Bladder cancer diagnosis from bladder wash by Fourier transform infrared spectroscopy as a novel test for tumor recurrence

Identification of diagnostic biomarkers used in the diagnosis of cardiovascular diseases and diabetes mellitus: A systematic review of quantitative studies

AIMS

To perform a systematic review of studies that sought to identify diagnostic biomarkers for the diagnosis of cardiovascular diseases (CVDs) and diabetes mellitus (DM), which could be used in low- and middle-income countries (LMICs) where there is a lack of diagnostic equipment, treatments and training.

MATERIALS AND METHODS

Papers were sourced from six databases: the British Nursing Index, Google Scholar, PubMed, Sage, Science Direct and Scopus. Articles published between January 2002 and January 2023 were systematically reviewed by three reviewers and appropriate search terms and inclusion/exclusion criteria were applied.

RESULTS

A total of 18 studies were yielded, as well as 234 diagnostic biomarkers (74 for CVD and 160 for DM). Primary biomarkers for the diagnosis of CVDs included growth differentiation factor 15 and neurogenic locus notch homologue protein 1 (Notch1). For the diagnosis of DM, alpha-2-macroglobulin, C-peptides, isoleucine, glucose, tyrosine, linoleic acid and valine were frequently reported across the included studies. Advanced analytical techniques, such as liquid chromatography mass spectrometry, enzyme-linked immunosorbent assays and vibrational spectroscopy, were also repeatedly reported in the included studies and were utilized in combination with traditional and alternative matrices such as fingernails, hair and saliva.

CONCLUSIONS

While advanced analytical techniques are expensive, laboratories in LMICs should carry out a cost-benefit analysis of their use. Alternatively, laboratories may want to explore emerging techniques such as infrared, Fourier transform-infrared and near-infrared spectroscopy, which allow sensitive noninvasive analysis.

The investigation of the molecular changes during lipopolysaccharide-induced systemic inflammation on rat hippocampus by using FTIR spectroscopy

The aim of this study is to reveal the molecular changes accompanying the neuronal hyper-excitability during lipopolysaccharide (LPS)-induced systemic inflammation on rat hippocampus using Fourier transform infrared (FTIR) spectroscopy. For this aim, the body temperature of Wistar albino rats administered LPS or saline was recorded by radiotelemetry. The animals were decapitated when their body temperature began to decrease by 0.5°C after LPS treatment and the hippocampi of them were examined by FTIR spectroscopy. The results indicated that systemic inflammation caused lipid peroxidation, an increase in the amounts of lipids, proteins and nucleic acids, a decrease in membrane order, an increase in membrane dynamics and changes in the secondary structure of proteins. Principal component analysis successfully separated control and LPS-treated groups. In conclusion, significant structural, compositional and functional alterations occur in the hippocampus during systemic inflammation and these changes may have specific characteristics which can lead to neuronal hyper-excitability.

Near-infrared spectroscopy as a novel method of ex vivo bladder cancer tissue characterisation

OBJECTIVE

To evaluate near-infrared (NIR) spectroscopy in differentiating between benign and malignant bladder pathologies ex vivo immediately after resection, including the grade and stage of malignancy.

PATIENTS AND METHODS

A total of 355 spectra were measured on 71 bladder specimens from patients undergoing transurethral resection of bladder tumour (TURBT) between April and August 2022. Scan time was 5 s, undertaken using a portable NIR spectrometer within 10 min from excision. Specimens were then sent for routine histopathological correlation. Machine learning models were applied to the spectral dataset to construct diagnostic algorithms; these were then tested for their ability to predict the histological diagnosis of each sample using its NIR spectrum.

RESULTS

A two-group algorithm comparing low- vs high-grade urothelial cancer demonstrated 97% sensitivity, 99% specificity, and the area under the receiver operating characteristic curve (AUC) was 0.997. A three-group algorithm predicting stages Ta vs T1 vs T2 achieved 97% sensitivity, 92% specificity, and the AUC was 0.996.

