring. Ex situ FTIR spectroscopy's accuracy in identifying impurities, monitoring crystallisation processes, and regulating medication release patterns improves product quality, safety, and efficacy. Furthermore, its quantification capabilities enable more effective drug development, dosage procedures, and quality control practices, all of which are consistent with green analytical principles. On the other hand, in situ FTIR spectroscopy appears to be a novel tool for the real-time investigation of molecular changes during reactions and processes, allowing for the monitoring of drug release kinetics, crystallisation dynamics, and surface contacts, as well as providing vital insights into material behaviour. The combination of ex situ FTIR precision and in situ FTIR dynamic capabilities gives a comprehensive analytical framework for developing green practices, quality control, and innovation in the chemical and pharmaceutical industries. This review presents the wide range of applications of ex situ and in situ FTIR spectroscopy in chemical, pharmaceutical and medical fields as an analytical green chemistry tool. However, further study is required to fully realise FTIR's potential and develop new applications that improve sustainability in these areas.

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm-1, 1013 cm-1, 1002 cm-1, 1004 cm-1, and 1011 cm-1 in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool.

Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung cancer.

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis

BACKGROUND

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples.

METHODS

This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review.

RESULTS

Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples.

CONCLUSION

This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

Understanding the transition to viable but non-culturable state in Escherichia coli W3110: a comprehensive analysis of potential spectrochemical biomarkers

The viable but non-culturable (VBNC) state is considered a survival strategy employed by bacteria to endure stressful conditions, allowing them to stay alive. Bacteria in this state remain unnoticed in live cell counts as they cannot proliferate in standard culture media. VBNC cells pose a significant health risk because they retain their virulence and can revive when conditions normalize. Hence, it is crucial to develop fast, reliable, and cost-effective methods to detect bacteria in the VBNC state, particularly in the context of public health, food safety, and microbial control assessments. This research examined the biomolecular changes in Escherichia coli W3110 induced into the VBNC state in artificial seawater under three different stress conditions (temperature, metal, and antibiotic). Initially, confirmation of VBNC cells under various stresses was done using fluorescence microscopy and plate counts. Subsequently, lipid peroxidation was assessed through the TBARS assay, revealing a notable increase in peroxidation end-products in VBNC cells compared to controls. ATR-FTIR spectroscopy and chemomometrics were employed to analyze biomolecular changes, uncovering significant spectral differences in RNA, protein, and nucleic acid concentrations in VBNC cells compared to controls. Notably, RNA levels increased, while protein and nucleic acid amounts decreased. ROC analyses identified the 995 cm- 1 RNA band as a consistent marker across all studied stress conditions, suggesting its potential as a robust biomarker for detecting cells induced into the VBNC state under various stressors.

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

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

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

A disposable optofluidic micro-transmission cell with tailorable length for Fourier-transform infrared spectroscopy of biological fluids

Mid-infrared Fourier-transform infrared (FT-IR) spectroscopy of liquid biological samples is limited by the high absorption of water in this spectral range, which makes conventional transmission cuvettes unsuitable as their centimeter-scale length is already too big. The most common alternative relies on the use of attenuated total reflection (ATR) accessories, which provide a small interaction path length for light along the interface between the analyte and the expensive ATR crystals. In this work, we address this issue by proposing a disposable and low-cost micro-transmission cell. Its construction relies on a simple technique, which consists of dispersing plastic spherical microparticles in a liquid sample before dispensing it between two pieces of silicon assembled one onto the other and acting as windows for the cell. Consequently, the microparticles act as a spacer of very precise height in-between the two silicon windows. This technique allows easy construction of infrared absorption cells with near-optimum optical interaction path length just by selecting the most appropriate particle size. The concept is demonstrated by measuring the concentration of glucose in aqueous solutions using microspheres of diameter 20 μm then 40 μm and analyzing the corresponding glucose absorption peaks in the wavenumber range 950-1200 cm-1. The performance is compared to that of standard ATR spectroscopy of the same samples. This resulted in a root-mean-square error of cross-validation (RMSECV) of 58.8 mg dl-1 as obtained for transmission measurements by partial least squares (PLS) regression, which is comparable to the RMSECV of 53 mg dl-1 for single-reflection diamond ATR measurements.

