Spectrochemical differentiation in gestational diabetes mellitus based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and multivariate analysis

Abstract: Gestational diabetes mellitus (GDM) is a hyperglycaemic imbalance first recognized during pregnancy, and affects up to 22% of pregnancies worldwide, bringing negative maternal–fetal consequences in the short- and long-term. In order to better characterize GDM in pregnant women, 100 blood plasma samples (50 GDM and 50 healthy pregnant control group) were submitted Attenuated Total Reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, using chemometric approaches, including feature selection algorithms associated with discriminant analysis, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM), analyzed in the biofingerprint region between 1800 and 900 cm−1 followed by Savitzky–Golay smoothing, baseline correction and normalization to Amide-I band (~ 1650 cm−1). An initial exploratory analysis of the data by Principal Component Analysis (PCA) showed a separation tendency between the two groups, which were then classified by supervised algorithms. Overall, the results obtained by Genetic Algorithm Linear Discriminant Analysis (GA-LDA) were the most satisfactory, with an accuracy, sensitivity and specificity of 100%. The spectral features responsible for group differentiation were attributed mainly to the lipid/protein regions (1462–1747 cm−1). These findings demonstrate, for the first time, the potential of ATR-FTIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost GDM detection

Optical sensors and instrumentations for determination of contaminants in water

Orientadores: Ivo Milton Raimundo Junior, Maria Fernanda D'Oliveira PimentelTese (doutorado) - Universidade Estadual de Campinas, Instituto de QuimicaResumo: Este trabalho descreve o desenvolvimento de sensores ópticos para determinação de benzeno, tolueno, etilbenzeno e xilenos (BTEX) e de íons metálicos em águas. Para a determinação de BTEX, monolitos de polidimetilsiloxano (PDMS) foram colocados dentro de um frasco preenchido com soluções aquosas dos compostos BTEX por um determinado tempo. Em seguida, a fase sensora era removida da solução, seca rapidamente e inserida num sistema de medidas, empregando-se um espectrofotômetro FT-NIR. Limites de detecção de 0,079; 0,12; 0,14 e 0,28 mg L para benzeno, tolueno, etilbenzeno e xilenos foram alcançados. A fase sensora foi aplicada a amostras de águas contaminadas por gasolina, quantificando teores de BT (benzeno e tolueno) sem diferença estatística, no nível de 95% de confiança, comparada a técnica GC-FID. A fase sensora também foi usada na determinação simultânea de BTX. Valores de RMSEP (raiz quadrada do erro médio quadrático de previsão) de 0,57 mg L para benzeno, 2,21 mg L para tolueno e 1,23 mg L para xilenos foram alcançados. Um fotômetro no infravermelho próximo baseado em LED (diodos emissores de luz) para a determinação de BTEX total foi desenvolvido. O instrumento desenvolvido opera com dois LED, um fotodiodo, um sistema de fibras ópticas para captação da radiação, célula de transmissão e um programa em Visualbasic.Net para controle e aquisição de dados. O instrumento pode ser uma alternativa viável, de baixo custo para a determinação de BTEX total em águas. Foi avaliado o comportamento do novo reagente luminescente di(hexafluorofosfato) de bis(1,10-fenantrolina)(2-(1H-imidazo[4,5- f][1,10]fenantrolina)Rutenio (II), abreviadamente [Ru(phen)2iip](PF6)2, no desenvolvimento de um sensor óptico para a determinação de íons metálicos em águas. A imobilização do reagente em matrizes poliméricas revelou que o sensor óptico e seletivo ao íon Cu(II), apresentando limite de detecção 32 mg L. O novo complexo de rutênio (II) foi aplicado numa determinação simultânea dos íons metálicos Cu(II) e Hg(II) em solução aquosa, alcançando valores de RMSEP de 2,12 mg L e 0,95 mg L, respectivamenteAbstract: This work describes the development of optical sensors for determination of benzene, toluene, ethylbenzene and xylenes (BTEX) and metal ions in water. For the determination of BTEX, monoliths of polydimethylsiloxane (PDMS) were inserted into a bottle filled with aqueous solutions of BTEX compounds for a pre-defined period of time. Afterwards the sensing phase was removed from the solution, dried and placed in the detection system of an FT-NIR spectrophotometer. Detection limits of 0.079, 0.12, 0.14 and 0.28 mg L for benzene, toluene, ethylbenzene and xylenes, respectively, have been achieved. The sensing phase was applied to the determination of benzene and toluene in water samples contaminated by gasoline, providing results that did not show statistical differences from those obtained by GC-FID at a confidence level of 95%. The sensing phase was also applied to the simultaneous determination of BTX in contaminated water, providing RMSEP values (root mean square error of prediction) of 0.57 mg L for benzene, 2.21 mg L for toluene and 1.23 mg L for xylenes. A near infrared photometer based on LED (light emitting diodes) for the determination of total BTEX was developed. The instrument operates with two LED as light sources and a photodiode as detector, a transmission cell connected to an optical fiber bundles; a VisualBasic.Net program was written for control and data acquisition. The instrument performance indicated that it can be a feasible and low cost alternative for the determination of total BTEX in water. Finally, it was evaluated the performance of the new luminescent reagent bis(1,10-phenanthroline)(2-(1H-imidazol-2- yl)-1H-imidazo[4,5-f][1,10]phenanthroline)ruthenium(II) di(hexafluorophosphate) for the development of an optical sensor for the determination of metal ions in water. The immobilization the reagent in the polymeric matrices showed that the optical sensor is selective to Cu (II) ion, providing a detection limit of 32 mg L. The new complex of ruthenium (II) was also applied to the simultaneous determination of Cu (II) and Hg (II) in aqueous solution, showing RMSEP values 2.12 mg L and 0.95 mg L, respectivelyDoutoradoQuimica AnaliticaDoutor em Ciência

