Attenuated Total Reflection Fourier-Transform Infrared Spectral Discrimination in Human Tissue of Oesophageal Transformation to Adenocarcinoma.

This study presents ATR-FTIR (attenuated total reflectance Fourier-transform infrared) spectral analysis of ex vivo oesophageal tissue including all classifications to oesophageal adenocarcinoma (OAC). The article adds further validation to previous human tissue studies identifying the potential for ATR-FTIR spectroscopy in differentiating among all classes of oesophageal transformation to OAC. Tissue spectral analysis used principal component analysis quadratic discriminant analysis (PCA-QDA), successive projection algorithm quadratic discriminant analysis (SPA-QDA), and genetic algorithm quadratic discriminant analysis (GA-QDA) algorithms for variable selection and classification. The variables selected by SPA-QDA and GA-QDA discriminated tissue samples from Barrett's oesophagus (BO) to OAC with 100% accuracy on the basis of unique spectral "fingerprints" of their biochemical composition. Accuracy test results including sensitivity and specificity were determined. The best results were obtained with PCA-QDA, where tissues ranging from normal to OAC were correctly classified with 90.9% overall accuracy (71.4-100% sensitivity and 89.5-100% specificity), including the discrimination between normal and inflammatory tissue, which failed in SPA-QDA and GA-QDA. All the models revealed excellent results for distinguishing among BO, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and OAC tissues (100% sensitivities and specificities). This study highlights the need for further work identifying potential biochemical markers using ATR-FTIR in tissue that could be utilised as an adjunct to histopathological diagnosis for early detection of neoplastic changes in susceptible epithelium.

Spectrochemical approach combined with symptoms data to diagnose fibromyalgia through paper spray ionization mass spectrometry (PSI-MS) and multivariate classification.

This study performs a chemical investigation of blood plasma samples from patients with and without fibromyalgia, combined with some of the symptoms and their levels of intensity used in the diagnosis of this disease. The symptoms evaluated were: visual analogue pain scale (VAS); fibromyalgia impact questionnaire (FIQ); Hamilton anxiety rating scale (HAM); Tampa Scale for Kinesiophobia (TAMPA); quality of life Questionnaire-physical and mental health (QL); and Pain Catastrophizing Scale (CAT). Plasma samples were analyzed by paper spray ionization mass spectrometry (PSI-MS). Spectral data were organized into datasets and related to each of the symptoms measured. The datasets were submitted to multivariate classification using supervised models such as principal component analysis with linear discriminant analysis (PCA-LDA), successive projections algorithm with linear discriminant analysis (SPA-LDA), genetic algorithm with linear discriminant analysis (GA-LDA) and their versions with quadratic discriminant analysis (PCA/SPA/GA-QDA) and support vector machines (PCA/SPA/GA-SVM). These algorithm combinations were performed aiming the best class separation. Good discrimination between the controls and fibromyalgia samples were observed using PCA-LDA, where the spectral data associated with the CAT symptom achieved 100% classification sensitivity, and associated with the VAS symptom achieved 100% classification specificity, with both symptoms at the moderate level of intensity. The spectral variable at 579 m/z was found to be substantially significant for classification according to the PCA loadings. According to the human metabolites database, this variable can be associated with a LysoPC compound, which comprises a class of metabolites already evidenced in other studies for fibromyalgia diagnosis. This study proposed an investigation of spectral data combined with clinical data to compare the classification ability of different datasets. The good classification results obtained confirm this technique is as a good analytical tool for the detection of fibromyalgia, and provides theoretical support for other studies about fibromyalgia diagnosis.

Automated Computational Detection of Disease Activity in ANCA-Associated Glomerulonephritis Using Raman Spectroscopy: A Pilot Study.