CONCLUSIONS

This first study evaluating the diagnostic potential of NIR spectroscopy in urothelial cancer shows that it can be accurately used to assess tissue in an ex vivo setting immediately after TURBT. This offers point-of-care assessment of bladder pathology, with potential to influence the extent of resection, reducing both the need for re-resection where invasive disease may be suspected, and also the potential for complications where extent of diagnostic resection can be limited. Further studies utilising fibre-optic probes offer the potential for in vivo assessment.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning

Urothelial bladder carcinoma (BC) is primarily diagnosed with a subjective examination of biopsies by histopathologists, but accurate diagnosis remains time-consuming and of low diagnostic accuracy, especially for low grade non-invasive BC. We propose a novel approach for high-throughput BC evaluation by combining infrared (IR) microscopy of bladder sections with machine learning (partial least squares-discriminant analysis) to provide an automated prediction of the presence of cancer, invasiveness and grade. Cystoscopic biopsies from 50 patients with clinical suspicion of BC were histologically examined to assign grades and stages. Adjacent tissue cross-sections were IR imaged to provide hyperspectral datasets and cluster analysis segregated IR images to extract the average spectra of epithelial and subepithelial tissues. Discriminant models, which were validated using repeated random sampling double cross-validation, showed sensitivities (AUROC) ca. 85% (0.85) for the identification of cancer in epithelium and subepithelium. The diagnosis of non-invasive and invasive cases showed sensitivity values around 80% (0.84-0.85) and 76% (0.73-0.80), respectively, while the identification of low and high grade BC showed higher sensitivity values 87-88% (0.91-0.92). Finally, models for the discrimination between cancers with different invasiveness and grades showed more modest AUROC values (0.67-0.72). This proves the high potential of IR imaging in the development of ancillary platforms to screen bladder biopsies.

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.

Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology

Non-muscle Invasive Bladder Cancer (NMIBC) accounts for 80% of all bladder cancers. Although it is mostly low-grade tumors, its high recurrence rate necessitates three-times-monthly follow-ups and cystoscopy examinations to detect and prevent its progression. A rapid liquid biopsy-based assay is needed to improve detection and reduce complications from invasive cystoscopy. Here, we present a rapid spectroscopic method to detect the recurrence of NMIBC in urine. Urine samples from previously-diagnosed NMIBC patients (n = 62) were collected during their follow-up visits before cystoscopy examination. Cystoscopy results were recorded (41 cancer-free and 21 recurrence) and attenuated total refraction Fourier transform infrared (ATR-FTIR) spectra were acquired from urine samples using direct application. Spectral processing and normalization were optimized using parameter grid searching. We assessed their technical variability through multivariate analysis and principal component analysis (PCA). We assessed 35 machine learning models on a training set (70%), and the performance was evaluated on a held-out test set (30%). A Regularized Random Forests (RRF) model achieved a 0.92 area under the receiver operating characteristic (AUROC) with 86% sensitivity and 77% specificity. In conclusion, our spectroscopic liquid biopsy approach provides a promising technique for the early identification of NMIBC with a less invasive examination.

The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers-What Should a Urologist Really Know?

The aim of this review is to analyze and describe the current landscape of bladder cancer diagnostic and surveillance biomarkers. We researched the literature from 2016 to November 2021 to find the most promising new molecules and divided them into seven different subgroups based on their function and location in the cell. Although cystoscopy and cytology are still the gold standard for diagnosis and surveillance when it comes to bladder cancer (BCa), their cost is quite a burden for national health systems worldwide. Currently, the research is focused on finding a biomarker that has high negative predictive value (NPV) and can exclude with a certainty the presence of the tumor, considering missing it could be disastrous for the patient. Every subgroup has its own advantages and disadvantages; for example, protein biomarkers cost less than genomic ones, but on the other hand, they seem to be less precise. We tried to simplify this complicated topic as much as possible in order to make it comprehensible to doctors and urologists that are not as familiar with it, as well as encourage them to actively participate in ongoing research.

Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum

Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm^-1 and was assigned to the C-C, C-O, and C-N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

CoronaVac (Sinovac) COVID-19 vaccine-induced molecular changes in healthy human serum by infrared spectroscopy coupled with chemometrics

From the beginning of the COVID-19 coronavirus pandemic in December of 2019, the disease has infected millions of people worldwide and caused hundreds of thousands of deaths. Since then, several vaccines have been developed. One of those vaccines is inactivated CoronaVac-Sinovac COVID-19 vaccine. In this proof of concept study, we first aimed to determine CoronaVac-induced biomolecular changes in healthy human serum using infrared spectroscopy. Our second aim was to see whether the vaccinated group can be separated or not from the non-vaccinated group by applying chemometric techniques to spectral data. The results revealed that the vaccine administration induced significant changes in some functional groups belonging to lipids, proteins and nucleic acids. In addition, the non-vaccinated and vaccinated groups were successfully separated from each other by principal component analysis (PCA) and linear discriminant analysis (LDA). This proof-of-concept study will encourage future studies on CoronaVac as well as other vaccines and will lead to make a comparison between different vaccines to establish a better understanding of the vaccination outcomes on serum biomolecules.

