Serum-based ATR-FTIR spectroscopy combined with multivariate analysis for the diagnosis of pre-diabetes and diabetes.

Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity.

There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15-30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results (n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value.

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 µg/mL and >0.5 µg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting.

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab "dip" test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab "dip" test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme.

Point-of-Care Disease Screening in Primary Care Using Saliva: A Biospectroscopy Approach for Lung Cancer and Prostate Cancer.

Saliva is a largely unexplored liquid biopsy that can be readily obtained noninvasively. Not dissimilar to blood plasma or serum, it contains a vast array of bioconstituents that may be associated with the absence or presence of a disease condition. Given its ease of access, the use of saliva is potentially ideal in a point-of-care screening or diagnostic test. Herein, we developed a swab "dip" test in saliva obtained from consenting patients participating in a lung cancer-screening programme being undertaken in north-west England. A total of 998 saliva samples (31 designated as lung-cancer positive and 17 as prostate-cancer positive) were taken in the order in which they entered the clinic (i.e., there was no selection of participants) during the course of this prospective screening programme. Samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. In addition to unsupervised classification on resultant infrared (IR) spectra using principal component analysis (PCA), a range of feature selection/extraction algorithms were tested. Following preprocessing, the data were split between training (70% of samples, 22 lung-cancer positive versus 664 other) and test (30% of samples, 9 lung-cancer positive versus 284 other) sets. The training set was used for model construction and the test set was used for validation. The best model was the PCA-quadratic discriminant analysis (QDA) algorithm. This PCA-QDA model was built using 8 PCs (90.4% of explained variance) and resulted in 93% accuracy for training and 91% for testing, with clinical sensitivity at 100% and specificity at 91%. Additionally, for prostate cancer patients amongst the male cohort (n = 585), following preprocessing, the data were split between training (70% of samples, 12 prostate-cancer positive versus 399 other) and test (30% of samples, 5 prostate-cancer positive versus 171 other) sets. A PCA-QDA model, again the best model, was built using 5 PCs (84.2% of explained variance) and resulted in 97% accuracy for training and 93% for testing, with clinical sensitivity at 100% and specificity at 92%. These results point to a powerful new approach towards the capability to screen large cohorts of individuals in primary care settings for underlying malignant disease.

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky-Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard-Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

Fourier-transform infrared spectroscopy discriminates a spectral signature of endometriosis independent of inter-individual variation.

Endometriosis is the growth of endometrial tissue outside of the uterine cavity. Its aetiology remains obscure, and it is difficult to diagnose ranging from asymptomatic to debilitating disease. Mid-infrared (IR) spectroscopy has become recognised as a potential clinical diagnostic tool. Biomolecules absorb mid-IR (4000 cm(-1) to 400 cm(-1)) and from this, a biochemical-cell fingerprint in the form of an absorbance spectrum can be derived. We set out to determine if IR spectroscopy could be used to identify underlying biochemical differences between endometrial tissues growing outside of the uterus (ectopic) from endometrial tissue of the uterus (eutopic). For comparative purposes, endometrial tissues from endometriosis-free women were also obtained (benign eutopic). Attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy or transmission FTIR microspectroscopy was employed for spectral acquisition. Principal component analysis (PCA)-linear discriminant analysis (LDA) was used for chemometric analysis. A clear segregation was exhibited between the three categories independent of inter-individual confounding differences. Importantly, there was a marked difference between eutopic endometrial tissue from patients with or without endometriosis. This indicates that IR spectroscopy coupled with multivariate analysis (e.g., PCA-LDA) may provide a non-invasive diagnostic tool for endometriosis. By analysing the underlying biochemistry of these endometrial tissues, this approach may facilitate a better understanding of this pathology.

Elevated expression of CYP1A1 and gamma-SYNUCLEIN in human ectopic (ovarian) endometriosis compared with eutopic endometrium.