Biospectroscopy offers the ability to simultaneously identify key biochemical changes in tissue associated with a given pathological state to facilitate biomarker extraction and automated detection of key lesions. Herein, we evaluated the application of machine learning in conjunction with Raman spectroscopy as an innovative low-cost technique for the automated computational detection of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (AAGN). Consecutive patients with active AAGN and those in disease remission were recruited from a single UK centre. In those with active disease, renal biopsy samples were collected together with a paired urine sample. Urine samples were collected immediately prior to biopsy. Amongst those in remission at the time of recruitment, archived renal tissue samples representative of biopsies taken during an active disease period were obtained. In total, twenty-eight tissue samples were included in the analysis. Following supervised classification according to recorded histological data, spectral data from unstained tissue samples were able to discriminate disease activity with a high degree of accuracy on blind predictive modelling: F-score 95% for >25% interstitial fibrosis and tubular atrophy (sensitivity 100%, specificity 90%, area under ROC 0.98), 100% for necrotising glomerular lesions (sensitivity 100%, specificity 100%, area under ROC 1) and 100% for interstitial infiltrate (sensitivity 100%, specificity 100%, area under ROC 0.97). Corresponding spectrochemical changes in paired urine samples were limited. Future larger study is required, inclusive of assigned variables according to novel non-invasive biomarkers as well as the application of forward feature extraction algorithms to predict clinical outcomes based on spectral features.

Multivariate assessment for predicting antioxidant activity from clove and pomegranate extracts by MCR-ALS and PLS models combined to IR spectroscopy.

This study evaluated the feasibility of infrared (MIR/NIR) spectroscopy coupled to chemometrics as an alternative method for determining the antioxidant activity (AA%) of pomegranate (Punica granatum) and clove (Syzygium aromaticum) alcoholic extracts versus the conventional DPPH method. Multivariate curve resolution with alternating least squares (MCR-ALS) and Partial least squares (PLS) regression were efficient to predict the AA%, thus providing good accuracy and low residuals compared to the standard method. The MCR-ALS combined with NIR data stood out among the other models with R2 ≥ 0.962 and RMSEP ≤ 3.38 %; furthermore, this technique presents the great feature of recovering the pure spectral profile of the analytes and identifying interferents in the sample. The application of chemometrics tools to predict the antioxidant activity of natural extracts resulted in a greener, low-cost and efficient process for the food industry.

Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia.

Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.

Raman spectroscopy of blood and urine liquid biopsies for ovarian cancer diagnosis: identification of chemotherapy effects.

Blood plasma and serum Raman spectroscopy for ovarian cancer diagnosis has been applied in pilot studies, with promising results. Herein, a comparative analysis of these biofluids, with a novel assessment of urine, was conducted by Raman spectroscopy application in a large patient cohort. Spectra were obtained through samples measurements from 116 ovarian cancer patients and 307 controls. Principal component analysis identified significant spectral differences between cancers without previous treatment (n = 71) and following neo-adjuvant chemotherapy (NACT), (n = 45). Application of five classification algorithms achieved up to 73% sensitivity for plasma, high specificities and accuracies for both blood biofluids, and lower performance for urine. A drop in sensitivities for the NACT group in plasma and serum, with an opposite trend in urine, suggest that Raman spectroscopy could identify chemotherapy-related changes. This study confirms that biofluids' Raman spectroscopy can contribute in ovarian cancer's diagnostic work-up and demonstrates its potential in monitoring treatment response.

Spectrochemical analysis of liquid biopsy harnessed to multivariate analysis towards breast cancer screening.

Mortality due to breast cancer could be reduced via screening programs where preliminary clinical tests employed in an asymptomatic well-population with the objective of identifying cancer biomarkers could allow earlier referral of women with altered results for deeper clinical analysis and treatment. The introduction of well-population screening using new and less-invasive technologies as a strategy for earlier detection of breast cancer is thus highly desirable. Herein, spectrochemical analyses harnessed to multivariate classification techniques are used as a bio-analytical tool for a Breast Cancer Screening Program using liquid biopsy in the form of blood plasma samples collected from 476 patients recruited over a 2-year period. This methodology is based on acquiring and analysing the spectrochemical fingerprint of plasma samples by attenuated total reflection Fourier-transform infrared spectroscopy; derived spectra reflect intrinsic biochemical composition, generating information on nucleic acids, carbohydrates, lipids and proteins. Excellent results in terms of sensitivity (94%) and specificity (91%) were obtained using this method in comparison with traditional mammography (88-93% and 85-94%, respectively). Additional advantages such as better disease prognosis thus allowing a more effective treatment, lower associated morbidity, fewer false-positive and false-negative results, lower-cost, and higher analytical frequency make this method attractive for translation to the clinical setting.