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

Introduction

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

Materials and methods

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

Results

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

Conclusion

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

Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

Effects of solvent extracted organic matter from outdoor air pollution on human type II pneumocytes: Molecular and proteomic analysis

Outdoor air pollution is responsible for the exacerbation of respiratory diseases in humans. Particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) is one of the main components of outdoor air pollution, and solvent extracted organic matter (SEOM) is adsorbed to the main PM2.5 core. Some of the biological effects of black carbon and polycyclic aromatic hydrocarbons, which are components of PM2.5, are known, but the response of respiratory cell lineages to SEOM exposure has not been described until now. The aim of this study was to obtain SEOM from PM2.5 and analyze the molecular and proteomic effects on human type II pneumocytes. PM2.5 was collected from Mexico City in the wildfire season and the SEOM was characterized to be exposed on human type II pneumocytes. The effects were compared with benzo [a] pyrene (B[a]P) and hydrogen peroxide (H2O2). The results showed that SEOM induced a decrease in surfactant and deregulation in the molecular protein and lipid pattern analyzed by reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on human type II pneumocytes after 24 h. The molecular alterations induced by SEOM were not shared by those induced by B[a]P nor H2O2, which highlights specific SEOM effects. In addition, proteomic patterns by quantitative MS analysis revealed a downregulation of 171 proteins and upregulation of 134 proteins analyzed in the STRING database. The deregulation was associated with positive regulation of apoptotic clearance, removal of superoxide radicals, and positive regulation of heterotypic cell-cell adhesion processes, while ATP metabolism, nucleotide process, and cellular metabolism were also affected. Through this study, we conclude that SEOM extracted from PM2.5 exerts alterations in molecular patterns of protein and lipids, surfactant expression, and deregulation of metabolic pathways of type II pneumocytes after 24 h of exposure in absence of cytotoxicity, which warns about apparent SEOM silent effects.

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Biomolecular alterations detected in multiple sclerosis skin fibroblasts using Fourier transform infrared spectroscopy

Multiple sclerosis (MS) is the leading cause of non-traumatic disability in young adults. New avenues are needed to help predict individuals at risk for developing MS and aid in diagnosis, prognosis, and outcome of therapeutic treatments. Previously, we showed that skin fibroblasts derived from patients with MS have altered signatures of cell stress and bioenergetics, which likely reflects changes in their protein, lipid, and biochemical profiles. Here, we used Fourier transform infrared (FTIR) spectroscopy to determine if the biochemical landscape of MS skin fibroblasts were altered when compared to age- and sex-matched controls (CTRL). More so, we sought to determine if FTIR spectroscopic signatures detected in MS skin fibroblasts are disease specific by comparing them to amyotrophic lateral sclerosis (ALS) skin fibroblasts. Spectral profiling of skin fibroblasts from MS individuals suggests significant alterations in lipid and protein organization and homeostasis, which may be affecting metabolic processes, cellular organization, and oxidation status. Sparse partial least squares-discriminant analysis of spectral profiles show that CTRL skin fibroblasts segregate well from diseased cells and that changes in MS and ALS may be unique. Differential changes in the spectral profile of CTRL, MS, and ALS cells support the development of FTIR spectroscopy to detect biomolecular modifications in patient-derived skin fibroblasts, which may eventually help establish novel peripheral biomarkers.

Unlocking the Diagnostic Potential of Saliva: A Comprehensive Review of Infrared Spectroscopy and Its Applications in Salivary Analysis

Infrared (IR) spectroscopy is a noninvasive and rapid analytical technique that provides information on the chemical composition, structure, and conformation of biomolecules in saliva. This technique has been widely used to analyze salivary biomolecules, owing to its label-free advantages. Saliva contains a complex mixture of biomolecules including water, electrolytes, lipids, carbohydrates, proteins, and nucleic acids which are potential biomarkers for several diseases. IR spectroscopy has shown great promise for the diagnosis and monitoring of diseases such as dental caries, periodontitis, infectious diseases, cancer, diabetes mellitus, and chronic kidney disease, as well as for drug monitoring. Recent advancements in IR spectroscopy, such as Fourier-transform infrared (FTIR) spectroscopy and attenuated total reflectance (ATR) spectroscopy, have further enhanced its utility in salivary analysis. FTIR spectroscopy enables the collection of a complete IR spectrum of the sample, whereas ATR spectroscopy enables the analysis of samples in their native form, without the need for sample preparation. With the development of standardized protocols for sample collection and analysis and further advancements in IR spectroscopy, the potential for salivary diagnostics using IR spectroscopy is vast.

Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study

Zika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student's t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm^-1 as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.

