Camilo L. M. Morais

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Significance Vibrational spectroscopy is an ideal technique for analysis of biofluids, as it provides a “spectral fingerprint” of all of the molecules present within a biological sample, thus generating a holistic picture of the sample’s status. Neurodegenerative diseases lack early and accurate diagnosis, and tests currently used for their detection are either invasive or expensive and time-consuming. This study used blood plasma to diagnose and differentiate various neurodegenerative diseases; the achieved sensitivities and specificities are equal to, or even higher than, the ones obtained by clinical/molecular methods. Herein, we show that spectroscopy could provide a simple and robust diagnostic test. Additional work should include asymptomatic individuals for an early screening test and exploration of neurodegenerative diseases at all stages of severity. The progressive aging of the world’s population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer’s disease (AD; n = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype (APOE ε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases (n = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; n = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; n = 30), Parkinson’s disease (PD; n = 32), and progressive supranuclear palsy (PSP; n = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias.

A colorimetric microwell method using a desktop scanner for biochemical assays

A method for rapid, inexpensive and sensitive simultaneous analysis of glucose, creatinine, triglycerides, total cholesterol and total protein is needed to analyze blood. The proposed method is based on the production of a specific color after reaction. The method was adapted to a 64-microwell plate format, and it uses the transparency scanner feature of a commercially available desktop scanner. Each microwell plate had an 8×8 array of flat-bottomed 250μL microwells, and these microwells were used to simultaneously house the solutions for clinical assay. The scanned image was saved in TIFF format in a portable computer and then processed using a Graphic User Interface (GUI) designed in our laboratory to obtain analytical curves and to automate the mathematics and statistics calculations. This automation improved the analytical frequency of the method. The results showed that it is possible to measure a few microliters of solution with exactitude and precision better than 5.30%. The measured concentration ranges of glucose, triglycerides, creatinine, total cholesterol and total protein were 0.781 to 100, 1.56 to 200, 0.031 to 4.0, 1.56 to 200mg dL(-1) and 0.031 to 4.0g dL(-1), respectively. The limits of detection were 16.2, 51.7, 0.12, 41.5mg dL(-1) and 0.62g dL(-1) for glucose, triglycerides, creatinine, total cholesterol and total protein, respectively. The recoveries were from 98.7% to 101.3% for total cholesterol, 98.7% to 124.9% for triglycerides, 54.2% to 98.3% for total protein, 89.6% to 101% for glucose and 65.7% to 115.4% for creatinine. The results provided by the scanner were compared with those obtained with a commercial photometer and did not show significant differences at a confidence level of 95%. Good results were obtained for the correlation coefficient and Root Mean Square Error of Prediction (RMSEP) values for the five parameters, especially the total cholesterol and creatinine. The RMSEP values for glucose, creatinine, triglyceride, total cholesterol and total protein were 8.05, 0.28, 7.69, 1.41mg dL(-1) and 2.2g dL(-1), respectively.

Variable selection with a support vector machine for discriminating Cryptococcus fungal species based on ATR-FTIR spectroscopy

Variable selection with supervised classification is currently an important tool for discriminating biological samples. In this paper, 15 supervised classification algorithms based on a support vector machine (SVM) were applied to discriminate Cryptococcus neoformans and Cryptococcus gattii fungal species using ATR-FTIR spectroscopy. These two fungal species of the Cryptococcus genus are the etiological agents of Cryptococcosis, which is an opportunistic or primary fungal infection with global distribution. This disease is potentially fatal, especially for immunocompromised patients, like those suffering from AIDS. The multivariate classification algorithms tested were based on principal component analysis (PCA), successive projections algorithm (SPA) and genetic algorithm (GA) as data reduction and variable selection methods, being coupled to a SVM with different kernel functions (linear, quadratic, 3rd order polynomial, radial basis function, and multilayer perceptron). Some of these algorithms achieved very successful classification rates for discriminating fungal species, with accuracy, sensitivity, and specificity equal to 100% using both SPA-SVM-polynomial and GA-SVM-polynomial algorithms. These results show the potential of such techniques coupled to ATR-FTIR spectroscopy as a rapid and non-destructive tool for classifying these fungal species.

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.

Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy as a new technology for discrimination between Cryptococcus neoformans and Cryptococcus gattii

Systemic fungal infections are among the most difficult diseases to manage in humans, especially when the recognition of the correct species is required for a precise and successful treatment. This is the case for Cryptococcus species and its genotypes, which are the main cause of meningitides in immunocompromised patients. Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy with discriminant analysis was employed to distinguish between the pathogenic fungal species Cryptococcus neoformans and Cryptococcus gattii by determining which wavenumber–absorbance/intensity relationships might reveal biochemical differences. Cryptococcus inactivated colonies were applied to an ATR crystal, and vibrational spectra were obtained in the ATR mode. Twenty-eight Cryptococcus isolates, fourteen C. neoformans and fourteen C. gattii were investigated. Spectral categories were analyzed using principal component analysis (PCA), successive projection algorithm (SPA) and genetic algorithm (GA) followed by linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). Multivariate classification accuracy results were estimated based on sensitivity, specificity, positive (or precision) and negative predictive values, Youden index, and positive and negative likelihood ratios. Sensitivity for C. neoformans and C. gattii categories were 84.4% and 89.3%, respectively, using a QDA-LDA model with 17 wavenumbers with respect to their “fingerprints”. Compared to classical methods for differentiation of Cryptococcus species, this new technology could represent an alternative and innovative tool for faster and cheaper fungal identification for routine diagnostic laboratories.

Determination of the geographical origin and ethanol content of Brazilian sugarcane spirit using near-infrared spectroscopy coupled with discriminant analysis

Aguardente is a typical Brazilian spirit produced by the distillation of sugarcane. The valorisation of this spirit can be attributed to its notoriety, related to its production origin which can influence its quality. Therefore, the objective of this study was to use NIR spectroscopy coupled with discriminant analysis as a non-destructive method to validate the authenticity of aguardentes produced in two geographic regions and to predict their ethanol content. Some chemometric methods were used to discriminate the sugarcane aguardente, namely partial least squares-linear discriminant analysis (PLS-LDA), principal component analysis-linear discriminant analysis (PCA-LDA), and variable selection techniques such as successive projection algorithm (SPA-LDA) and genetic algorithm (GA-LDA). NIR spectra were collected using a FT-NIR spectrometer (4000–10 000 cm−1) with a spectral resolution of 16 cm−1, 8 cm−1 intervals, and 64 scans. The PCA results were not effective in classifying the aguardente samples, but with PLS-DA, PCA-LDA, SPA-LDA, GA-LDA and LDA it was possible to obtain an 87.2% prediction accuracy. Better results were obtained using PLS-DA on raw spectra and GA-LDA using only six wavelengths (namely 1025 nm, 1181 nm, 1596 nm, 1610 nm, 1653 nm, 2125 nm) which gave a relatively good accuracy rate (up to 87.2%). NIR spectroscopy and chemometrics can be used as a non-destructive method to validate authenticity, and PLSR combined with NIR was a good non-destructive method to predict ethanol content in sugarcane aguardente.

A low-cost microcontrolled photometer with one color recognition sensor for selective detection of Pb2+ using gold nanoparticles

The present work describes a microcontrolled photometer based on light-emitting-diodes (LEDs) for detection of Pb2+ using gold nanoparticles (AuNPs). The photometer makes use of a single LED as a light source, a sensor TCS230 (TAOS, USA) and an Arduino electronic card as an acquisition system. On the sensor, the light from the three closely adjoined red, green, and blue LEDs composing the “white” light source LED is contact-coupled to the map-illumination pointed toward the detection cell. To maintain a constant light intensity, a common white-color LED (emitting a 450–620 nm continuous spectrum) was employed as a controllable light source. Software was written in C++ to control the photometer through a USB interface and for data acquisition. Pb2+ measurements are based on the color change of AuNPs due to their aggregation provoked by Pb2+. The method showed excellent selectivity for Pb2+ compared to other 19 metal ions (Ag+, Al3+, Ba2+, Ca2+, Cd2+, Co2+, Cr3+, Cu2+, Fe2+, Hg+, K+, Li+, Mg2+, Mn2+, Na+, Ni2+, Sn2+, Sr2+, and Zn2+). Pb2+ was detected with the photometer and also monitored via UV-Vis. Solutions containing Pb2+ in the concentration range from 0.6 to 10 mmol L−1 were employed to construct the analytical curves, proving a limit of detection (LOD) of 0.89 mmol L−1. The sensitivity was compared to that obtained with a UV-Vis spectrophotometer at 520 nm. A repeatability of 4.11% (expressed as the relative standard deviation of 10 measurements) was obtained. The proposed method was successfully applied to detect Pb2+ in spiked water samples.