FTIR Spectroscopic Imaging Supports Urine Cytology for Classification of Low- and High-Grade Bladder Carcinoma

Simple Summary

Human urine cytological samples were investigated using Fourier transform infrared spectroscopic imaging in terms of recognition of bladder cancer. The clustering of IR spectra of whole cytological smears revealed very good spectral correlation with normal urothelial cell features. Next, the combination of spectral information derived from unsupervised hierarchical cluster analysis and partial least square discriminant analysis (PLS-DA) classified normal vs. low- and high-grade bladder urothelial carcinoma with sensitivity and specificity of 90–97%.

Abstract

Bladder urothelial carcinoma (BC) is a common, recurrent, life-threatening, and unpredictable disease which is difficult to diagnose. These features make it one of the costliest malignancies. Although many possible diagnostic methods are available, molecular heterogeneity and difficulties in cytological or histological examination induce an urgent need to improve diagnostic techniques. Herein, we applied Fourier transform infrared spectroscopy in imaging mode (FTIR) to investigate patients’ cytology samples assigned to normal (N), low-grade (LG) and high-grade (HG) BC. With unsupervised hierarchical cluster analysis (UHCA) and hematoxylin-eosin (HE) staining, we observed a correlation between N cell types and morphology. High-glycogen superficial (umbrella) and low-glycogen piriform urothelial cells, both with normal morphology, were observed. Based on the spectra derived from UHCA, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed, indicating a variation of protein content between the patient groups. Moreover, BC spectral cytology identified a low number of high-glycogen cells for which a shift of the carbohydrate/phosphate bands was also observed. Despite high cellular heterogeneity, PLS-DA was able to classify the spectra obtained. The voided urine FTIR cytology is one of the options that might be helpful in BC diagnosis, as high sensitivity and specificity up to 97% were determined.

Novel approaches for COVID-19 diagnosis and treatment: a nonsystematic review

Since COVID-19 pandemic has been continuously rising and spreading, several original contributions and review articles on COVID-19 started to appear in the literature. The review articles are mainly focus on the current status of the pandemic along with current status of the corona diagnosis and treatment process. Due to some disadvantages of the currently used methods, the improvement on the novel promising diagnosis and treatment methods of corona virus is very important issue. In this review, after briefly discussing the status of current diagnosis and treatment methods, we present to the scientific community, novel promising methods in the diagnosis and treatment of COVID-19. As with other novel approaches, first, the diagnosis potential of mass spectroscopy and optical spectroscopic methods such as UV/visible, infrared, and Raman spectroscopy coupled with chemometrics will be discussed for the corona virus infected samples based on the relevant literature. In vibrational spectroscopy studies, due to complexity of the data, multivariate analysis methods are also applied to data. The application of multivariate analysis tools that can be used to extract useful information from the data for diagnostic and characterisation purposes is also included in this review. The reviewed methods include hierarchical cluster analysis, principal component analysis, linear and quadratic discriminant analysis, support vector machine algorithm, and one form of neural networks namely deep learning method. Second, novel treatment methods such as photodynamic therapy and the use of nanoparticles in the in-corona virus therapy will be discussed. Finally, the advantages of novel promising diagnosis and treatment methods in COVID-19, over standard methods will be discussed. One of the main aims of this paper is to encourage the scientific community to explore the potential of this novel tools for their use in corona virus characterization, diagnosis, and treatment.

In Vitro Spectroscopy-Based Profiling of Urothelial Carcinoma: A Fourier Transform Infrared and Raman Imaging Study

Simple Summary

The mortality and recurrence associated with urothelial carcinoma are high. High heterogeneity makes it hard to detect with currently available methods such as cytology and histology. We propose here vibrational spectroscopic imaging as an additional diagnostic tool for the classification of bladder cancer. Our study revealed that chemism-induced spectroscopic features of the cancer cells of various stages and invasiveness were specifically detected.