Spectrochemical analysis in blood plasma combined with subsequent chemometrics for fibromyalgia detection.

Fibromyalgia is a rheumatologic condition characterized by multiple and chronic body pain, and other typical symptoms such as intense fatigue, anxiety and depression. It is a very complex disease where treatment is often made by non-medicated alternatives in order to alleviate symptoms and improve the patient's quality of life. Herein, we propose a method to detect patients with fibromyalgia (n = 252, 126 controls and 126 patients with fibromyalgia) through the analysis of their blood plasma using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques, hence, providing a low-cost, fast and accurate diagnostic approach. Different chemometric algorithms were tested to classify the spectral data; genetic algorithm with linear discriminant analysis (GA-LDA) achieved the best diagnostic results with a sensitivity of 89.5% in an external test set. The GA-LDA model identified 24 spectral wavenumbers responsible for class separation; amongst these, the Amide II (1,545 cm-1) and proteins (1,425 cm-1) were identified to be discriminant features. These results reinforce the potential of ATR-FTIR spectroscopy with multivariate analysis as a new tool to screen and detect patients with fibromyalgia in a fast, low-cost, non-destructive and minimally invasive fashion.

Paper Spray Ionization Mass Spectrometry as a Potential Tool for Early Diagnosis of Cervical Cancer.

Squamous intraepithelial lesion is an abnormal growth of epithelial cells on the surface of the cervix that may lead to cervical cancer. Analytical protocols for the determination of squamous intraepithelial lesions are in high demand, since cervical cancer is the fourth most diagnosed cancer among women in the world. Here, paper spray ionization mass spectrometry (PSI-MS) is used to distinguish between healthy (negative for intraepithelial lesion or malignancy) and diseased (high-grade squamous intraepithelial lesion) blood plasmas. A total of 86 blood samples of different women (49 healthy samples, 37 diseased samples) were collected, and the plasmas were prepared. Then, 10 µL of each plasma sample was deposited onto triangular papers for PSI-MS analysis. No additional step of sample preparation was necessary. The interval-successive projection algorithm linear discriminant analysis (iSPA-LDA) was applied to the PSI mass spectra, showing six ions (mostly phospholipids) that were predictive of healthy and diseased plasmas. Values of 77% accuracy, 86% sensitivity, 80% positive predictive value (PPV), and 75% negative predictive value (NPV) were achieved. This study provides evidence that PSI-MS may potentially be used as a fast and simple analytical technique for the early diagnosis of cervical cancer.

Detecting Endometrial Cancer by Blood Spectroscopy: A Diagnostic Cross-Sectional Study.

Endometrial cancer is the sixth most common cancer in women, with a rising incidence worldwide. Current approaches for the diagnosis and screening of endometrial cancer are invasive, expensive or of moderate diagnostic accuracy, limiting their clinical utility. There is a need for cost-effective and minimally invasive approaches to facilitate the early detection and timely management of endometrial cancer. We analysed blood plasma samples in a cross-sectional diagnostic accuracy study of women with endometrial cancer (n = 342), its precursor lesion atypical hyperplasia (n = 68) and healthy controls (n = 242, total n = 652) using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning algorithms. We show that blood-based infrared spectroscopy has the potential to detect endometrial cancer with 87% sensitivity and 78% specificity. Its accuracy is highest for Type I endometrial cancer, the most common subtype, and for atypical hyperplasia, with sensitivities of 91% and 100%, and specificities of 81% and 88%, respectively. Our large-cohort study shows that a simple blood test could enable the early detection of endometrial cancer of all stages in symptomatic women and provide the basis of a screening tool in high-risk groups. Such a test has the potential not only to differentially diagnose endometrial cancer but also to detect its precursor lesion atypical hyperplasia-the early recognition of which may allow fertility sparing management and cancer prevention.

