Disease pattern recognition testing for rheumatoid arthritis using infrared spectra of human serum

Comparative profiling of serum biomarkers and ATR-FTIR spectroscopy for differential diagnosis of patients with rheumatoid and psoriatic arthritis - a pilot study

BACKGROUND

Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic inflammatory diseases in which innate and adaptive responses of the immune system are induced. RA and PsA have complex signaling pathways. Despite the differences in their clinical presentation, there is a great demand for fast and accurate diagnosis of diseases to implement treatment and plan an individual therapeutic strategy quickly. In this report, we present the results of differential diagnosis of patients with RA and PsA and healthy subjects (C, control group), allowing for reliable differentiation of groups of rheumatoid patients based on biochemical parameters, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectra, and combined data sets.

MATERIALS AND METHODS

Biochemical analyses, ELISA (enzyme-linked immunosorbent assays), and multiplex assays were conducted for blood sera from patients with RA (n = 32), patients with PsA (n = 28), and the control group (n = 18). ATR-FTIR spectra were collected for lyophilized sera.

RESULTS

The combination of six biochemical parameters (WBC, ESR, RF, CRP, HCC-4/CCL16, and HMGB1/HMGB) allowed the development of the partial least squares discriminant analysis (PLS-DA) model with an overall accuracy (OA) of 80% for test samples. The best separation between RA, PsA, and the control group was obtained utilizing spectral data. Using the interval PLS algorithm (iPLS) specific spectral ranges were selected and a classifier characterized by OA value for test set equal to 88% was obtained. This parameter, for the hybrid PLS-DA model constructed using selected biochemical parameters and a significantly reduced number of spectral variables, reached the level of 84%.

CONCLUSIONS

PLS-DA models developed on the basis of spectral data enable effective differentiation of patients with RA, patients with PsA, and healthy subjects. They appeared to be insensitive to existing inflammation processes which opens interesting perspectives for new diagnostic tests and algorithms for identification of patients with RA and PsA.

Fracture-related infection blood-based biomarkers: Diagnostic strategies

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

Infrared analysis in the aqueous humor of patients with uveitis: Preliminary results

Inflammatory processes affecting the uvea result in a temporary o permanent blurred vision and represent an important cause of visual impairment worldwide. It is often hard to make a precise diagnosis which is dependent on the clinical expertise, diagnostic tests, laboratory investigations in blood and sometimes in the aqueous humor (AH). With the aim of obtaining proof of principle Fourier Transformed Infrared (FT-IR) absorbance spectroscopy was applied to study the molecular composition of 72 AH samples collected in 26 patients with uveitis and in 44 controls. The unsupervised exploration of the internal structure of the dataset by principal component analysis reduced hundreds IR variables to those most representative allowing to obtain the predictive model that distinguished the AH spectra of patients with uveitis from controls. The same result was obtained by unsupervised agglomerative cluster analysis. After labeling the spectra with some clinical information it was observed that most severe uveitis with active processes were grouped separately from chronic and relapsing uveitis and controls. The consistence of prediction models is discussed in the light of supporting etiological diagnosis by machine learning processes. In conclusion, proof of principle has been obtained that the IR spectral pattern of AH may reflect particular uveal diseases.

Lipid bands of approx. 1740 cm-1 as spectral biomarkers and image of tissue oxidative stress

Studies with the use of FTIR and FTR methods to find spectroscopic biomarkers within the 1740 cm^-1 band of pathological tissues found that oxidative stress, including damage to epidermis and structural changes in pathological amnion and placenta tissue, are associated with the occurrence of products of lipid peroxidation and have impact on fluidity and transport function of membranes. In particular, the findings show that the absence of a marker lipid band of approx. 1743 cm^-1 and the occurrence of a minimum of 1764 cm^-1 (FTIR) and 1734 cm^-1 (FTR) testify to the integrity and absence of damage in the allogeneic dermis, while the presence the 1743 or 1747 cm^-1 bands indicates denaturation of the thermally or electrically burned epidermis. The absence of a marker lipid band of approx. 1736-1740 cm^-1 for a healthy placental and amniotic tissue and the presence of a band of 1740 cm^-1 indicate placental gestosis, while the presence of a band of 1742 cm^-1 indicates hypotrophy. The 1738 cm^-1 bands indicate amniotic macrosomia. Structural changes caused by tissue modification with antioxidants, which were observed on individual samples: the L-ascorbic acid (presence of a lipid band marker at a frequency of 1755 cm^-1), sodium ascorbate (disappearance of the marker band), orthosilicic acid (disappearance or decrease in the intensity of the marker band with a decrease in the concentration of the modifier), as well as graphene oxide (separation of the marker lipid band of 1755 cm^-1), inform us about the effect of modifiers on the tissue repair process. The studies also tracked spectral changes identified in serum. Withing the range of the lipid band and the amide I and II bands (α → β conversion), there are clear differences between normal and pathological serum lyophilisates and a sample analyzed from the solution.