Abstract

Markers of bladder cancer cells remain elusive, which is a major cause of the low recognition of this malignant neoplasm and its recurrence. This implies an urgent need for additional diagnostic tools which are based on the identification of the chemism of bladder cancer. In this study, we employed label-free techniques of molecular imaging—Fourier Transform Infrared and Raman spectroscopic imaging—to investigate bladder cancer cell lines of various invasiveness (T24a, T24p, HT-1376, and J82). The urothelial HCV-29 cell line was the healthy control. Specific biomolecules discriminated spatial distribution of the nucleus and cytoplasm and indicated the presence of lipid bodies and graininess in some cell lines. The most prominent discriminators are the total content of lipids and sugar moieties as well as the presence of glycogen and other carbohydrates, un/saturated lipids, cytochromes, and a level of S-S bridges in proteins. The combination of the obtained hyperspectral database and chemometric methods showed a clear differentiation of each cell line at the level of the nuclei and cytoplasm and pointed out spectral signals which differentiated bladder cancer cells. Registered spectral markers correlated with biochemical composition changes can be associated with pathogenesis and potentially used for the diagnosis of bladder cancer and response to experimental therapies.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical "fingerprint" with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

FTIR Spectroscopy of Cerebrospinal Fluid Reveals Variations in the Lipid: Protein Ratio at Different Stages of Alzheimer's Disease

Alzheimer's disease (AD) is a disease of advanced civilization and a common form of dementia in people over 65 years of age. We used Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) to determine changes in the quantity and quality of the cerebrospinal fluid from AD patients at three different stages of the disease (ADI, ADII, and ADIII), as well as from patients with mild cognitive impairment (MCI). Moreover, based on the FTIR spectra, we calculated the ratio of α-helix and β-sheet secondary protein structures as well as the lipid-protein balance as potential AD markers. The FTIR spectra of cerebrospinal fluid obtained from MCI, ADI, ADII, and ADIII patients showed that peaks corresponding to protein and deoxyribonucleic acid (DNA), and phospholipid and lipid vibrations were shifted in comparison with those of control subjects. Furthermore, the levels of these chemical compounds were lower in the patients than in the control subjects. The β-sheet secondary protein structure levels were increased in the MCI and AD patients compared with the control subjects. In addition, significant changes in the lipid-protein balance were observed. Interestingly, as the disease progressed, the lipid-protein balance became further disrupted, that is, the lipid amount decreased with disease progression. PCA analysis of lipid-protein FTIR regions revealed that the spectra could be used to distinguish between controls and patients with MCI, ADI, ADII, and ADIII.

Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects

Infrared spectroscopy has long been used to characterize chemical compounds, but the applicability of this technique to the analysis of biological materials containing highly complex chemical components is arguable. However, recent advances in the development of infrared spectroscopy have significantly enhanced the capacity of this technique in analyzing various types of biological specimens. Consequently, there is an increased number of studies investigating the application of infrared spectroscopy in screening and diagnosis of various diseases. The lack of highly sensitive and specific methods for early detection of cancer has warranted the search for novel approaches. Being more simple, rapid, accurate, inexpensive, non-destructive and suitable for automation compared to existing screening, diagnosis, management and monitoring methods, Fourier transform infrared spectroscopy can potentially improve clinical decision-making and patient outcomes by detecting biochemical changes in cancer patients at the molecular level. Besides the commonly analyzed blood and tissue samples, extracellular vesicle-based method has been gaining popularity as a non-invasive approach. Therefore, infrared spectroscopic analysis of extracellular vesicles could be a useful technique in the future for biomedical applications. In this review, we discuss the potential clinical applications of Fourier transform infrared spectroscopic analysis using various types of biological materials for cancer. Additionally, the rationale and advantages of using extracellular vesicles in the spectroscopic analysis for cancer diagnostics are discussed. Furthermore, we highlight the challenges and future directions of clinical translation of the technique for cancer.

Novel urinary biomarkers for the detection of bladder cancer: A systematic review

BACKGROUND

Urinary biomarkers for the diagnosis of bladder cancer represents an area of considerable research which has been tested in both patients presenting with haematuria and non-muscle invasive bladder cancer patients requiring surveillance cystoscopy. In this systematic review, we identify and appraise the diagnostic sensitive and specificity of reported novel biomarkers of different 'omic' class and highlight promising biomarkers investigated to date.

METHODS

A MEDLINE/Pubmed systematic search was performed between January 2013 and July 2017 using the following keywords: (bladder cancer OR transitional cell carcinoma OR urothelial cell carcinoma) AND (detection OR diagnosis) AND urine AND (biomarker OR assay). All studies had a minimum of 20 patients in both bladder cancer and control arms and reported sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. QUADAS-2 tool was used to assess risk of bias and applicability of studies. The search protocol was registered in the PROSPERO database (CRD42016049918).