Establishing spectrochemical changes in the natural history of oesophageal adenocarcinoma from tissue Raman mapping analysis.

Raman spectroscopy is a fast and sensitive technique able to identify molecular changes in biological specimens. Herein, we report on three cases where Raman microspectroscopy was used to distinguish normal vs. oesophageal adenocarcinoma (OAC) (case 1) and Barrett's oesophagus vs. OAC (cases 2 and 3) in a non-destructive and highly accurate fashion. Normal and OAC tissues were discriminated using principal component analysis plus linear discriminant analysis (PCA-LDA) with 97% accuracy (94% sensitivity and 100% specificity) (case 1); Barrett's oesophagus vs. OAC tissues were discriminated with accuracies ranging from 98 to 100% (97-100% sensitivity and 100% specificity). Spectral markers responsible for class differentiation were obtained through the difference-between-mean spectrum for each group and the PCA loadings, where C-O-C skeletal mode in ß-glucose (900 cm-1), lipids (967 cm-1), phosphodioxy (1296 cm-1), deoxyribose (1456 cm-1) and collagen (1445, 1665 cm-1) were associated with normal and OAC tissue differences. Phenylalanine (1003 cm-1), proline/collagen (1066, 1445 cm-1), phospholipids (1130 cm-1), CH2 angular deformation (1295 cm-1), disaccharides (1462 cm-1) and proteins (amide I, 1672/5 cm-1) were associated with Barrett's oesophagus and OAC tissue differences. These findings show the potential of using Raman microspectroscopy imaging for fast and accurate diagnoses of oesophageal pathologies and establishing subtle molecular changes predisposing to adenocarcinoma in a clinical setting. Graphical abstract Graphical abstract demonstrating how oesophageal tissue is processed through Raman mapping analysis in order to detect spectral differences between stages of oesophageal transformation to adenocarcinoma.

Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model.

Introduction: In order for brain tumours to be successfully treated, maximal resection is beneficial. A method to detect infiltrative tumour edges intraoperatively, improving on current methods would be clinically useful. Vibrational spectroscopy offers the potential to provide a handheld, reagent-free method for tumour detection.Purpose: This study was designed to determine the ability of both Raman and Fourier-transform infrared (FTIR) spectroscopy towards differentiating between normal brain tissue, glioma or meningioma.Method: Unfixed brain tissue, which had previously only been frozen, comprising normal, glioma or meningioma tissue was placed onto calcium fluoride slides for analysis using Raman and attenuated total reflection (ATR)-FTIR spectroscopy. Matched haematoxylin and eosin slides were used to confirm tumour areas. Analyses were then conducted to generate a classification model.Results: This study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to discriminate tumour from non-tumour fresh frozen brain tissue with 94% and 97.2% of cases correctly classified, with sensitivities of 98.8% and 100%, respectively. This decreases when spectroscopy is used to determine tumour type.Conclusion: The study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to detect tumour tissue from non-tumour brain tissue with a high degree of accuracy. This demonstrates the ability of spectroscopy when targeted for a cancer diagnosis. However, further improvement would be required for a classification model to determine tumour type using this technology, in order to make this tool clinically viable.

Raman spectral discrimination in human liquid biopsies of oesophageal transformation to adenocarcinoma.

The aim of this study was to determine whether Raman spectroscopy combined with chemometric analysis can be applied to interrogate biofluids (plasma, serum, saliva and urine) towards detecting oesophageal stages through to oesophageal adenocarcinoma [normal/squamous epithelium, inflammatory, Barrett's, low-grade dysplasia, high-grade dysplasia and oesophageal adenocarcinoma (OAC)]. The chemometric analysis of the spectral data was performed using principal component analysis, successive projections algorithm or genetic algorithm (GA) followed by quadratic discriminant analysis (QDA). The genetic algorithm quadratic discriminant analysis (GA-QDA) model using a few selected wavenumbers for saliva and urine samples achieved 100% classification for all classes. For plasma and serum, the GA-QDA model achieved excellent accuracy in all oesophageal stages (>90%). The main GA-QDA features responsible for sample discrimination were: 1012 cm-1 (C─O stretching of ribose), 1336 cm-1 (Amide III and CH2 wagging vibrations from glycine backbone), 1450 cm-1 (methylene deformation) and 1660 cm-1 (Amide I). The results of this study are promising and support the concept that Raman on biofluids may become a useful and objective diagnostic tool to identify oesophageal disease stages from squamous epithelium to OAC.