Endometriosis is a debilitating disease in which apoptotic, genetic, immunological, angiogenic and environmental factors have been implicated. Endocrine-disrupting agents (e.g. dioxins) might be involved. Dioxins, via the arylhydrocarbon receptor (AhR), induce estrogen-metabolizing enzymes CYP1A1 and CYP1B1. Elevated expression of gamma-SYNUCLEIN (gamma-SYN) has been associated with hormone-related conditions. Tissue sets consisting of eutopic and ectopic (ovarian) endometrium from patients with stage 3 or 4 endometriosis were obtained. Following RNA extraction and reverse transcription, quantitative real-time reverse transcriptase-polymerase chain reaction was performed for anti-apoptotic B-cell leukaemia/lymphoma 2 (BCL-2), CYP1A1, CYP1B1, estrogen receptor (ER)alpha, ER beta and gamma-SYN. Immunohistochemical analyses for gamma-syn, ER alpha, ER beta and CYP1A1 were also conducted. A 3-9-fold increase in intra-individual expression of CYP1A1 in ectopic (ovarian) endometrium compared with eutopic tissue was observed; immunohistochemical analyses pointed to CYP1A1 being localized to the glandular epithelium. This intra-individual expression profile was not observed for CYP1B1 or BCL-2. However, a 5-53-fold intra-individual increase in gamma-SYN expression was also demonstrated in six of nine tissue sets (a further two showed an increase that was not considered significant) when comparing ectopic to eutopic endometrium; gamma-syn positivity was associated with endothelial cells. An elevation in ER beta was also noted when comparing ectopic to eutopic endometrium; with regard to ER alpha, this was inconsistent. These results suggest an up-regulation of dioxin-inducible CYP1A1 and gamma-SYN occurs in endometriosis. Whether gamma-syn may be a novel diagnostic marker for endometriosis remains to be ascertained.

Gamma-synuclein and the progression of cancer.

The synucleins are a small, soluble, highly conserved group of neuronal proteins that have been implicated in both neurodegenerative diseases and cancer. The synuclein family consists of alpha-, beta-, and gamma-synucleins (gamma-syn). They are a natively unfolded group of proteins that share sequence homologies and structural properties. So far, the biological functions of the synucleins are still unclear, but their involvement in neurodegenerative diseases and cancer may provide insights into the pathological processes that result from these two groups of debilitating diseases, and present the possibility to use them as potential targets for early diagnosis and treatment. Recently, elevated levels of gamma-syn proteins have been detected in various types of cancer, especially in advanced stages of the disease. Furthermore, studies to date indicate that overexpression of gamma-syn compromises normal mitotic checkpoint controls, resulting in multinucleation as well as faster cell growth. Gamma-syn has also been shown to promote invasion and metastasis in in vitro assays as well as in animal models. Overexpression of gamma-syn also interferes with drug-induced apoptotic responses. These observations raise questions about the involvement of gamma-syn in the process of tumorigenesis and metastasis, and efforts have already been made to use gamma-syn as a marker for assessing breast cancer progression. This review will discuss the involvement of gamma-syn in cancer progression, metastasis and its potential as a marker.

Quantifiable mRNA transcripts for tamoxifen-metabolising enzymes in human endometrium.

Tamoxifen has been used in the management of receptor-positive breast cancer for >20 years. Usage confers an elevated risk of developing endometrial carcinoma. Its mechanism of carcinogenicity remains unresolved with controversy as to whether or not this is mediated through a genotoxic mechanism. Usage is not only associated with an elevated occurrence of endometrioid endometrial carcinoma, but also type 2 and mixed epithelial-stromal tumours (MESTs) that have a poorer prognosis. Following hysterectomy, endometrial tissues (n=18) classified as benign (n=6), non-tamoxifen-associated carcinoma (n=6) and tamoxifen-associated carcinoma (n=6) were obtained; quantitative gene expression was performed. Employing real-time RT-PCR, the relative gene expressions of phase I/II metabolic enzymes CYP1A2, CYP1B1 and CYP3A4, cathechol-O-methyltransferase (COMT) and SULT2A1 were ascertained. Measurable mRNA transcripts, especially for those genes associated with tamoxifen bioactivation, were quantifiable in all the tissues examined. Whether this is evidence that generation of genotoxic tamoxifen metabolites may occur in human endometrial tissue remains to be ascertained.

The multiple applications of tamoxifen: an example pointing to SERM modulation being the aspirin of the 21st century.