Novel strategies to diagnose prosthetic or native bone and joint infections

INTRODUCTION

Bone and Joint Infections (BJI) are medically important, costly and occur in native and prosthetic joints. Arthroplasties will increase significantly in absolute numbers over time as well as the incidence of Prosthetic Joint Infections (PJI). Diagnosis of BJI and PJI is sub-optimal. The available diagnostic tests have variable effectiveness, are often below standard in sensitivity and/or specificity, and carry significant contamination risks during the collection of clinical samples. Improvement of diagnostics is urgently needed.

AREAS COVERED

We provide a narrative review on current and future diagnostic microbiology technologies. Pathogen identification, antibiotic resistance detection, and assessment of the epidemiology of infections via bacterial typing are considered useful for improved patient management. We confirm the continuing importance of culture methods and successful introduction of molecular, mass spectrometry-mediated and next-generation genome sequencing technologies. The diagnostic algorithms for BJI must be better defined, especially in the context of diversity of both disease phenotypes and clinical specimens rendered available.

EXPERT OPINION

Whether interventions in BJI or PJI are surgical or chemo-therapeutic (antibiotics and bacteriophages included), prior sensitive and specific pathogen detection remains a therapy-substantiating necessity. Innovative tests for earlier and more sensitive and specific detection of bacterial pathogens in BJI are urgently needed.

Ruling out septic arthritis risk in a few minutes using mid-infrared spectroscopy in synovial fluids

OBJECTIVES

The aim of this study was to show the usefulness of a mid-infrared fibre evanescent wave spectroscopy point of care device in the identification of septic arthritis patients in a multicentre cohort, and to apply this technology to clinical practice among physicians.

METHODS

SF samples from 402 patients enrolled in a multicentre cohort were frozen for analysis by mid-infrared fibre evanescent wave spectroscopy. The calibration cohort was divided into two groups of patients (septic arthritis and non-septic arthritis) and relevant spectral variables were used for logistic regression model. Model performances were tested on an independent set of 86 freshly obtained SF samples from patients enrolled in a single-centre acute arthritis cohort and spectroscopic analyses performed at the patient's bedside.

RESULTS

The model set-up, using frozen-thawed SFs, provided good performances, with area under the curve 0.95, sensitivity 0.90, specificity 0.90, positive predictive value 0.41 and negative predictive value 0.99. Performances obtained in the validation cohort were area under the curve 0.90, sensitivity 0.92, specificity 0.81, positive predictive value 0.46 and negative predictive value 0.98. The septic arthritis probability has been translated into a risk score from 0 to 4 according to septic risk. For a risk score of 0, the probability of identifying a septic patient is very low (negative predictive value of 1), whereas a risk score of 4 indicates very high risk of septic arthritis (positive predictive value of 1).

CONCLUSION

Mid-infrared fibre evanescent wave spectroscopy could distinguish septic from non-septic synovial arthritis fluids with good performances, and showed particular usefulness in ruling out septic arthritis. Our data supports the possibility of technology transfer.

TRIAL REGISTRATION

ClinicalTrials.gov, http://clinicaltrials.gov, NCT02860871.

Infrared spectroscopy of serum as a potential diagnostic screening approach for naturally occurring canine osteoarthritis associated with cranial cruciate ligament rupture

OBJECTIVE

To evaluate infrared (IR) spectroscopy of serum as a screening tool to differentiate dogs affected by naturally occurring osteoarthritis (OA) associated with cranial cruciate ligament rupture (CrCLR) and controls.

METHOD

104 adult dogs with CrCLR (affected group) and 50 adult control dogs were recruited for this prospective observational study. Serum samples were collected preoperatively from CrCLR dogs and from a subset of these dogs at 4-, and 12-week post-surgical intervention to stabilize the affected stifles. Serum was collected once from control dogs. Dry films were made from serum samples, and IR absorbance spectra acquired. Data preprocessing, principal component analysis and multivariate analysis of covariance were performed to separate samples from the two groups, and to evaluate temporal differences. Weighted logistic regression with L1 regularization method was used to develop a predictive model. Model performance based on an independent test set was evaluated.

RESULTS

Spectral data analysis revealed significant separation between the sera of CrCLR and control dogs (P < 0.0001), but not amongst different time points in the OA group. The sensitivity, specificity, AUC and accuracy of the test set were 84.62%, 96.15%, 93.20% and 92.31% respectively.

