Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

A novel R-package graphic user interface for the analysis of metabonomic profiles

Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. /Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA). Multivariate supervised analysis: partial least squares (PLS), linear discriminant analysis (LDA), k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. /Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics

Development of a nomogram for the prediction of periodontal tooth loss using the staging and grading system: A long- term cohort study

AimTo develop and internally validate a nomogram built on a multivariate prediction model including parameters from the new classification of periodontal diseases, able to predict, at baseline, the occurrence of tooth loss due to periodontal reason (TLP).Materials and MethodsA total of 315 individuals diagnosed with periodontal disease and receiving a minimum of one annual supportive periodontal therapy visit were included in the study. Patients were staged and graded based upon baseline data. The population was divided into a development (254 patients) and a validation (61 patients) cohort to allow subsequent temporal validation of the model. According to the TLP at the 10- year follow- up, patients were categorized as - low tooth loss- (- ¤ 1 TLP) or - high tooth loss- (- ¥ 2 TLP). Bootstrap internal validation was performed on the whole data set to calculate an optimism- corrected estimate of performance.ResultsThe generated nomogram showed a strong predictive capability (AUC = 0.81) and good calibration with an intercept = 0 and slope = 1. These findings were confirmed by internal validation using bootstrapping (average bootstrap AUC = 0.83).ConclusionsThe clinical implementation of the present nomogram guides the prediction of patients with high risk of disease progression and subsequent tooth loss for personalized care.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163380/2/jcpe13362.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163380/1/jcpe13362_am.pd

MVBatch: A matlab toolbox for batch process modeling and monitoring

[EN] A novel user-friendly graphical interface for process understanding, monitoring and troubleshooting has been developed as a freely available MATLAB toolbox, called the MultiVariate Batch (MVBatch) Toolbox. The main contribution of this software package is the integration of recent developments in Principal Component Analysis (PCA) based Batch Multivariate Statistical Process Monitoring (BMSPM) that overcome modeling problems such as missing data, different speed of process evolution and length of batch trajectories, and multiple stages. An interactive user interface is provided, which aims to guide users in handling batch data through the main BMSPM steps: data alignment, data modeling, and the development of monitoring schemes. In addition, a small-scale non-linear dynamic simulator of the fermentation process of the Saccharomyces cerevisiae cultivation is available to generate realistic batch data under normal and abnormal operating conditions. This generator of synthetic data can be used for teaching purposes or as a benchmark to illustrate and compare the performance of new methods with sound techniques published in the field of BMSPM.This work is partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through the projects DPI2017-82896-C2-1-R and TIN2017-83494-R. Authors also acknowledge the volunteers to test MVBatch and report their impressions for this software tutorial.González Martínez, JM.; Camacho Paez, J.; Ferrer, A. (2018). MVBatch: A matlab toolbox for batch process modeling and monitoring. Chemometrics and Intelligent Laboratory Systems. 183:122-133. https://doi.org/10.1016/j.chemolab.2018.11.001S12213318

Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra

Rapid Classification Of Agricultural Products Based On Their Electro-Optic Properties Using Near Infrared Reflectance And Chemometrics

Abstract. Near infrared technology have been widely applied in many fields, including agriculture especially in sorting and grading process. The advantage of this technology: simple sample preparation, rapid, effective and non-destructive. The main objective of this study is to evaluate the feasibility of NIR technology in classifying several agricultural products based on their electro-optic properties. NIR diffuse reflectance spectra of apples, bananas, mangoes, garlics, tomatoes, green grapes, red grapes and oranges were acquired in wavelength range of 1000-2500 nm with gradual increment of 2 nm. Chemometrics methods were applied in combination with NIR spectra data. Classification was performed by applying principal component analysis (PCA) followed by non-iterative partial least square (NIPALS) cross validation. The results showed that NIR and chemometrics was able to differentiate and classify these agricultural products with two latent variables (2 PCs) and total explained variance of 97% (88% PC1 and 9% PC2). Furthermore, it also showed that multiplicative scatter correction (MSC) was found to be effective spectra correction or enhancement method and increased classification accuracy and robustness. It may conclude that NIR technology combined with chemometrics was feasible to apply as a rapid and non-destructive method for sorting and grading agricultural products. Rapid Classification Of Agricultural Products Based On Their Electro-Optic Properties Using Near Infrared Reflectance And ChemometricsAbstract. Aplikasi teknologi near infra red (NIR) telah digunakan dalam banyak bidang, termasuk untuk bidang pertanian terutama pada proses sortasi dan grading. Keunggulan metode ini antara lain : rapid, efektif, simultan dan tanpa merusak objek yang dikaji. Tujuan utama dari studi ini adalah untuk mengkaji potensi NIR dalam mengklasifikasi beberapa produk pertanian berdasarkan karakteristik sifat elektro-optik dari produk tersebut. Spektrum NIR pada panjang gelombang 1000 – 2500 nm dengan increment 2 nm diakuisisi untuk produk pertanian : apel, pisang, manga, bawang putih, tomat, anggur hijau, anggur merah dan jeruk. Metode chemo metrics digunakan dalam studi ini untuk dikombinasikan dengan spektrum NIR. Klasifikasi produk pertanian dilakukan dengan menerapkan metode principal component analysis (PCA) yang disertai dengan metode non-iterative partial least square (NIPALS) cross validation. Hasil studi menunjukkan bahwa kombinasi NIR dan chemo metrics mampu membedakan dan mengklasifikasi produk pertanian tersebut dengan menggunakan dua latent variable pada PCA (2 PCs) dengan total explained variance 97% (88% PC1 dan 9% PC2). Selain itu, dari studi ini juga didapatkan bahwa perbaikan data spectrum dengan metode multiplicative scatter correction (MSC) sebelum klasifikasi mampu meningkatkan akurasi hasil klasifikasi. Secara umum, dapat disimpulkan bahwa teknologi NIR dan chemo metrics dapat dijadikan sebagai metode yang efektif untuk sortasi dan atau grading produk pertanian

MATLAB in electrochemistry: A review

International audienceMATLAB (MATrix LABoratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces and interfacing with programs written in other languages, including C, C ++ , Java, Fortran and Python. Electrochemistry is a branch of chemistry that studies the relationship between electricity, as a measurable and quantitative phenomenon, and identifiable chemical change, with either electricity considered an outcome of a particular chemical change or vice versa. MATLAB has obtained a wide range of applications in different fields of science and electrochemists are also using it for solving their problems which can help them to obtain more quantitative and qualitative information about systems under their studies. In this review, we are going to cast a look on different applications of MATLAB in electrochemistry and for each section, a number of selected articles published in the literature will be discussed and finally, the results will be summarized and concluded