RESULTS

Systematic search yielded 115 reports were included for analysis. In single target biomarkers had a sensitivity of 2-94%, specificity of 46-100%, positive predictive value (PPV) of 47-100% and negative predictive value (NPV) of 21-94%. Multi-target biomarkers achieved a sensitivity of 24-100%, specificity of 48-100%, PPV of 42-95% and NPV of 32-100%. 50 studies achieved a sensitivity and specificity of ≥80%. Protein (n = 59) and transcriptomic (n = 21) biomarkers represents the most studied biomarkers. Multi-target biomarker panels had a better diagnostic accuracy compared to single biomarker targets. Urinary cytology with urinary biomarkers improved the diagnostic ability of the biomarker. The sensitivity and specificity of biomarkers were higher for primary diagnosis compared to patients in the surveillance setting. Most studies were case control studies and did not have a predefined threshold to determine a positive test result indicating a possible risk of bias.

CONCLUSION

This comprehensive systematic review provides an update on urinary biomarkers of different 'omic' class and highlights promising biomarkers. Few biomarkers achieve a high sensitivity and negative predictive value. Such biomarkers will require external validation in a prospective observational setting before adoption in clinical practice.

Relapsing-Remitting Multiple Sclerosis diagnosis from cerebrospinal fluids via Fourier transform infrared spectroscopy coupled with multivariate analysis

Multiple sclerosis (MS) is a chronic, progressive, inflammatory and degenerative disease of central nervous system. Here, we aimed to develop a method for differential diagnosis of Relapsing-Remitting MS (RRMS) and clinically isolated syndrome (CIS) patients, as well as to identify CIS patients who will progress to RRMS, from cerebrospinal fluid (CSF) by infrared (IR) spectroscopy and multivariate analysis. Spectral analyses demonstrated significant differences in the molecular contents, especially in the lipids and Z conformation of DNA of CSF from CIS, CIS to RRMS transformed (TCIS) and RRMS groups. These changes enables the discrimination of diseased groups and controls (individuals with no neurological disease) from each other using hierarchical cluster and principal component analysis. Some CIS samples were consistently clustered in RRMS class, which may indicate that these CIS patients potentially will transform to RRMS over time. Z-DNA band at 795 cm⁻¹ that is existent only in diseased groups and significant increase in carbonyl amount, decrease in amideI/amide II and lipid/protein ratios observed only for RRMS groups can be used as diagnostic biomarkers. The results of the present study shed light on the early diagnosis of RRMS by IR spectroscopy complemented with multivariate analysis tools.

Differentiation of Chronic and Aggressive Periodontitis by FTIR Spectroscopy

Without longitudinal clinical data, it is difficult to differentiate some cases of chronic periodontitis (CP) and aggressive periodontitis (AgP). Furthermore, both forms of disease are exacerbated by tobacco use. Therefore, this cross-sectional study was planned, primarily, to determine the ability of Fourier-transform infrared (FTIR) spectroscopy to distinguish CP and AgP patients by analysis of human saliva samples and, secondarily, to assess the potential confounding influence of smoking on discriminating disease-specific spectral signatures. FTIR spectra were collected from patients with a clinical diagnosis of CP (n = 18; 7 smokers) or AgP (n = 23; 9 smokers). Self-reported smoking status, which may be unreliable, was confirmed by salivary cotinine analysis. Spectral band area analysis and hierarchical cluster analyses were performed to clarify if the 2 periodontitis groups as well as smoker and nonsmoker patients could be differentiated from each other. Significant variations in lipid, amino acid, lactic acid, and nucleic acid content were found between nonsmoker CP and AgP groups. Although significantly lower lipid, phospholipid, protein, amino acid, lactic acid, and nucleic acid content was noted in the smoker AgP group compared with the nonsmoker AgP group, in the CP group, phospholipid, protein, amino acid, and lactic acid content was significantly lower for smokers compared with the nonsmokers. Based on these variations, nonsmoker CP and AgP patients were discriminated from each other with high sensitivity and specificity. Successful differentiation was also obtained for the smoker CP and AgP groups. Thiocyanate levels successfully differentiated smokers from nonsmokers, irrespective of periodontal status, with 100% accuracy. Differentiation of AgP and CP forms, concomitant with determination of smoking status, may allow the dental health professional to tailor treatment accordingly.