Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females.

One of the most important steps in preventing arboviruses is entomological surveillance. The main entomological surveillance action is to detect vector foci in the shortest possible stages. In this work, near and medium infrared spectra collected from female Aedes aegypti mosquitoes recently infected and not infected with dengue were used in order to build chemometric models capable of differentiating the spectra of each class. For this, computational algorithms such as Successive Projection Algorithm (SPA) and Genetic Algorithm (GA) were used together with Linear Discriminant Analysis (LDA). The constructed models were evaluated with sensitivity and specificity calculations. It was observed that models based on near infrared (NIR) spectra have better classification results when compared to mid infrared (MIR) spectra, as well as models based on GA present better results when compared to those based on SPA. Thus, NIR-GA-LDA obtained the best results, reaching 100.00 % for sensitivity and specificity. NIR spectroscopy is 18 times faster and 116 times cheaper than RT-qPCR. The findings reported in this study may have important applications in the field of entomological surveillance, prevention and control of dengue vectors. In the future, mosquito traps equipped with portable NIR instruments capable of detecting infected mosquitoes may be used, in order to enable an action plan to prevent future outbreaks of the disease.

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.

Near-infrared spectroscopy of blood plasma with chemometrics towards HIV discrimination during pregnancy.

Prevention of mother-to-child transmission programs have been one of the hallmarks of success in the fight against HIV/AIDS. In Brazil, access to antiretroviral therapy (ART) during pregnancy has increased, leading to a reduction in new infections among children. Currently, lifelong ART is available to all pregnant, however yet challenges remain in eliminating mother-to-child transmission. In this paper, we focus on the role of near-infrared (NIR) spectroscopy to analyse blood plasma samples of pregnant women with HIV infection to differentiate pregnant women without HIV infection. Seventy-seven samples (39 HIV-infected patient and 38 healthy control samples) were analysed. Multivariate classification of resultant NIR spectra facilitated diagnostic segregation of both sample categories in a fast and non-destructive fashion, generating good accuracy, sensitivity and specificity. This method is simple and low-cost, and can be easily adapted to point-of-care screening, which can be essential to monitor pregnancy risks in remote locations or in the developing world. Therefore, it opens a new perspective to investigate vertical transmission (VT). The approach described here, can be useful for the identification and exploration of VT under various pathophysiological conditions of maternal HIV. These findings demonstrate, for the first time, the potential of NIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost HIV detection.

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.

Distinguishing active from quiescent disease in ANCA-associated vasculitis using attenuated total reflection Fourier-transform infrared spectroscopy.

The current lack of a reliable biomarker of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA) associated vasculitis poses a significant clinical unmet need when determining relapsing or persisting disease. In this study, we demonstrate for the first time that attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy offers a novel and functional candidate biomarker, distinguishing active from quiescent disease with a high degree of accuracy. Paired blood and urine samples were collected within a single UK centre from patients with active disease, disease remission, disease controls and healthy controls. Three key biofluids were evaluated; plasma, serum and urine, with subsequent chemometric analysis and blind predictive model validation. Spectrochemical interrogation proved plasma to be the most conducive biofluid, with excellent separation between the two categories on PC2 direction (AUC 0.901) and 100% sensitivity (F-score 92.3%) for disease remission and 85.7% specificity (F-score 92.3%) for active disease on blind predictive modelling. This was independent of organ system involvement and current ANCA status, with similar findings observed on comparative analysis following successful remission-induction therapy (AUC > 0.9, 100% sensitivity for disease remission, F-score 75%). This promising technique is clinically translatable and warrants future larger study with longitudinal data, potentially aiding earlier intervention and individualisation of treatment.

ATR-FTIR spectroscopy in blood plasma combined with multivariate analysis to detect HIV infection in pregnant women.

The primary concern for HIV-infected pregnant women is the vertical transmission that can occur during pregnancy, in the intrauterine period, during labour or even breastfeeding. The risk of vertical transmission can be reduced by early diagnosis. Therefore, it is necessary to develop new methods to detect this virus in a quick and low-cost fashion, as colorimetric assays for HIV detection tend to be laborious and costly. Herein, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with multivariate analysis was employed to distinguish HIV-infected patients from healthy uninfected controls in a total of 120 blood plasma samples. The best sensitivity (83%) and specificity (92%) values were obtained using the genetic algorithm with linear discriminant analysis (GA-LDA). These good classification results in addition to the potential for high analytical frequency, the low cost and reagent-free nature of this method demonstrate its potential as an alternative tool for HIV screening during pregnancy.

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.

Blood-based near-infrared spectroscopy for the rapid low-cost detection of Alzheimer's disease.

Alzheimer's disease (AD) is currently under-diagnosed and is predicted to affect a great number of people in the future, due to the unrestrained aging of the population. An accurate diagnosis of AD at an early stage, prior to (severe) symptomatology, is of crucial importance as it would allow the subscription of effective palliative care and/or enrolment into specific clinical trials. Today, new analytical methods and research initiatives are being developed for the on-time diagnosis of this devastating disorder. During the last decade, spectroscopic techniques have shown great promise in the robust diagnosis of various pathologies, including neurodegenerative diseases and dementia. In the current study, blood plasma samples were analysed with near-infrared (NIR) spectroscopy as a minimally-invasive method to distinguish patients with AD (n = 111) from non-demented volunteers (n = 173). After applying multivariate classification models (principal component analysis with quadratic discriminant analysis - PCA-QDA), AD individuals were correctly identified with 92.8% accuracy, 87.5% sensitivity and 96.1% specificity. Our results show the potential of NIR spectroscopy as a simple and cost-effective diagnostic tool for AD. Robust and early diagnosis may be a first step towards tackling this disease by allowing timely intervention.