CONCLUSIONS

These findings confirm the potential of IR-spectroscopy of serum with chemometrics methods to differentiate controls from dogs with OA associated with CrCLR. This is the first step in development of an economic, and comparatively simple IR-based screening serum test for OA. Utility of this tool as a clinical screening and diagnostic test requires further investigation and validation.

Biomedical applications of mid-infrared quantum cascade lasers - a review

Mid-infrared spectroscopy has been applied to research in biology and medicine for more than 20 years and conceivable applications have been identified. More recently, these applications have been shown to benefit from the use of quantum cascade lasers due to their specific properties, namely high spectral power density, small beam parameter product, narrow emission spectrum and, if needed, tuning capabilities. This review provides an overview of the achievements and illustrates some applications which benefit from the key characteristics of quantum cascade laser-based mid-infrared spectroscopy using examples such as breath analysis, the investigation of serum, non-invasive glucose monitoring in bulk tissue and the combination of spectroscopy and microscopy of tissue thin sections for rapid histopathology.

Fourier Transform Infrared Spectroscopy and Photoacoustic Spectroscopy for Saliva Analysis

Saliva provides a valuable tool for assessing oral and systemic diseases, but concentrations of salivary components are very small, calling the need for precise analysis methods. In this work, Fourier transform infrared (FT-IR) spectroscopy using transmission and photoacoustic (PA) modes were compared for quantitative analysis of saliva. The performance of these techniques was compared with a calibration series. The linearity of spectrum output was verified by using albumin-thiocyanate (SCN(-)) solution at different SCN(-) concentrations. Saliva samples used as a comparison were obtained from healthy subjects. Saliva droplets of 15 µL were applied on the silicon sample substrate, 6 drops for each specimen, and dried at 37 ℃ overnight. The measurements were carried out using an FT-IR spectrometer in conjunction with an accessory unit for PA measurements. The findings with both transmission and PA modes mirror each other. The major bands presented were 1500-1750 cm(-1) for proteins and 1050-1200 cm(-1) for carbohydrates. In addition, the distinct spectral band at 2050 cm(-1) derives from SCN(-) anions, which is converted by salivary peroxidases to hypothiocyanate (OSCN(-)). The correlation between the spectroscopic data with SCN(-) concentration (r > 0.990 for transmission and r = 0.967 for PA mode) was found to be significant (P < 0.01), thus promising to be utilized in future applications.

Diagnostic prediction of renal failure from blood serum analysis by FTIR spectrometry and chemometrics

A new diagnostic approach based on Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectrometry and classification algorithm has been introduced which provides a rapid, reliable, and easy way to perform blood test for the diagnosis of renal failure. Blood serum samples from 35 renal failure patients and 40 healthy persons were analyzed by ATR-FTIR spectrometry. The resulting data was processed by Quadratic Discriminant Analysis (QDA) and QDA combined with simple filtered method. Spectroscopic studies were performed in 900-2000cm(-)(1) spectral region with 3.85cm(-1) data space. Results showed 93.33% and 100% of accuracy for QDA and filter-QDA models, respectively. In the first step, 30 samples were applied to construct the model. In order to modify the capability of QDA in prediction of test samples, filter-based feature selection methods were applied. It was found that the filtered spectra coupled with QDA could correctly predict the test samples in most of the cases.

Profiling serologic biomarkers in cirrhotic patients via high-throughput Fourier transform infrared spectroscopy: toward a new diagnostic tool of hepatocellular carcinoma

Identification of novel serum biomarkers of hepatocellular carcinoma (HCC) is needed for early-stage disease detection and to improve patients' survival. The aim of this study was to evaluate the potential of serum Fourier transform infrared (FTIR) spectroscopy for differentiating sera from cirrhotic patients with and without HCC. Serum samples were collected from 2 sets of patients: cirrhotic patients with HCC (n = 39) and without HCC (n = 40). The FTIR spectra (10 per sample) were acquired in the transmission mode, and data homogeneity was tested by cluster analysis to exclude outliers. After data preprocessing by extended multiplicative signal correction and principal component analysis, the Support Vector Machine (SVM) method was applied using a leave-one-out cross-validation algorithm to classify the spectra into 2 classes of cirrhotic patients with and without HCC. When SVM was applied to all spectra (n = 790), the sensitivity and the specificity for the diagnosis of HCC were, respectively, 82.02% and 82.5%. When applied to the subset of spectra excluding the outliers (n = 739), SVM classification led to a sensitivity and specificity of 87.18% and 85%, respectively. Using median spectra for each patient instead of all replicates, the sensitivity and specificity were 84.62% and 82.50%, respectively. The overall accuracy rate was 82%-86%. In conclusion, this study suggests that FTIR spectroscopy combined with advanced methods of pattern analysis shows potential for differentiating sera from cirrhotic patients with and without HCC.