Quantification of Synthetic Amino-Nitroquinoxaline Dyes: An Approach Using Image Analysis

This paper describes the use of digital imaging acquired from a conventional desktop scanner for characterization of newly synthesized amino-nitroquinoxaline dyes. The synthetic protocol was based on reactions of building block 2,3-dichloro-6,7-dinitroquinoxaline with aliphatic amines and experiments based on image analysis were carried out with compounds in solution (96-microwell plates) and/or adsorbed on silica (thin-layer chromatography (TLC) plates). Three main goals were achieved: firstly, compounds 2-chloro-6,7-dinitro-3-pyrrolidinoquinoxaline and 6,7-dinitro-2,3-dipyrrolidinoquinoxaline had their concentration predicted in dimethyl sulfoxide (DMSO) solution, as a mixture. Secondly, TLC studies were performed to build a kinetic profile in attempt to monitor reaction involving conversion of 2-chloro-6,7-dinitro-3-pyrrolidinoquinoxaline into 6,7-dinitro-2,3-dipyrrolidinoquinoxaline. Lastly, image analysis and UV-Vis spectroscopy were applied to study the interaction of 2,3-di-n-butylamino-6,7-dinitroquinoxaline with hydroxide anion in DMSO. These approaches based on digital image characterization were successfully implemented in qualitative and quantitative analysis of organic dyes.

Determination and analytical validation of creatinine content in serum using image analysis by multivariate transfer calibration procedures

The main objective of this study was to explore the feasibility of image analysis (RGB, HSI and gray intensity histograms) and partial least squares regression using a calibration model transfer technique in the quantitative analysis of creatinine in serum samples by the use of two different devices: a desktop scanner and a cell phone camera. In addition, a multivariate validation model based on linearity, accuracy, sensitivity, bias, prediction uncertainty and β-expectation tolerance intervals was estimated. The colorimetric reaction for creatinine was carried out in a 96-microwell plate format with flat-bottomed 250 µL microwells. The results achieved separately for both devices were very significant compared to the reference method, showing no statistical difference at a confidence level of 95%. When the calibration model based on the scanner was used directly to predict the concentration for cell phone data, it produced an unsatisfactory prediction with the RMSEP = 0.79 mg dL−1. However, the prediction was greatly improved after the calibration was transferred based on DS (RMSEP = 0.14 mg dL−1). The same trend was observed when the scanned data were predicted by the calibration model based on the cell phone, where the initial RMSEP = 0.25 mg dL−1 was reduced to RMSEP = 0.10 mg dL−1, after calibration transfer. These results show the transferability of the calibration transfer technology applied to the image data, where efficient calibration transfer to other devices was clearly demonstrated with all devices in the study effectively giving similar results on a transfer set.

Determination of serum protein content using cell phone image analysis

This paper presents a simple, fast and inexpensive way to measure serum protein content (albumin and total proteins) by integration of color images acquired with a cell phone camera and multiple linear regression (MLR). MLR models were built using the RGB-HSV image systems from the color changes induced by bromocresol green and Biuret methods on solutions containing albumin (0.24 to 5.03 g dL−1) and total proteins (0.25 to 8.20 g dL−1), respectively, acquired from a cell phone camera. These methods were adapted to a microscale analysis by using an ELISA 96-microwell plate of 250 μL as a reaction container, and subsequently were statistically validated. The RMSEP values obtained for albumin and total protein models were respectively equal to 0.28 and 0.12 g dL−1, and their precision was respectively equal to 6.16 and 4.85%. No statistical difference was observed at a confidence level of 95% between the protein concentration calculated by the proposed method and those found by the reference method, proving the reliability of the imaging method as an alternative approach for these assays.