Attenuated total reflection Fourier-transform infrared spectral discrimination in human bodily fluids of oesophageal transformation to adenocarcinoma.

Diagnostic tools for the detection of early-stage oesophageal adenocarcinoma (OAC) are urgently needed. Our aim was to develop an accurate and inexpensive method using biofluids (plasma, serum, saliva or urine) for detecting oesophageal stages through to OAC (squamous; inflammatory; Barrett's; low-grade dysplasia; high-grade dysplasia; OAC) using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. ATR-FTIR spectroscopy coupled with variable selection methods, with successive projections or genetic algorithms (GA) combined with quadratic discriminant analysis (QDA) were employed to identify spectral biomarkers in biofluids for accurate diagnosis in a hospital setting of different stages through to OAC. Quality metrics (Accuracy, Sensitivity, Specificity and F-score) and biomarkers of disease were computed for each model. For plasma, GA-QDA models using 15 wavenumbers achieved 100% classification for four classes. For saliva, PCA-QDA models achieved 100% for the inflammatory stage and high-quality metrics for other classes. For serum, GA-QDA models achieved 100% performance for the OAC stage using 13 wavenumbers. For urine, PCA-QDA models achieved 100% performance for all classes. Selected wavenumbers using a Student's t-test (95% confidence interval) identified a differentiation of the stages on each biofluid: plasma (929 cm-1 to 1431 cm-1, associated with DNA/RNA and proteins); saliva (1000 cm-1 to 1150 cm-1, associated with DNA/RNA region); serum (1435 cm-1 to 1573 cm-1, associated with methyl groups of proteins and Amide II absorption); and, urine (1681 cm-1 to 1777 cm-1, associated with a high frequency vibration of an antiparallel ß-sheet of Amide I and stretching vibration of lipids). Our methods have demonstrated excellent efficacy for a rapid, cost-effective method of diagnosis for specific stages to OAC. These findings suggest a potential diagnostic tool for oesophageal cancer and could be translated into clinical practice.

Potential of mid-infrared spectroscopy as a non-invasive diagnostic test in urine for endometrial or ovarian cancer.

The current lack of an accurate, cost-effective and non-invasive test that would allow for screening and diagnosis of gynaecological carcinomas, such as endometrial and ovarian cancer, signals the necessity for alternative approaches. The potential of spectroscopic techniques in disease investigation and diagnosis has been previously demonstrated. Here, we used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse urine samples from women with endometrial (n = 10) and ovarian cancer (n = 10), as well as from healthy individuals (n = 10). After applying multivariate analysis and classification algorithms, biomarkers of disease were pointed out and high levels of accuracy were achieved for both endometrial (95% sensitivity, 100% specificity; accuracy: 95%) and ovarian cancer (100% sensitivity, 96.3% specificity; accuracy 100%). The efficacy of this approach, in combination with the non-invasive method for urine collection, suggest a potential diagnostic tool for endometrial and ovarian cancers.

Synchrotron- and focal plane array-based Fourier-transform infrared spectroscopy differentiates the basalis and functionalis epithelial endometrial regions and identifies putative stem cell regions of human endometrial glands.