Tamoxifen is a selective oestrogen receptor modulator (SERM) with an established role in the treatment and chemoprevention of hormone-related breast cancer. It is also cardioprotective and increases bone mineral density. However, due to pleiotrophic ligand-receptor properties, its role in a variety of seemingly unrelated disorders, including multiple sclerosis, Parkinson's disease, Alzheimer's disease, systemic lupus erythematosus and urological cancers, has been investigated in many studies. The non-patented drug tamoxifen confers a significant advantage over newer drugs in being inexpensive and well-tolerated with a known side-effect profile. This review highlights the interaction of tamoxifen on oestrogen receptors (ERs) and assesses whether this agent continues to have future applications in a variety of clinical settings, both as a therapy in early and established disease and usage as a prophylactic in those at risk of debilitating conditions. Indeed, it may have as-yet-unknown benefit(s) in a variety of conditions, both as a prophylactic in those at high-risk and also as in novel therapeutic strategies in established disease. Future clinical studies may seek to establish the exact future role and efficacy for SERMs both in men and women. Perhaps a multi-functional SERM such as tamoxifen may be the aspirin of the 21(st) century.

Tamoxifen: important considerations of a multi-functional compound with organ-specific properties.

Tamoxifen remains a frontline treatment for hormone-responsive breast cancer despite its use being associated with a 2-7-fold elevated risk of developing endometrial carcinoma. Several groups have investigated whether tamoxifen induces DNA-damaging (genotoxic) versus non-genotoxic mechanisms. Some studies point to the presence of tamoxifen-DNA adducts while others suggest otherwise. In many of these studies, the histological sub-type has not been considered; as type 1 carcinomas are associated with PTEN and KRAS2 mutations whereas type 2 carcinomas exhibit TP53 and ERBB-2 mutations, the absence of this information makes comparisons between such independent investigations difficult. An examination of the sub-types of endometrial carcinoma points to histological and mechanistic distinctions between sporadic and tamoxifen-associated disease; this could suggest differing aetiologies. On this basis, we propose a dual mechanism of action highlighted by the different patterns of endometrial carcinoma sub-types. Tamoxifen may initially be pro-oestrogenic in the endometrium giving rise to elevated type 1 endometrioid carcinoma occurrence whereas after long-term use, there is an increase of type 2 disease or malignant mixed mullerian tumours associated with a hormone-independent mechanism of action. Despite these associated risk factors, and the introduction of new selective oestrogen receptor modulators (SERMs), we suggest that the organ-specific pleiotrophic effects of tamoxifen mean that this effective therapeutic agent for breast cancer will continue to have significant usage. The focus of future research should concentrate on the different aetiologies of tamoxifen-associated endometrial carcinomas while efforts continue to develop future SERMs.

Incarcerated retroverted uterus in the third trimester complicated by postpartum pulmonary embolism.

BACKGROUND: Incarceration of the retroverted gravid uterus is an extremely rare diagnosis in the third trimester that carries significant maternal operative and postoperative morbidity. CASE: The condition was diagnosed clinically in the third trimester of a primigravida. Despite the preoperative diagnosis, operative delivery proved difficult, requiring delivery of the fetus through the posterior wall of the uterus. Thirty-six hours after delivery the patient had a pulmonary embolus. CONCLUSION: This case report discusses the diagnosis and management of the condition incarcerated, retroverted uterus with postpartum pulmonary embolism. Prophylactic low molecular weight heparin in the antepartum period should be a considered for possible venous stasis due to the anatomy of the uterus.

Derivation of a subtype-specific biochemical signature of endometrial carcinoma using synchrotron-based Fourier-transform infrared microspectroscopy.