A biospectroscopic analysis of human prostate tissue obtained from different time periods points to a trans-generational alteration in spectral phenotype

Prostate cancer is the most commonly-diagnosed malignancy in males worldwide; however, there is marked geographic variation in incidence that may be associated with a Westernised lifestyle. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy combined with principal component analysis-linear discriminant analysis or variable selection techniques employing genetic algorithm or successive projection algorithm could be utilised to explore differences between prostate tissues from differing years. In total, 156 prostate tissues from transurethral resection of the prostate procedures for benign prostatic hyperplasia from 1983 to 2013 were collected. These were distributed to form seven categories: 1983?1984 (n = 20), 1988?1989 (n = 25), 1993?1994 (n = 21), 1998?1999 (n = 21), 2003?2004 (n = 21), 2008?2009 (n = 20) and 2012?2013 (n = 21). Ten-\ensuremathμm-thick tissue sections were floated onto Low-E (IR-reflective) slides for ATR-FTIR or Raman spectroscopy. The prostate tissue spectral phenotype altered in a temporal fashion. Examination of the two categories that are at least one generation (30 years) apart indicated highly-significant segregation, especially in spectral regions containing DNA and RNA bands (≈1,000?1,490 cm?1). This may point towards alterations that have occurred through genotoxicity or through epigenetic modifications. Immunohistochemical studies for global DNA methylation supported this. This study points to a trans-generational phenotypic change in human prostate

NIRS and iSPA-PLS for predicting total anthocyanin content in jaboticaba fruit

The aim of this study was to evaluate the potential of the successive projection algorithm for interval selection in partial least squares (iSPA-PLS) together with near-infrared reflectance spectroscopy (NIRS) as a feasible method to determine the total anthocyanin content (TAC) of intact jaboticaba fruit [Myrciaria jaboticaba (Vell.) O. Berg]. A total of 579 jaboticaba fruit were collected in three different harvests in three separate years (2011 and 2013). The correlation coefficients between the predicted and measured TAC were between 0.65 and 0.89, the RMSEPs were 7.55 g kg(-1) and 9.35 g kg(-1) (good accuracy) for prediction set, respectively. The RPD ratios for TAC were in the range of 2.57-3.19 with iSPA-PLS, which showed better predictive performance (acceptable precision). These results suggest that the NIR spectroscopy and wavelength selection (iSPA-PLS) algorithm can be used to determine the TAC of intact jaboticaba fruit. (C) 2014 Elsevier Ltd. All rights reserved.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using near-infrared spectroscopy and chemometrics

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIR) as a rapid and non-destructive method to determine soluble solid content (SSC) in intact jaboticaba [Myrciaria jaboticaba (Veil.) O. Berg] fruit. Multivariate calibration techniques were compared with pre-processed data and variable selection algorithms, such as partial least squares (PLS), interval partial least squares (iPLS), a genetic algorithm (GA), a successive projections algorithm (SPA) and nonlinear techniques (BP-ANN, back propagation of artificial neural networks; LS-SVM, least squares support vector machine) were applied to building the calibration models. The PLS model produced prediction accuracy (R-2 = 0.71, RMSEP = 1.33 degrees Brix, and RPD = 1.65) while the BP-ANN model (R-2 = 0.68, RMSEM = 1.20 degrees Brix, and RPD = 1.83) and LS-SVM models achieved lower performance metrics (R-2 = 0.44, RMSEP = 1.89 degrees Brix, and RPD = 1.16). This study was the first attempt to use NIR spectroscopy as a non-destructive method to determine SSC jaboticaba fruit. (C) 2014 Elsevier Ltd. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Testing prediction accuracy of ideal and prolonged length of hospital stay following ovarian cancer cytoreduction using machine learning methods