HHT diagnosis by Mid-infrared spectroscopy and artificial neural network analysis

BACKGROUND

The vascular disorder Hereditary Hemorrhagic Telangiectasia (HHT) is in general an inherited disease caused by mutations in the TGF-β/BMP receptors endoglin or ALK1 or in rare cases by mutations of the TGF-β signal transducer protein Smad4 leading to the combined syndrome of juvenile polyposis and HHT. HHT is characterized by several clinical symptoms like spontaneous and recurrent epistaxis, multiple telangiectases at sites like lips, oral cavity, fingers, nose, and visceral lesions like gastrointestinal telangiectasia, pulmonary, hepatic, cerebral or spinal arteriovenous malformations. The disease shows an inter- and intra-family variability in penetrance as well as symptoms from mild to life threatening. Penetrance is also depending on age. Diagnosis of the disease is based on the presence of some of the listed symptoms or by genetic testing. HHT diagnosis is laborious, time consuming, costly and sometimes uncertain. Not all typical symptoms may be present, especially at a younger age, and genetic testing does not always identify the disease causing mutation.

METHODS

Infrared (IR) spectroscopy was investigated as a potential alternative to the current diagnostic methods. IR-spectra were obtained by Fourier-transform Mid-IR spectroscopy from blood plasma from HHT patients and a healthy control group. Spectral data were mathematically processed and subsequently classified and analysed by artificial neural network (ANN) analyses and by visual analysis of scatter plots of the dominant principal components.

RESULTS

The analyses showed that for HHT a disease specific IR-spectrum exists that is significantly different from the control group. Furthermore, at the current stage with the here used methods, HHT can be diagnosed by Mid-IR-spectroscopy in combination with ANN analysis with a sensitivity and specificity of at least 95%. Visual analysis of PCA scatter plots revealed an inter class variation of the HHT group.

CONCLUSION

IR-spectroscopy in combination with ANN analysis can be considered to be a serious alternative diagnostic method compared to clinical and genetically based methods. Blood plasma is an ideal candidate for diagnostic purposes, it is inexpensive, easy to isolate and only minimal amounts are required. In addition, IR-spectroscopy measurement times are fast, less than one minute, and diagnosis is not based on interpretation of may be uncertain clinical data. And last but not least, the method is inexpensive.

Comparative assessment of essential and toxic metals in the blood of rheumatoid arthritis patients and healthy subjects

The present study deals with the comparative evaluation of essential and toxic metals in rheumatoid arthritis and healthy donors. Blood samples collected from rheumatoid arthritis patients and healthy subjects were analysed for selected essential and toxic metals (Ca, Mg, Fe, Zn, Cu, Co, Mn, Cr, Cd and Pb). The samples were digested in nitric acid and perchloric acid mixture, followed by quantification of the metals using atomic absorption spectrometry. Mean levels of Ca, Mg, Fe and Zn were significantly higher in the blood of healthy donors; however, elevated levels of Cd, Co, Cr, Cu and Pb were observed in blood of the patients. The correlation coefficients among the selected metals in the blood of arthritis patients were significantly different compared with the healthy counterparts. Multivariate cluster analysis revealed mutual apportionment of the essential and toxic metals in blood of the patients, whereas, in controls, the essential and toxic metals revealed diverse apportionment. Variations in the metal levels with gender, residence and smoking habits were also evaluated in both donor groups. Relative distribution, correlation and apportionment of the essential and toxic metals in the blood of the patients were significantly different than of controls.

A factorization method for the classification of infrared spectra

Background

Bioinformatics data analysis often deals with additive mixtures of signals for which only class labels are known. Then, the overall goal is to estimate class related signals for data mining purposes. A convenient application is metabolic monitoring of patients using infrared spectroscopy. Within an infrared spectrum each single compound contributes quantitatively to the measurement.

Results

In this work, we propose a novel factorization technique for additive signal factorization that allows learning from classified samples. We define a composed loss function for this task and analytically derive a closed form equation such that training a model reduces to searching for an optimal threshold vector. Our experiments, carried out on synthetic and clinical data, show a sensitivity of up to 0.958 and specificity of up to 0.841 for a 15-class problem of disease classification. Using class and regression information in parallel, our algorithm outperforms linear SVM for training cases having many classes and few data.

Conclusions

The presented factorization method provides a simple and generative model and, therefore, represents a first step towards predictive factorization methods.

Diabetes-related molecular signatures in infrared spectra of human saliva

BACKGROUND

There is an ongoing need for improvements in non-invasive, point-of-care tools for the diagnosis and prognosis of diabetes mellitus. Ideally, such technologies would allow for community screening.

METHODS

In this study, we employed infrared spectroscopy as a novel diagnostic tool in the prediction of diabetic status by analyzing the molecular and sub-molecular spectral signatures of saliva collected from subjects with diabetes (n = 39) and healthy controls (n = 22).

RESULTS

Spectral analysis revealed differences in several major metabolic components - lipid, proteins, glucose, thiocyanate and carboxylate - that clearly demarcate healthy and diseased saliva. The overall accuracy for the diagnosis of diabetes based on infrared spectroscopy was 100% on the training set and 88.2% on the validation set. Therefore, we have established that infrared spectroscopy can be used to generate complex biochemical profiles in saliva and identify several potential diabetes-associated spectral features.

CONCLUSIONS

Infrared spectroscopy may represent an appropriate tool with which to identify novel diseases mechanisms, risk factors for diabetic complications and markers of therapeutic efficacy. Further study into the potential utility of infrared spectroscopy as diagnostic and prognostic tool for diabetes is warranted.

Spectroscopic diagnosis of myocardial infarction and heart failure by Fourier transform infrared spectroscopy in serum samples

Cardiovascular disease is the leading cause of death in Western civilization. In this pilot study we evaluated a new method for the diagnosis of myocardial infarction and heart failure by determining the typical fingerprint in the infrared (IR) spectrum of 1 microL of a dried patient serum sample by Fourier transform IR spectroscopy. For classification, cluster analysis and artificial neural networks (ANN) were applied. In this study 567 subjects were enrolled, comprising 225 controls (Co) and 342 patients with myocardial infarction (MI) (n = 157) and heart failure (HF) (n = 185). By applying artificial neural network algorithms, the following sensitivities and specificities of the same spectra were determined: MI versus Co (98%, 97%), HF versus Co (98%, 100%), MI versus HF (100%, 100%), and MI plus HF versus Co (100%, 100%). Based on our data, mid-IR spectroscopy appears to be a promising new method to diagnose heart diseases from serum samples. Artificial neural network algorithms proved to be superior to cluster analysis for correct prediction.

Toward point-of-care diagnostic metabolic fingerprinting: quantification of plasma creatinine by infrared spectroscopy of microfluidic-preprocessed samples

Infrared (IR) spectroscopy has previously been established as a means to accurately quantify several serum and urine metabolites, based upon spectroscopy of dry films. The same technique has also provided the basis to develop certain diagnostic tests, developed in the 'metabolomics' spirit. Here, we report on the further development of an integrated microfluidic-IR technology and technique, customized with the aim of dramatically extending the capabilities of IR spectroscopy in both analytical and diagnostic (metabolomic) applications. By exploiting the laminar fluid diffusion interface (LFDI), serum specimens are processed to yield product streams that are better suited for metabolic fingerprinting; metabolites are captured within the aqueous product stream, while proteins (which otherwise dominate the spectra of films dried from serum) are present in much reduced concentration. Spectroscopy of films dried from the aqueous stream then provides enhanced diagnostic and analytical sensitivity. The manuscript introduces an LFDI card design that is customized for integration with IR spectroscopy, and details the development of a quantitative assay for serum creatinine--based upon LFDI-processed serum samples--that is substantially more accurate (standard error of calibration, SEC = 43 micromol/L) than the corresponding assay based upon unprocessed serum specimens (SEC = 138 micromol/L). Preliminary results of diffusion modeling are reported, and the prospects for further optimization of the technique, guided by accurate modeling, are discussed.

Metabolic fingerprinting as a diagnostic tool

Within the framework of systems biology, functional analyses at all 'omic levels have seen an intense level of activity during the first decade of the twenty-first century. These include genomics, transcriptomics, proteomics, metabolomics and lipidomics. It could be said that metabolomics offers some unique advantages over the other 'omics disciplines and one of the core approaches of metabolomics for disease diagnostics is metabolic fingerprinting. This review provides an overview of the main metabolic fingerprinting approaches used for disease diagnostics and includes: infrared and Raman spectroscopy, Nuclear magnetic resonance (NMR) spectroscopy, followed by an introduction to a wide range of novel mass spectrometry-based methods, which are currently under intense investigation and developmental activity in laboratories worldwide. It is hoped that this review will act as a springboard for researchers and clinicians across a wide range of disciplines in this exciting era of multidisciplinary and novel approaches to disease diagnostics.

Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy

The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.

Pancreas and cystic fibrosis: the implications of increased survival in cystic fibrosis

Pancreatitis affects 0.5% people with cystic fibrosis (CF) in the UK and 0.01% of the normal population. Why do some with CF get pancreatitis and some not? And does pancreatitis in neonates result in pancreatic failure with no further inflammation or risk of pancreatic cancer? Review of the literature would suggest that 85% of those with CF have pancreatic destruction as children with minimal risk of further inflammatory or neoplastic changes. Those with a functioning pancreas are at risk of developing pancreatitis. There are several case series of pancreatic cancer reported in CF patients, but overall the risk is unknown. As patients with CF and pancreatic sufficiency are living longer, further studies to assess the risk of developing pancreatic cancer in this subgroup should be considered.

Identification of infrared absorption spectral characteristics of synovial fluid of horses with osteochondrosis of the tarsocrural joint

OBJECTIVE

To determine the feasibility of the use of Fourier-transform infrared (FTIR) spectroscopy within the midinfrared range to differentiate synovial fluid samples of joints with osteochondrosis from those of control samples.

ANIMALS

33 horses with osteochondrosis of the tarsocrural joint and 31 horses free of tarsocrural joint disease.

PROCEDURES

FTIR spectroscopy of synovial fluid was used. Sixty-four synovial fluid samples from the tarsocrural joint were collected. Of these, 33 samples were from horses with radiographic evidence of osteochondrosis of the tarsocrural joint and 31 from control joints. Disease-associated features within infrared spectra of synovial fluid were statistically selected for spectral classification, and the variables identified were used in a classification model. Linear discriminant analysis and leave-one-out cross-validation were used to develop a classifier to identify joints with osteochondrosis.

RESULTS

12 significant subregions were identified that met the selection criteria. The stepwise discriminant procedure resulted in the final selection of 6 optimal regions that most contributed to the discriminatory power of the classification algorithm. Infrared spectra derived from synovial fluid of joints with osteochondrosis were differentiated from the control samples with accuracy of 77% (81% specificity and 73% sensitivity).

CONCLUSIONS AND CLINICAL RELEVANCE

The disease-associated characteristics of infrared spectra of synovial fluid from joints with osteochondrosis may be exploited via appropriate feature selection and classification algorithms to differentiate joints with osteochondrosis from those of control joints. Further study with larger sample size including age-, breed-, and sex-matched control horses would further validate the clinical value of infrared spectroscopy for the diagnosis of osteochondrosis in horses.

USEFULNESS OF INFRARED SPECTROSCOPY IN DIAGNOSIS OF ACUTE PANCREATITIS

BACKGROUND

The lack of a gold standard for the diagnosis of acute pancreatitis remains a problem. Our aim was to evaluate whether infrared spectroscopy of serum can establish the diagnosis of acute pancreatitis.

METHODS

Sixty-four patients with acute pancreatitis, 112 patients with non-pancreatic acute abdomen and 40 healthy subjects were studied. In addition to serum infrared spectral analysis, serum concentrations of amylase and lipase were measured on admission.

RESULTS

Infrared spectroscopy based on serum absorption patterns in the range 800-1000 nm successfully distinguished acute pancreatitis from acute abdominal disorders of extrapancreatic origin and from control specimens. The sensitivity, specificity and positive and negative predictive values of infrared spectroscopy on admission were 91, 91, 85, and 94%, respectively. Within 24 h of onset of symptoms, infrared spectroscopy, lipase and amylase showed similar areas under the ROC curves for infrared spectra of serum (0.93), lipase (0.96) and amylase (0.91).

CONCLUSIONS

The successful classification of infrared spectra in patients with acute pancreatitis implies that the pathophysiology of disease alters the composition of the specimen in a characteristic fashion--in this case the serum make-up reflects the presence of acute pancreatitis.

Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy

A hierarchical scheme has been developed for detection of bovine spongiform encephalopathy (BSE) in serum on the basis of its infrared spectral signature. In the first stage, binary subsets between samples originating from diseased and non-diseased cattle are defined along known covariates within the data set. Random forests are then used to select spectral channels on each subset, on the basis of a multivariate measure of variable importance, the Gini importance. The selected features are then used to establish binary discriminations within each subset by means of ridge regression. In the second stage of the hierarchical procedure the predictions from all linear classifiers are used as input to another random forest that provides the final classification. When applied to an independent, blinded validation set of 160 further spectra (84 BSE-positives, 76 BSE-negatives), the hierarchical classifier achieves a sensitivity of 92% and a specificity of 95%. Compared with results from an earlier study based on the same data, the hierarchical scheme performs better than linear discriminant analysis with features selected by genetic optimization and robust linear discriminant analysis, and performs as well as a neural network and a support vector machine.