Salivary ATR-FTIR Spectroscopy Coupled with Support Vector Machine Classification for Screening of Type 2 Diabetes Mellitus

The blood diagnosis of diabetes mellitus (DM) is highly accurate; however, it is an invasive, high-cost, and painful procedure. In this context, the combination of ATR-FTIR spectroscopy and machine learning techniques in other biological samples has been used as an alternative tool to develop a non-invasive, fast, inexpensive, and label-free diagnostic or screening platform for several diseases, including DM. In this study, we used the ATR-FTIR tool associated with linear discriminant analysis (LDA) and a support vector machine (SVM) classifier in order to identify changes in salivary components to be used as alternative biomarkers for the diagnosis of type 2 DM. The band area values of 2962 cm^-1, 1641 cm^-1, and 1073 cm^-1 were higher in type 2 diabetic patients than in non-diabetic subjects. The best classification of salivary infrared spectra was by SVM, showing a sensitivity of 93.3% (42/45), specificity of 74% (17/23), and accuracy of 87% between non-diabetic subjects and uncontrolled type 2 DM patients. The SHAP features of infrared spectra indicate the main salivary vibrational modes of lipids and proteins that are responsible for discriminating DM patients. In summary, these data highlight the potential of ATR-FTIR platforms coupled with machine learning as a reagent-free, non-invasive, and highly sensitive tool for screening and monitoring diabetic patients.

Potential Role of Fourier Transform Infrared Spectroscopy as a Screening Approach for Breast Cancer

Breast cancer is a heterogeneous disease, and its spread involves a succession of clinical and pathological stages. Screening is predominantly based on mammography, which has critical limitations related to the effectiveness and production of false-positive or false-negative results, generating discomfort and low adherence. In this context, infrared with attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy emerges as a non-destructive sample tool, which is non-invasive, label-free, has a low operating-cost, and requires only a small amount of sample, including liquid plasma samples. We sought to evaluate the clinical applicability of ATR FT-IR in breast cancer screening. ATR FT-IR spectroscopy through its highest potential spectral biomarker could distinguish, by liquid plasma biopsy, breast cancer patients and healthy controls, obtaining a sensitivity of 97%, specificity of 93%, a receiver operating characteristic ROC curve of 97%, and a prediction accuracy of 94%. The main variance between the groups was mainly in the band 1511 cm^-1 of the control group, 1502 and 1515 cm^-1 of the cancer group, which are the peaks of the bands referring to proteins and amide II. ATR FT-IR spectroscopy has demonstrated to be a promising tool for breast cancer screening, given its time efficiency, cost of approach, and its high ability to distinguish between the liquid plasma samples of breast cancer patients and healthy controls.

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.

Analysis of urine using electronic tongue towards non-invasive cancer diagnosis

Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.

Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900-1425 cm^-1, 1475-1710 cm^-1, 2800-3000 cm^-1, and 3090-3700 cm^-1, to be modeled and detected respectively. The final results showed that the ranges 900-1425 cm^-1 and 1475-1710 cm^-1 had superior classification accuracies. The region 900-1425 cm^-1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475-1710 cm^-1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.

A Lower Serum Antioxidant Capacity as a Distinctive Feature for Women with HER2+ Breast Cancer: A Preliminary Study

The overexpression of HER2 in breast cancer (BC) can contribute to redox imbalance, which is related to damage and structural modification in many biomolecules. To the best of our knowledge, this is the first study that has investigated the infrared spectrum wavenumbers obtained by ATR-FTIR and their relationship with the levels of redox status markers such as reduced glutathione, superoxide dismutase (SOD), catalase, Ferric Reducing Antioxidant Power (FRAP), and protein carbonyl among women with HER2+ BC, HER2- BC, and benign breast disease (BBD). The study was conducted with 25 women, 17 of whom were diagnosed with BC (6 HER2+ and 11 HER2-) and 8 with BBD. Our results indicate HER2+ BC cases could be distinguished from HER2- BC and BBD cases by their serum's antioxidant capacity [HER2+ BC vs. HER2- BC (AUC = 0.818; specificity = 81.82%; sensitivity = 66.67%); HER2+ BC vs. BBD (AUC = 0.875; specificity = 75%; sensitivity = 83.33%)]. The changes in biochemical terms that occur in serum as a result of the scarcity of antioxidants are related to a peculiar fingerprint in the infrared spectrum obtained by ATR-FTIR. In the serum of women with BBD, the SOD enzyme level is the highest, and this characteristic allowed us to distinguish them from HER2- BC. Finally, data regarding the serological antioxidant capacity of FRAP and the infrared spectrum by ATR-FTIR will allow us to assess biochemical changes that occur before clinical signs, indicating whether changes in therapy or interventions are necessary.

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples

The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues (n = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, and none was misclassified. Moreover, it was able to correctly identify the histologically confirmed benign samples (n = 83) at 51.5-99.7% confidence (average = 91.64%). Of the 83 samples, 60 (72.29%) were classified with an above average confidence score, 22 (26.51%) classified below the average confidence score, and only 1 sample (1.20%) was misclassified. The study provides additional proof of the ability of attenuated total reflection (ATR) FT-IR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.

Comparison of the Immune Response in Vaccinated People Positive and Negative to SARS-CoV-2 Employing FTIR Spectroscopy

Various immunopathological events characterize the systemic acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Moreover, it has been reported that coronavirus disease 2019 (COVID-19) vaccination and infection by SARS-CoV-2 induce humoral immunity mediated by B-cell-derived antibodies and cellular immunity mediated by T cells and memory B cells. Immunoglobulins, cytokines, and chemokines play an important role in shaping immunity in response to infection and vaccination. Furthermore, different vaccines have been developed to prevent COVID-19. Therefore, this research aimed to analyze and compare Fourier-transform infrared (FTIR) spectra of vaccinated people with a positive (V-COVID-19 group) or negative (V-Healthy group) real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) test, evaluating the immunoglobulin and cytokine content as an immunological response through FTIR spectroscopy. Most individuals that integrated the V-Healthy group (88.1%) were asymptomatic; on the contrary, only 28% of the V-COVID-19 group was asymptomatic. Likewise, 68% of the V-COVID-19 group had at least one coexisting illness. Regarding the immunological response analyzed through FTIR spectroscopy, the V-COVID-19 group showed a greater immunoglobulins G, A, and M (IgG, IgA, and IgM) content, as well as the analyzed cytokines interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-ɑ), and interleukins 1β, 6, and 10 (IL-1β, IL-6, and IL-10). Therefore, we can state that it was possible to detect biochemical changes through FTIR spectroscopy associated with COVID-19 immune response in vaccinated people.

Diagnostic performance of attenuated total reflection Fourier-transform infrared spectroscopy for detecting COVID-19 from routine nasopharyngeal swab samples

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be detected from nasopharyngeal swab samples originally collected for polymerase chain reaction (PCR) analysis. Our retrospective study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when PCR analysis was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67-0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62-0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P < 0.001 for AUROC in label permutation test). However, the diagnostic performance of ATR-FTIR remained only moderate, potentially due to low concentration of viral particles in the transport medium. Further studies are needed before ATR-FTIR can be recommended for fast screening of SARS-CoV-2 from nasopharyngeal swab samples.

Application of FTIR Spectroscopy to Detect Changes in Skeletal Muscle Composition Due to Obesity with Insulin Resistance and STZ-Induced Diabetes

Age, obesity, and diabetes mellitus are pathophysiologically interconnected factors that significantly contribute to the global burden of non-communicable diseases. These metabolic conditions are associated with impaired insulin function, which disrupts the metabolism of carbohydrates, lipids, and proteins and can lead to structural and functional changes in skeletal muscle. Therefore, the alterations in the macromolecular composition of skeletal muscle may provide an indication of the underlying mechanisms of insulin-related disorders. The aim of this study was to investigate the potential of Fourier transform infrared (FTIR) spectroscopy to reveal the changes in macromolecular composition in weight-bearing and non-weight-bearing muscles of old, obese, insulin-resistant, and young streptozotocin (STZ)-induced diabetic mice. The efficiency of FTIR spectroscopy was evaluated by comparison with the results of gold-standard histochemical techniques. The differences in biomolecular phenotypes and the alterations in muscle composition in relation to their functional properties observed from FTIR spectra suggest that FTIR spectroscopy can detect most of the changes observed in muscle tissue by histochemical analyses and more. Therefore, it could be used as an effective alternative because it allows for the complete characterization of macromolecular composition in a single, relatively simple experiment, avoiding some obvious drawbacks of histochemical methods.

One-Drop Serum Screening Test for Anal Cancer in Men via Infrared Attenuated Total Reflection Spectroscopy

Rare cancers are a challenge for clinical practice, the treatment experience at major centers to which rare cancers are referred is limited and are the most difficult to diagnose. Research to identify causes or develop prevention and early detection strategies is extremely challenging. Anal cancer is an example of a rare cancer, with the human papillomavirus (HPV) infection being the most important risk factor associated. In the early stages, anal cancer does not exhibit evident symptoms. This disease is diagnosed by means of anoscopy, which diagnoses some cases of early cancer; nevertheless, sensitivity of this test ranges between 47 and 89%. Therefore, the development of new, effective, and evidence-based screening methodologies for the early detection of rare cancers is of great relevance. In this study, the potential of ATR-FTIR spectroscopy has been explored as a sensitive, nondestructive, and inexpensive analytical method for developing disease screening platforms in serum. Spectral differences were found in the regions of 1700-1100 and 1700-1400 cm^-1 between the control group and the anal cancer group related to the presence of proteins and nucleic acids. The chemometric analysis presented differences in the spectral fingerprints for both spectral regions with a high sensitivity ranging from 95.2 to 99.9% and a specificity ranging from 99.2 to 100%. This is the first step that we report for a methodology that is fast, nondestructive, and easy to perform, and the high sensitivity and specificity of the method are the basis for extensive research studies to implement these technologies in the clinical field.

Saliva may predict quality of life in psoriasis as measured by Fourier transform infrared spectroscopy (FTIR) and chemometrics

BACKGROUND

Psoriasis is a chronic skin disease, with several comorbidities, such as psoriatic arthritis, inflammatory bowel disease, metabolic syndrome, and impaired quality of life and work activity. The Dermatology Life Quality Index (DLQI) is the most commonly used quality of life index in psoriatic patients, as it is a marker of severe disease. This study evaluated the association between salivary Fourier transform Infrared Spectroscopy (FTIR) metabolic fingerprints and severity of psoriasis as measured by DLQI, using chemometric algorithms.

MATERIALS AND METHODS

Saliva was collected from 56 (27 with DLQI ≤ 10 [GI]; 29 with DLQI > 10 [GII]) psoriatic patients diagnosed and assessed by DLQI for disease severity by a dermatologist and analyzed by the transflectance technique in mid-infrared. Hierarchic cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and orthogonal partial least squares (OPLS) algorithms were used to associate salivary FTIR spectra with the respective DLQI scores.

RESULTS

Second derivative (2D) discriminated GI and GII at 2522 cm^-1 (p < 0.0001). HCA and PCA partially discriminated GI from GII at 4000-450 cm^-1 (p = 0.042 and 0.00821, respectively). Data processing with 1^st derivative (1D), 3 latent variables (LV) and 1 orthogonal signal correction (OSC) component at 2550-1801 cm^-1 generated an FTIR/OPLS-DA model with 100% accuracy to classify the severity of psoriasis, and an FTIR/OPLS model to quantify DLQI (range 0-28) with high performance: root mean square error of prediction (RMSEP) < 0.01 and coefficient of determination (R²) > 0.9999.

CONCLUSIONS

Salivary FTIR combined with chemometric algorithms such as OPLS-DA and OPLS can be used as a clinical tool to classify or predict the severity of psoriasis according to DLQI in patients with confirmed psoriasis.

Salivary biomarkers in breast cancer diagnosis: A systematic review and diagnostic meta-analysis

BACKGROUND

Salivary diagnostics and their utility as a nonaggressive approach for breast cancer diagnosis have been extensively studied in recent years. This meta-analysis assesses the diagnostic value of salivary biomarkers in differentiating between patients with breast cancer and controls.

METHODS

We conducted a meta-analysis and systematic review of studies related to salivary diagnostics published in PubMed, EMBASE, Scopus, Ovid, Science Direct, Web of Science (WOS), and Google Scholar. The articles were chosen utilizing inclusion and exclusion criteria, as well as assessing their quality. Specificity and sensitivity, along with negative and positive likelihood ratios (NLR and PLR) and diagnostic odds ratio (DOR), were calculated based on random- or fixed-effects model. Area under the curve (AUC) and summary receiver-operating characteristic (SROC) were plotted and evaluated, and Fagan's Nomogram was evaluated for clinical utility.

RESULTS

Our systematic review and meta-analysis included 14 papers containing 121 study units with 8639 adult subjects (4149 breast cancer patients and 4490 controls without cancer). The pooled specificity and sensitivity were 0.727 (95% CI: 0.713-0.740) and 0.717 (95% CI: 0.703-0.730), respectively. The pooled NLR and PLR were 0.396 (95% CI: 0.364-0.432) and 2.597 (95% CI: 2.389-2.824), respectively. The pooled DOR was 7.837 (95% CI: 6.624-9.277), with the AUC equal to 0.801. The Fagan's nomogram showed post-test probabilities of 28% and 72% for negative and positive outcomes, respectively. We also conducted subgroup analyses to determine specificity, sensitivity, DOR, PLR, and NLR based on the mean age of patients (≤52 or >52 years old), saliva type (stimulated and unstimulated saliva), biomarker measurement method (mass spectrometry [MS] and non-MS measurement methods), sample size (≤55 or >55), biomarker type (proteomics, metabolomics, transcriptomics and proteomics, and reagent-free biophotonic), and nations.

CONCLUSION

Saliva, as a noninvasive biomarker, has the potential to accurately differentiate breast cancer patients from healthy controls.

Design of High-Sensitivity Surface Plasmon Resonance Sensor Based on Nanostructured Thin Films for Effective Detection of DNA Hybridization

As developed countries' ability to control infectious diseases increases, it has become clear that genetic diseases are a major cause of disability, death, and human tragedy. Coronavirus has recently spread throughout the world, and the capacity to detect low concentrations and virus changes can help to prevent the sickness from spreading further. In this paper, a surface plasmon resonance sensor based on nanostructured thin films and graphene as a 2D material has been designed with high sensitivity and accuracy to identify DNA-based infectious diseases such as SARS-CoV-2. The transfer matrix method assesses the effects of different structural factors, including nanolayer thickness on the sensor's performance. The results demonstrated that the sensor with the Kretschmann configuration has ultra-high sensitivity (192.19 deg/RIU) and a high figure of merit (634.68 RIU^-1).

Oral lichen planus identification by mid-infrared spectroscopy of oral biofluids: A case-control study

BACKGROUND AND AIMS

This study aims to access the effectiveness of mid-infrared (MIR) spectroscopy on the identification of the reticular form of OLP, following the assessment of gingival crevicular fluid (GCF) and oral mucosa transudate (OMT).

MATERIAL AND METHODS

The trial follows a case-control design. Samples were characterized through MIR spectroscopy and chemometric tools were applied to distinguish between case and control participants, further identifying the spectral regions with the highest contribution to the developed models.

RESULTS

MIR spectroscopy was capable to discriminate between OLP patients and controls with 95.1% and 85.4% of correct predictions, regarding GCF and OMT samples, respectively. Additionally, the spectral regions mostly contributing to the successful prediction were identified, and possibly related with the distinctive presence of amino acids/proteins and oxidative stress mediators in oral biofluids, supporting the role of the immune-inflammatory activation on OLP etiology and disease course.

CONCLUSION

MIR spectroscopy analysis of GCF and OMT may be regarded as an innovative, non-invasive, low cost and sensitive technique, contributing to the identification of the reticular from of OLP.

Fourier Transform Infrared (FTIR) Spectroscopy to Analyse Human Blood over the Last 20 Years: A Review towards Lab-on-a-Chip Devices

Since microorganisms are evolving rapidly, there is a growing need for a new, fast, and precise technique to analyse blood samples and distinguish healthy from pathological samples. Fourier Transform Infrared (FTIR) spectroscopy can provide information related to the biochemical composition and how it changes when a pathological state arises. FTIR spectroscopy has undergone rapid development over the last decades with a promise of easier, faster, and more impartial diagnoses within the biomedical field. However, thus far only a limited number of studies have addressed the use of FTIR spectroscopy in this field. This paper describes the main concepts related to FTIR and presents the latest research focusing on FTIR spectroscopy technology and its integration in lab-on-a-chip devices and their applications in the biological field. This review presents the potential use of FTIR to distinguish between healthy and pathological samples, with examples of early cancer detection, human immunodeficiency virus (HIV) detection, and routine blood analysis, among others. Finally, the study also reflects on the features of FTIR technology that can be applied in a lab-on-a-chip format and further developed for small healthcare devices that can be used for point-of-care monitoring purposes. To the best of the authors’ knowledge, no other published study has reviewed these topics. Therefore, this analysis and its results will fill this research gap.

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics-area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)-were averaged for comparison. The NN models were compared to six (6) machine learning models-logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)-for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C-OH C-OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample's spectrum using NN.

Breast cancer or surrounding normal tissue? A successful discrimination by FTIR or Raman microspectroscopy

Breast cancer is a type of cancer with the highest incidence worldwide in 2021, with early diagnosis and rapid treatment intervention being the reasons for the decreasing mortality rate associated with the disease.