Introduction/Backgroun Cytoreductive surgery for advanced high grade serous ovarian cancer (HGSOC) patients to achieve complete removal of all visible disease often requires prolonged surgical time and possible multi-visceral resection potentially necessitating HDU support and prolonged hospitalisation. Length of stay (LOS) has been suggested as a marker of quality or effectiveness of short-term care. Identifying modifiable risk factors at admission predicting LOS could lead to appropriately targeted interventions to improve the delivery of care. Modern data mining technologies such as Machine Learning (ML) could be helpful in monitoring hospital stays to improve standards of care. We aimed to improve the accuracy of predicting both ideal and prolonged LOS, by use of ML algorithms. Methodology A cohort of 176 HGSOC patients, who underwent surgical cytoreduction, from Jan 2014 to Dec 2017 was selected from the ovarian database. They were randomly assigned to ‘training’ and ‘test’ subcohorts. ML methods including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision-Tree-Analysis, and K-Nearest Neighbors were employed to derive predictive information for LOS from selected variables including age, BMI, Surgical Complexity Score (SCS), operative time, preoperative albumin and morbidity score (Clavien-Dindo 3–5). These methods were tested against conventional linear regression. The accepted ‘ideal’ LOS was deemed to be 5 days or fewer. Prolonged LOS was defined as time spent in the hospital beyond the 90th percentile. Through the introduction of the Enhanced Recovery after Surgery (ERAS) pathway in 2015, effort was made to shorten the LOS for patients following major surgery, whilst still assuring they received effective treatment and high-quality care. Results Mean and median LOS was 4.6 and 4.0 days (IQR 0–38), respectively. The delayed LOS group consisted those staying 10 days or longer. The rate of ideal LOS continuously improved for every year from 32% in 2016 to 73.5% in 2019 despite increasing mean SCS. For ideal LOS prediction accuracy, ML slightly outperformed conventional logistic regression, with no bowel resection and operative time been the most predictive variables. For prolonged LOS, LDA and SVM were more accurate to predict prolonged LOS than conventional regression. Bowel resection and Clavien-Dindo complications were most importantly contributing to the improved accuracy of the model (figure 1)

Testing prediction accuracy of hdu admission following high grade serous advanced ovarian cancer cytoreductive surgery using machine learning methods

Introduction/Background Advanced high grade serous ovarian cancer patients (HGSOC) frequently require extensive procedures including bowel resections and upper abdominal surgery potentially necessitating HDU/ICU support and prolonged hospitalisation. HDU/ICU admission is a measurable outcome that can be used as a benchmark of surgical care. Modern data mining technologies such as Machine Learning (ML), a subfield of Artificial Intelligence, could be helpful in monitoring HDU/ICU admissions to improve standards of care. We aimed to improve the accuracy of predicting HDU admission in that cohort of patients by use of ML algorithms. Methodology A cohort of 176 HGSOC patients, who underwent surgical cytoreduction from Jan 2014 to Dec 2017 was selected from the ovarian database. They were randomly assigned to ‘training’ and ‘test’ subcohorts. ML methods including Classification and Regression Trees (CART) and Support Vector Machine (SVM), were employed to derive predictive information for HDU/ICU admission from a list of selected preoperative, intraoperative, and postoperative variables. These methods were tested against conventional linear regression analyses. Results There were 29 out of 176 (16.4%) HDU/ICU admissions; 23 admissions were elective whilst six were unplanned admissions. For the outcome of HDU/ICU admission, both ML methods outperformed conventional regression by far (table 1). Bowel resection and operative time were the most predictive variables (figure 1). HDU/ICU admission was not associated with increased length of stay, increased number of postoperative complications, and increased risk of readmission within 30 days

Dispositivo Sensor óptico Com Fase Sensora De Silicona Para A Determinação De Hidrocarbonetos

DISPOSITIVO SENSOR ÓPTICO COM FASE SENSORA DE SILICONA PARA A DETERMINAÇÃO DE HIDROCARBONETOS. Novo dispositivo sensor óptico com fase sensora de silicona para a determinação de hidrocarbonetos presentes em diferentes matrizes, o qual tem como objetivo principal o uso geral de fases sensoras de silicona, ou fases sensoras de outro material polimérico, sendo utilizadas para a determinação de hidrocarbonetos preferencialmente em águas, cuja aplicação pode ser estendida para os gases e também para sólidos, utilizando a espectroscopia no infravermelho próximo como técnica de detecção, com o objetivo principal de obter e conhecer os compostos contaminantes e suas principais características, no sentido de tratar e evitar a contaminação de águas e gases. Constituído de um cilindro/disco, de dimensões variáveis, que pode ser adaptado a uma sonda de transflectância ou uma cela de transmitância para a determinação qualitativa e quantitativa de hidrocarbonetos, por meio de medidas espectrofotométricas no infravermelho próximo, onde o uso de soluções concentradas de cloreto de sódio aumentam os limites de detecção do sensor óptico.BRPI0502311 (A)G01N21/35G01N21/3577G01N21/359G01N21/35BR2005PI02311G01N21/35G01N21/3577G01N21/359G01N21/3

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70–98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3–5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS