IonFlow: a galaxy tool for the analysis of ionomics data sets

Introduction Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools. Objective Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data. Methods IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis. Results and Conclusion The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.

PHYTOCHEMICAL AND ELEMENTAL PROFILING AND STANDARDIZATION OF SOME AYURVEDA MEDICINES USED IN COVID-19 PANDEMIC

Tribhuvankirti Rasa is a herbo-mineral Ayurvedic medicine regularly used to treat different types of fever. It has antipyretic and analgesic activities. It is an effective medicine for the common cold, flu and other Vata kapha problems. Laghumalini Vasanta is also Ayurvedic medicine, used to treat chronic fever and effective in pitta disorders. Ministry of AYUSH, Government of India also recommended these medicines to prevent the severe conditions of Cov-2 infection. Review of literature suggested that phytochemical and elemental characterization parameters of Tribhuvankirti Rasa and Laghumalini Vasant are not reported. The objective of this study is to report phytochemical and elemental profiling and to standardize Tribhuvankirti Rasa (TKR) and Laghumalini Vasant (LMV) to confirm quality and purity. Tribhuvankirti Rasa and Laghumalini Vasant evaluated for phytochemical and elemental parameters by HPTLC and ICP-OES respectively. HPTLC analysis confirms LMV contains Piperine and TKR contains Piperine and 6-Gingerol. The solvent systems toluene: ethyl acetate (7: 3) v/v for Piperine & Hexane: Ethyl acetate: Formic acid (4 : 6 : 0.1) v/v for 6-Gingerol were optimized. ICP-OES analysis confirms presence of Zn in LMV and Hg in TKR. HPTLC and ICP-OES methods were validated successfully for Tribhuvankirti Rasa and Laghumalini Vasant. The characterization and method validation parameters presented in this paper may serve as standard reference for quality control analysis of Tribhuvankirti Rasa and Laghumalini Vasant.

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.

Metabolomic and elemental profiling of human tissue in kidney cancer

Introduction Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite advances in treatment, no specific biomarker is currently in use to guide therapeutic interventions. Objectives Major aim of this work was to perform metabolomic and elemental profiling of human kidney cancer and normal tissue and to evaluate cancer biomarkers. Methods Metabolic and elemental profiling of tumor and adjacent normal human kidney tissue from 50 patients with kidney cancer was undertaken using three different analytical methods. Results Five potential tissue biomarkers of kidney cancer were identified and quantified using with high-resolution nuclear magnetic resonance spectroscopy. The contents of selected chemical elements in tissues was analyzed using inductively coupled plasma optical emission spectrometry. Eleven mass spectral features differentiating between kidney cancer and normal tissues were detected using silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry. Conclusions Our results, derived from the combination of ICP-OES, LDI MS and 1H NMR methods, suggest that tissue biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.