Use of Fourier transform infrared microscopy for the evaluation of drug efficiency

Fourier transform infrared (FTIR) spectroscopy has been used by chemists as a powerful tool to characterize inorganic and organic compounds. In this study, we examine the potential of FTIR microspectroscopy for early evaluation of therapy efficiency. For this purpose, we examine the effect of acyclovir (a known antiherpetic drug) on the development of herpes simplex virus type 1 (HSV-1) infection in cell culture. Also, we examine spectral changes in lymphocytes obtained from leukemia patients after appropriate chemotherapy treatment. Our results show early and significant spectral indicators for successful infection of Vero cells with HSV-1. Treatment of these infected cells with increasing doses of acyclovir reduces clearly the spectral changes caused by the infection in a correlation with inhibiting the development of the cytopathic effect (CPE) induced by this virus. Also significant and consistent spectral differences between lymphocytes from human leukemia patients compared to that from healthy persons are obtained. Treatment of these leukemia patients with appropriate drugs reduces significantly these spectral differences in a correlation with the improvement of the patient's clinical situation. It seems that FTIR spectroscopy can be used as an effective tool for early evaluation of the efficiency of drugs.

Use of infrared spectroscopy for diagnosis of traumatic arthritis in horses

OBJECTIVE

To evaluate use of infrared spectroscopy for diagnosis of traumatic arthritis in horses.

ANIMALS

48 horses with traumatic arthritis and 5 clinically and radiographically normal horses.

PROCEDURES

Synovial fluid samples were collected from 77 joints in 48 horses with traumatic arthritis. Paired samples (affected and control joints) from 29 horses and independent samples from an affected (n = 12) or control (7) joint from 19 horses were collected for model calibration. A second set of 20 normal validation samples was collected from 5 clinically and radiographically normal horses. Fourier transform infrared spectra of synovial fluids were acquired and manipulated, and data from affected joints were compared with controls to identify spectroscopic features that differed significantly between groups. A classification model that used linear discriminant analysis was developed. Performance of the model was determined by use of the 2 validation datasets.

RESULTS

A classification model based on 3 infrared regions classified spectra from the calibration dataset with overall accuracy of 97% (sensitivity, 93%; specificity, 100%). The model, with cost-adjusted prior probabilities of 0.60:0.40, yielded overall accuracy of 89% (sensitivity, 83%; specificity, 100%) for the first validation sample dataset and 100% correct classification of the second set of independent normal control joints.

CONCLUSIONS AND CLINICAL RELEVANCE

The infrared spectroscopic patterns of fluid from joints with traumatic arthritis differed significantly from the corresponding patterns for controls. These alterations in absorption patterns may be used via an appropriate classification algorithm to differentiate the spectra of affected joints from those of controls.

Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy

The ability to diagnose the early onset of disease, rapidly, non-invasively and unequivocally has multiple benefits. These include the early intervention of therapeutic strategies leading to a reduction in morbidity and mortality, and the releasing of economic resources within overburdened health care systems. Some of the routine clinical tests currently in use are known to be unsuitable or unreliable. In addition, these often rely on single disease markers which are inappropriate when multiple factors are involved. Many diseases are a result of metabolic disorders, therefore it is logical to measure metabolism directly. One of the strategies employed by the emergent science of metabolomics is metabolic fingerprinting; which involves rapid, high-throughput global analysis to discriminate between samples of different biological status or origin. This review focuses on a selective number of recent studies where metabolic fingerprinting has been forwarded as a potential tool for disease diagnosis using infrared and Raman spectroscopies.

Laminar fluid diffusion interface preconditioning of serum and urine for reagent-free infrared clinical analysis and diagnostics

A number of reagent-free infrared spectroscopic diagnostic and analytical methods have been established previously making use of dry biofluid films. For example, this approach has successfully measured high concentration analytes of serum and urine. However, a number of low concentration diagnostically relevant analytes presently elude detection by infrared spectroscopy. This is due in part to their relatively low concentration and in part to spectral interference by other strongly absorbing constituents. The applicability of the technique would be broadened substantially if it were possible to concentrate and separate lower concentration analytes, e. g., serum creatinine and urine proteins, from the obscuring presence of relatively high concentration compounds. One possible means to achieve this is through microfluidic sample preconditioning based on laminar fluid diffusion interfaces. The objective of this study was therefore to qualitatively assess the performance of this technology in preferentially separating certain serum and urine analytes of clinical interest that presently lie just below the threshold of detection by infrared spectroscopy. Observations from simulated and genuine urine and serum samples strongly suggest that this process should improve existing accuracy and extend the range of detectable analytes.

