Portable X-ray Fluorescence as a Rapid Determination Tool to Detect Parts per Million Levels of Ni, Zn, As, Se, and Pb in Human Toenails: A South India Case Study.

Chronic exposure to inorganic pollutants adversely affects human health. Inductively coupled plasma mass spectrometry (ICP-MS) is the most common method used for trace metal(loid) analysis of human biomarkers. However, it leads to sample destruction, generation of secondary waste, and significant recurring costs. Portable X-ray fluorescence (XRF) instruments can rapidly and nondestructively determine low concentrations of metal(loid)s. In this work, we evaluated the applicability of portable XRF as a rapid method for analyzing trace metal(loid)s in toenail samples from three populations (n = 97) near the city of Chennai, India. A Passing-Bablok regression analysis of results from both methods revealed that there was no proportional bias among the two methods for nickel (measurement range ∼25 to 420 mg/kg), zinc (10 to 890 mg/kg), and lead (0.29 to 4.47 mg/kg). There was a small absolute bias between the two methods. There was a strong proportional bias (slope = 0.253, 95% CI: 0.027, 0.614) between the two methods for arsenic (below detection to 3.8 mg/kg) and for selenium when the concentrations were lower than 2 mg/kg. Limits of agreement between the two methods using Bland-Altman analysis were derived for nickel, zinc, and lead. Overall, a suitably calibrated and evaluated portable XRF shows promise in making high-throughput assessments at population scales.

Fast eQTL Analysis for Twin Studies.

Twin data are commonly used for studying complex psychiatric disorders, and mixed effects models are one of the most popular tools for modeling dependence structures between twin pairs. However, for eQTL (expression quantitative trait loci) data where associations between thousands of transcripts and millions of single nucleotide polymorphisms need to be tested, mixed effects models are computationally inefficient and often impractical. In this paper, we propose a fast eQTL analysis approach for twin eQTL data where we randomly split twin pairs into two groups, so that within each group the samples are unrelated, and we then apply a multiple linear regression analysis separately to each group. A score statistic that automatically adjusts the (hidden) correlation between the two groups is constructed for combining the results from the two groups. The proposed method has well-controlled type I error. Compared to mixed effects models, the proposed method has similar power but drastically improved computational efficiency. We demonstrate the computational advantage of the proposed method via extensive simulations. The proposed method is also applied to a large twin eQTL data from the Netherlands Twin Register.

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.

INTRODUCTION: The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. OBJECTIVES: This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. METHODS: Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. RESULTS: The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. CONCLUSION: The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

Advanced taste sensors based on artificial lipids with global selectivity to basic taste qualities and high correlation to sensory scores.

Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets.

Design of an expert system for mitigating trace element toxicity in cancer risk management.

Cancer risk management involves obliterating excess concentration of cancer causing trace elements by the natural immune system and hence intake of nutritious diet is of paramount importance. Human diet should consist of essential macronutrients that have to be consumed in large quantities and trace elements are to be consumed in very little amount. As some of these trace elements are causative factors for various types of cancer and build up at the expense of macronutrients, cancer risk management of these trace elements should be based on their initial concentration in the blood of each individual and not on their tolerable upper intake level. We propose an information theory based Expert System (ES) for estimating the lowest limit of toxicity association between the trace elements and the macronutrients. Such an estimate would enable the physician to prescribe required medication containing the macronutrients to annul the toxicity of cancer risk trace elements. The lowest limit of toxicity association is achieved by minimizing the correlated information of the concentration correlation matrix using the concept of Mutual Information (MI) and an algorithm based on a Technique of Determinant Inequalities (TDI) developed by the authors. The novelty of our ES is that it provides the lowest limit of toxicity profile for all trace elements in the blood not restricted to a group of compounds having similar structure. We demonstrate the superiority our algorithm over Principal Component Analysis in mitigating trace element toxicity in blood samples.