The cyclical process of regeneration of the endometrium suggests that it may contain a cell population that can provide daughter cells with high proliferative potential. These cell lineages are clinically significant as they may represent clonogenic cells that may also be involved in tumourigenesis as well as endometriotic lesion development. To determine whether the putative stem cell location within human uterine tissue can be derived using vibrational spectroscopy techniques, normal endometrial tissue was interrogated by two spectroscopic techniques. Paraffin-embedded uterine tissues containing endometrial glands were sectioned to 10-µm-thick parallel tissue sections and were floated onto BaF2 slides for synchrotron radiation-based Fourier-transform infrared (SR-FTIR) microspectroscopy and globar focal plane array-based FTIR spectroscopy. Different spectral characteristics were identified depending on the location of the glands examined. The resulting infrared spectra were subjected to multivariate analysis to determine associated biophysical differences along the length of longitudinal and crosscut gland sections. Comparison of the epithelial cellular layer of transverse gland sections revealed alterations indicating the presence of putative transient-amplifying-like cells in the basalis and mitotic cells in the functionalis. SR-FTIR microspectroscopy of the base of the endometrial glands identified the location where putative stem cells may reside at the same time pointing towards νsPO2- in DNA and RNA, nucleic acids and amide I and II vibrations as major discriminating factors. This study supports the view that vibration spectroscopy technologies are a powerful adjunct to our understanding of the stem cell biology of endometrial tissue. Graphical abstract ᅟ.

Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study.

Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort has been dedicated to combating this cancer, in particular by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM, n = 42) and squamous intraepithelial lesion (SIL, n = 34). The crude lipid extract was directly analyzed with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM), linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA results, achieving sensitivity and specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions.

Aluminium foil as an alternative substrate for the spectroscopic interrogation of endometrial cancer.

Biospectroscopy has the potential to investigate and characterize biological samples and could, therefore, be utilized to diagnose various diseases in a clinical environment. An important consideration in spectrochemical studies is the cost-effectiveness of the substrate used to support the sample, as high expense would limit their translation into clinic. In this paper, the performance of low-cost aluminium (Al) foil substrates was compared with the commonly used low-emissivity (low-E) slides. Attenuated total reflection-Fourier transform infrared spectroscopy was used to analyse blood plasma and serum samples from women with endometrial cancer and healthy controls. The 2 populations were differentiated using principal component analysis with support vector machines with 100% sensitivity in plasma samples (endometrial cancer = 70; healthy controls = 15) using both Al foil and low-E slides as substrates. The same sensitivity results (100%) were achieved for serum samples (endometrial cancer = 60; healthy controls = 15). Specificity was found higher using Al foil (90%) in comparison to low-E slides (85%) and lower using Al foil (70%) in comparison to low-E slides in serum samples. The establishment of Al foil as low-cost and highly performing substrate would pave the way for large-scale, multicentre studies and potentially for routine clinical use.

MIR-biospectroscopy coupled with chemometrics in cancer studies.

This review focuses on chemometric techniques applied in MIR-biospectroscopy for cancer diagnosis and analysis over the last ten years of research. Experimental applications of chemometrics coupled with biospectroscopy are discussed throughout this work. The advantages and drawbacks of this association are also highlighted. Chemometric algorithms are evidenced as a powerful tool for cancer diagnosis, classification, and in different matrices. In fact, it is shown how chemometrics can be implemented along all different types of cancer analyses.

Imaging cervical cytology with scanning near-field optical microscopy (SNOM) coupled with an IR-FEL.

Cervical cancer remains a major cause of morbidity and mortality among women, especially in the developing world. Increased synthesis of proteins, lipids and nucleic acids is a pre-condition for the rapid proliferation of cancer cells. We show that scanning near-field optical microscopy, in combination with an infrared free electron laser (SNOM-IR-FEL), is able to distinguish between normal and squamous low-grade and high-grade dyskaryosis, and between normal and mixed squamous/glandular pre-invasive and adenocarcinoma cervical lesions, at designated wavelengths associated with DNA, Amide I/II and lipids. These findings evidence the promise of the SNOM-IR-FEL technique in obtaining chemical information relevant to the detection of cervical cell abnormalities and cancer diagnosis at spatial resolutions below the diffraction limit (≥0.2 µm). We compare these results with analyses following attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy; although this latter approach has been demonstrated to detect underlying cervical atypia missed by conventional cytology, it is limited by a spatial resolution of ~3 µm to 30 µm due to the optical diffraction limit.