Endometrial carcinoma consists of endometrioid (type I) and serous papillary (SP; type II) subtypes; a rarer form is malignant mixed müllerian tumours (MMMT; type II/mixed). We set out to determine whether one might be able to biochemically signature these subtypes using Fourier-transform infrared (FTIR) microspectroscopy and distinguish non-tamoxifen associated from tamoxifen-associated cases. Paraffin-embedded blocks were obtained from non-tamoxifen associated cases reported as endometrioid (n=7), SP (n=4) or MMMT (n=4). From tamoxifen-associated cases, endometrioid (n=1), SP (n=3) and MMMT (n=4) blocks were retrieved; benign tissues (n=3) were also analysed. Exploiting synchrotron-based radiation, sections (10-microm thick) on BaF(2) windows were interrogated through a 10 microm x 10 microm aperture. Point spectra were derived from >or=10 locations in each of six glandular elements per tissue; a further 20 stromal spectra were obtained. Following normalisation to Amide I, average spectra (1800-900 cm(-1)) per gland or stroma were analysed for variance using principal component analysis (PCA) and linear discriminant analysis (LDA). In scores plots, segregation of spectra from different subtypes or benign tissues was noted and it proved possible to distinguish tamoxifen-associated cases. In the PCA-LDA loadings plots, the wavenumbers that highlighted variance for benign or endometrioid carcinoma tissues were in the protein region (1800-1480 cm(-1)) whereas those contributing most to SP or MMMT segregation were primarily in the DNA/RNA region (1425-900 cm(-1)) of the vibrational spectrum. Our results suggest that the application of FTIR microspectroscopy is a powerful new approach in disease diagnosis and characterisation.

FTIR Microspectroscopy Coupled with Two-Class Discrimination Segregates Markers Responsible for Inter- and Intra-Category Variance in Exfoliative Cervical Cytology.

Infrared (IR) absorbance of cellular biomolecules generates a vibrational spectrum, which can be exploited as a "biochemical fingerprint" of a particular cell type. Biomolecules absorb in the mid-IR (2-20 mum) and Fourier-transform infrared (FTIR) microspectroscopy applied to discriminate different cell types (exfoliative cervical cytology collected into buffered fixative solution) was evaluated. This consisted of cervical cytology free of atypia (i.e. normal; n = 60), specimens categorised as containing low-grade changes (i.e. CIN1 or LSIL; n = 60) and a further cohort designated as high-grade (CIN2/3 or HSIL; n = 60). IR spectral analysis was coupled with principal component analysis (PCA), with or without subsequent linear discriminant analysis (LDA), to determine if normal versus low-grade versus high-grade exfoliative cytology could be segregated. With increasing severity of atypia, decreases in absorbance intensity were observable throughout the 1,500 cm(-1) to 1,100 cm(-1) spectral region; this included proteins (1,460 cm(-1)), glycoproteins (1,380 cm(-1)), amide III (1,260 cm(-1)), asymmetric (nu(as)) PO(2) (-) (1,225 cm(-1)) and carbohydrates (1,155 cm(-1)). In contrast, symmetric (nu(s)) PO(2) (-) (1,080 cm(-1)) appeared to have an elevated intensity in high-grade cytology. Inter-category variance was associated with protein and DNA conformational changes whereas glycogen status strongly influenced intra-category. Multivariate data reduction of IR spectra using PCA with LDA maximises inter-category variance whilst reducing the influence of intra-class variation towards an objective approach to class cervical cytology based on a biochemical profile.

ATR microspectroscopy with multivariate analysis segregates grades of exfoliative cervical cytology.

Although cervical cancer screening in the UK has led to reductions in the incidence of invasive disease, this programme remains flawed. We set out to examine the potential of infrared (IR) microspectroscopy to allow the profiling of cellular biochemical constituents associated with disease progression. Attenuated total reflection-Fourier Transform IR (ATR) microspectroscopy was employed to interrogate spectral differences between samples of exfoliative cervical cytology collected into liquid based cytology (LBC). These were histologically characterised as normal (n = 5), low-grade (n = 5), high-grade (n = 5) or severe dyskaryosis (? carcinoma) (n = 5). Examination of resultant spectra was coupled with principal component analysis (PCA) and subsequent linear discriminant analysis (LDA). The interrogation of LBC samples using ATR microspectroscopy with PCA-LDA facilitated the discrimination of different categories of exfoliative cytology and allowed the identification of potential biomarkers of abnormality; these occurred prominently in the IR spectral region 1200 cm(-1) - 950 cm(-1) consisting of carbohydrates, phosphate, and glycogen. Shifts in the centroids of amide I (approximately 1650 cm(-1)) and II (approximately 1530 cm(-1)) absorbance bands, indicating conformational changes to the secondary structure of intracellular proteins and associated with increasing disease progression, were also noted. This work demonstrates the potential of ATR microspectroscopy coupled with multivariate analysis to be an objective alternative to routine cytology.