Mapping high-dimensional data onto a relative distance plane—an exact method for visualizing and characterizing high-dimensional patterns

We introduce a distance (similarity)-based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances between points with respect to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). As only a single calculation of a distance matrix is required, this method is computationally efficient, an essential requirement for any exploratory data analysis. The data visualization afforded by this representation permits a rapid assessment of class pattern distributions. In particular, we can determine with a simple statistical test whether both training and validation sets of a 2-class, high-dimensional dataset derive from the same class distributions. We can explore any dataset in detail by identifying the subset of reference pairs whose members belong to different classes, cycling through this subset, and for each pair, mapping the remaining patterns. These multiple viewpoints facilitate the identification and confirmation of outliers. We demonstrate the effectiveness of this method on several complex biomedical datasets. Because of its efficiency, effectiveness, and versatility, one may use the RDP representation as an initial, data mining exploration that precedes classification by some classifier. Once final enhancements to the RDP mapping software are completed, we plan to make it freely available to researchers.

Classification of signatures of Bovine Spongiform Encephalopathy in serum using infrared spectroscopy

Signatures of Bovine Spongiform Encephalopathy (BSE) have been identified in serum by means of "Diagnostic Pattern Recognition (DPR)". For DPR-analysis, mid-infrared spectroscopy of dried films of 641 serum samples was performed using disposable silicon sample carriers and a semi-automated DPR research system operating at room temperature. The combination of four mathematical classification approaches (principal component analysis plus linear discriminant analysis, robust linear discriminant analysis, artificial neural network, support vector machine) allowed for a reliable assignment of spectra to the class "BSE-positive" or "BSE-negative". An independent, blinded validation study was carried out on a second DPR research system at the Veterinary Laboratory Agency, Weybridge, UK. Out of 84 serum samples originating from terminally-ill, BSE-positive cattle, 78 were classified correctly. Similarly, 73 out of 76 BSE-negative samples were correctly identified by DPR such that, numerically, an accuracy of 94.4 % can be calculated. At a confidence level of 0.95 (alpha = 0.05) these results correspond to a sensitivity > 85% and a specificity > 90%. Identical class assignment by all four classifiers occurred in 75% of the cases while ambiguous results were obtained in only 8 of the 160 cases. With an area under the ROC (receiver operating charateristics) curve of 0.991, DPR may potentially supply a valuable surrogate marker for BSE even in cases in which a deliberate bias towards improved sensitivity or specificity is desired. To the best of our knowledge, DPR is the first and--up to now--only method which has demonstrated its capability of detecting BSE-related signatures in serum.

Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for idiosyncratic toxicity

An evaluation of high-throughput Fourier-transform infrared spectroscopy (FT-IR) as a technology that could support a "metabonomics" component in toxicological studies of drug candidates is presented. The hypothesis tested in this study was that FT-IR had sufficient resolving power to discriminate between urine collected from control rat populations and rats subjected to treatment with a potent inflammatory agent, bacterial lipopolysaccharide (LPS). It was also hypothesized that co-administration of LPS with ranitidine, a drug associated with reports of idiosyncratic susceptibility, would induce hepatotoxicity in rats and that this could be detected non-invasively by an FT-IR-based metabonomics approach. The co-administration of LPS with "idiosyncratic" drugs represents an attempt to develop a predictive model of idiosyncratic toxicity and FT-IR is used herein to support characterization of this model. FT-IR spectra are high dimensional and the use of genetic programming to identify spectral sub-regions that most contribute to discrimination is demonstrated. FT-IR is rapid, reagentless, highly reproducible and inexpensive. Results from this pilot study indicate it could be extended to routine applications in toxicology and to supporting characterization of a new animal model for idiosyncratic susceptibility.

Studies on acute human infections using FTIR microspectroscopy and cluster analysis

A novel methodology for the diagnosis of acute infections using FTIR microspectroscopy (FTIR-MSP) data on blood components and cluster analysis is presented. Blood samples were collected from 11 patients suffering from various infections and 16 age-matched healthy human controls. Blood components such as white blood cells, red blood cells, and plasma were isolated using standard procedures and FTIR-MSP of these components was utilized. A cluster analysis of the FTIR spectra was performed. The spectra obtained from the three blood components of patients were different from those of controls. The FTIR spectra of white blood cells from patients suffering infections were significantly different from the controls. Cluster analyses of averaged FTIR-MSP spectra of white blood cells provided 100% classification between patients and healthy controls.