Two New Pentacyclic Triterpenoids, an Alkaloid and a Long-chain Fatty Acid from Albizia Coriaria (Welw ex. Oliver)

Herein, we report the occurrence of four new compounds in ethanolic extract of Albizia coriaria Welw ex. Oliver leaves along with other compounds previously reported in this species. The compounds were isolated and characterized using column chromatography, Fourier Transform Infrared (FTIR) and gas chromatography/mass spectrometry. FTIR spectrum of the extract showed phenolic OH stretching (3362.30 cm-1), C=O (1660.08 cm-1), CO stretching (1369.46 cm-1 and 1319.00 cm-1) and CN stretch (1072.44 cm-1) which confirmed the presence of alcohols, carboxylic acids and nitrogen-containing compounds. Oleanolic acid (1), oleanolic acid acetate (2), pterin-6-carboxylic acid (3), undecanol (4), betulinic acid (5), betulin (6) and benzyl alcohol (7) were tentatively identified in the extract. Compounds 1-4 are being reported for the first time in Albizia coriaria.

A New Look into Cancer-A Review on the Contribution of Vibrational Spectroscopy on Early Diagnosis and Surgery Guidance

Simple Summary

Cancer is a leading cause of death worldwide, with the detection of the disease in its early stages, as well as a correct assessment of the tumour margins, being paramount for a successful recovery. While breast cancer is one of most common types of cancer, head and neck cancer is one of the types of cancer with a lower prognosis and poor aesthetic results. Vibrational spectroscopy detects molecular vibrations, being sensitive to different sample compositions, even when the difference was slight. The use of spectroscopy in biomedicine has been extensively explored, since it allows a broader assessment of the biochemical fingerprint of several diseases. This literature review covers the most recent advances in breast and head and neck cancer early diagnosis and intraoperative margin assessment, through Raman and Fourier transform infrared spectroscopies. The rising field of spectral histopathology was also approached. The authors aimed at expounding in a more concise and simple way the challenges faced by clinicians and how vibrational spectroscopy has evolved to respond to those needs for the two types of cancer with the highest potential for improvement regarding an early diagnosis, surgical margin assessment and histopathology.

Abstract

In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients’ prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment.

ATR-FTIR spectrum analysis of saliva samples from COVID-19 positive patients

The coronavirus disease 2019 (COVID-19) is the latest biological hazard for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even though numerous diagnostic tests for SARS-CoV-2 have been proposed, new diagnosis strategies are being developed, looking for less expensive methods to be used as screening. This study aimed to establish salivary vibrational modes analyzed by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to detect COVID-19 biological fingerprints that allow the discrimination between COVID-19 and healthy patients. Clinical dates, laboratories, and saliva samples of COVID-19 patients (N = 255) and healthy persons (N = 1209) were obtained and analyzed through ATR-FTIR spectroscopy. Then, a multivariate linear regression model (MLRM) was developed. The COVID-19 patients showed low SaO2, cough, dyspnea, headache, and fever principally. C-reactive protein, lactate dehydrogenase, fibrinogen, D-dimer, and ferritin were the most important altered laboratory blood tests, which were increased. In addition, changes in amide I and immunoglobulin regions were evidenced in the FTIR spectra analysis, and the MLRM showed clear discrimination between both groups. Specific salivary vibrational modes employing ATR-FTIR spectroscopy were established; moreover, the COVID-19 biological fingerprint in saliva was characterized, allowing the COVID-19 detection using an MLRM, which could be helpful for the development of new diagnostic devices.

ATR-FTIR spectroscopy probing of structural alterations in the cellular membrane of abscopal liver cells

In this study, we utilize ATR-FTIR spectroscopy to investigate the structural damages in the cell membrane lipids and proteins as a result of the oxidative stress in abscopal liver tissue of rats either whole-body, cranially or lower limb irradiated as compared with sham-irradiated group. We also question whether the original irradiation region would influence the induction of the abscopal effect. The data present compelling evidence that an abscopal effect was induced in the liver tissue following both cranial and lower limb irradiations, marked by damage in the membrane-associated lipids and proteins. Lipid damage manifestation is evident by; 1) decrease in the lipid/protein ratio. 2) Degradation of lipid as marked by the decrease in the area ratio CH ₂ asymmetric/CH ₃ asymmetric stretching bands. 3) Increase in the carbonyl content evident by the increase in the band area ratio of carbonyl ester/lipid. 4) Increase in the degree of methylation as indicated by the increase in the band area ratio of CH₃/lipid. 5) Disorder in the phospholipid acyl chains marked by the shift in the CH₂ asymmetric stretching and olefinic HCCH absorption bands. Protein damage was indicated by 1) Shifts in the position of amide I and amide II bands. 2) Decrease in the area ratio amide I/amide II. 3) Broadening in amide II band. Our data strongly suggest similar induction of the abscopal effect as a result of either cranial or lower limb irradiation, which means that the original irradiation region did not influence the induced abscopal effect in the examined system.

Analysis of Saliva Lipids in Breast and Prostate Cancer by IR Spectroscopy

We have developed a method for studying the lipid profile of saliva, combining preliminary extraction and IR spectroscopic detection. The case-control study involved patients with a histologically verified diagnosis of breast and prostate cancer and healthy volunteers. The comparison group included patients with non-malignant pathologies of the breast (fibroadenomas) and prostate gland (prostatic intraepithelial neoplasia). Saliva was used as a material for biochemical studies. It has been shown that the lipid profile of saliva depends on gender, and for males it also depends on the age group. In cancer pathologies, the lipid profile changes significantly and also depends on gender and age characteristics. The ratio of 1458/1396 cm-1 for both breast and prostate cancer has a potential diagnostic value. In both cases, this ratio decreases compared to healthy controls. For prostate cancer, the ratio of 2923/2957 cm-1 is also potentially informative, which grows against the background of prostate pathologies. It is noted that, in all cases, changes in the proposed ratios are more pronounced in the early stages of diseases, which increases the relevance of their study in biomedical applications.

Saliva based non invasive screening of Oral Submucous Fibrosis using ATR-FTIR spectroscopy

Oral Submucous Fibrosis (OSMF) is a type of precancerous condition of Oral cancer and considered to have the greatest malignant potential. Biopsy is an ultimate option for the conformation of the malignancy. But the invasiveness of the procedure makes it interdict. Therefore, there is an urgent need to identify effective screening and diagnostic methods which would be less invasive, rapid, more accurate and cost effective. Here, we used Attenuated Total Reflection- Fourier transform infrared spectroscopy (ATR-FTIR) with Chemometric analysis coupled with estimation of total salivary protein to discriminate OSMF and Healthy Control (HC). The present study showed the specific Infrared spectrum for OSMF patients, which was specifically differentiated from HC based on the spectral shift of proteins/amide II, carbohydrate and nucleic acid using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) with small data sets. ATR-FTIR spectroscopy of saliva coupled with total protein estimation can be used to discriminate between OSMF and HC. However, large sample size should be needed to evaluate the ATR-FTIR for consideration as a screening tool for an early diagnosis OSMF.

SP, N. M, Barik AK, Pai KM, Unnikrishnan VK, George SD, Kartha VB, Chidangil S. Photonics of human saliva: potential optical methods for the screening of abnormal health conditions and infections

Human saliva can be treated as a pool of biological markers able to reflect on the state of personal health. Recent years have witnessed an increase in the use of optical devices for the analysis of body fluids. Several groups have carried out studies investigating the potential of saliva as a non-invasive and reliable clinical specimen for use in medical diagnostics. This brief review aims to highlight the optical technologies, mainly surface plasmon resonance (SPR), Raman, and Fourier transform infrared (FTIR) spectroscopy, which are being used for the probing of saliva for diverse biomedical applications. Advances in bio photonics offer the promise of unambiguous, objective and fast detection of abnormal health conditions and viral infections (such as COVID-19) from the analysis of saliva.

A Systematic Study of the Cryogenic Milling of Chrysotile Asbestos

For more than 40 years, intensive research has been devoted to shedding light on the mechanisms of asbestos toxicity. Given the key role of fibre length in the mechanisms of asbestos toxicity, much work has been devoted to finding suitable comminution routes to produce fibres in desired size intervals. A promising method is cryogenic milling that, unlike other mechanical size reduction techniques, preserves the crystal–chemical properties of materials. In this study, the effect of cryogenic milling on the physical–chemical properties of commercial Russian chrysotile was studied in order to produce precise size fractions with invariant properties compared to the pristine fibres. In particular, two batches with fibres > 5 µm and < 5 µm were prepared, as this limit sets their potential toxicity. The results are fundamental for future in vitro toxicity testing of this commercial product, widely used in chrysotile-friendly countries but not yet adequately studied. Results show that fibre length can be controlled by milling time under cryogenic conditions without inducing structural defects or amorphization; short fibres (95% L < 5 µm) can be obtained by cryogenic milling for 40 min, while 10 min is enough to yield long chrysotile fibres (90% L > 5 µm).