Comparative Analysis of Serum Amino Acid Profiles in Patients with Myasthenia Gravis and Multiple Sclerosis.

: Multiple sclerosis (MS) and myasthenia gravis (MG) are autoimmune diseases that attack the central nervous system (CNS) and the neuromuscular junction, respectively. As the common pathogenesis of both diseases is associated with an autoimmune background and the involvement of T and B lymphocytes, the overlapping of selected clinical symptoms may cause difficulties in the differential diagnosis of both diseases. : The aim of the study was to use Liquid Chromatography-Electrospray Ionization-Mass Spectrometry (LC-ESI-MS/MS) in conjunction with multivariate statistical analyses to examine the changes in amino acid metabolic profiles between patients with MG, MS, and a control group.

Serum amino acid profiles in patients with myasthenia gravis.

Myasthenia gravis (MG) is an autoimmune disease characterized by weakness and rapid fatigue. Diagnostic methods used for myasthenia gravis are not conclusive and satisfactory, therefore it is necessary to develop reliable tools to help diagnose myasthenia gravis as early as possible. The aim of the study was to use HPLC-MS in conjunction with multivariate statistical analyses to investigate changes in the amino acid metabolic profiles between myasthenia gravis patients compared and controls.

Serum amino acid profiles in patients with mild cognitive impairment and in patients with mild dementia or moderate dementia.

Neurodegenerative disorders are one of the greatest global challenges for social and health care in the twenty-first century. Nowadays, determination of cerebrospinal fluid biomarkers for early diagnosis is served by a complex sample preparation procedure with limited diagnostic accuracy. Furthermore, neuroimaging methods are expensive, time-consuming and are not readily available for use as a complimentary and common screening method.

Amino Acid Levels as Potential Biomarker of Elderly Patients with Dementia.

Dementia is a clinical syndrome characterized by cognitive impairment, in which there is disturbance of multiple higher cortical functions. The primary risk factor of dementia is old age, and due to significant changes in the worldwide demographic structure, the prevalence of cognitive impairment is increasing dramatically with aging populations in most countries. Alzheimer's disease is the predominant and leading cause of dementia.

Metabolomic Signature of Early Vascular Aging (EVA) in Hypertension.

Arterial stiffening is a hallmark of early vascular aging (EVA) syndrome and an independent predictor of cardiovascular morbidity and mortality. In this case-control study we sought to identify plasma metabolites associated with EVA syndrome in the setting of hypertension. An untargeted metabolomic approach was used to identify plasma metabolites in an age-, BMI-, and sex-matched groups of EVA ( = 79) and non-EVA ( = 73) individuals with hypertension.

Bayesian multilevel model of micro RNA levels in ovarian-cancer and healthy subjects.

In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis.

How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.

Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure.

Multilevel pharmacokinetics-driven modeling of metabolomics data.

Introduction: Multilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge.

Objectives: In this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients.

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data.

Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates.

The metabolic profiles of pterin compounds as potential biomarkers of bladder cancer-Integration of analytical-based approach with biostatistical methodology.

Cancer disease is the second leading cause of death across the world. The analysis of potential biomarkers of cancer can be useful in cancer screening or cancer diagnosis, and may provide valuable information on the disease risk and progression. Pterin compounds have been studied as candidates of potential biomarkers as their elevated levels have been reported in various cancer diseases.

Statistical-based approach in potential diagnostic application of urinary nucleosides in urogenital tract cancer.

Aim: We aimed at evaluation the potential diagnostic role of urinary nucleosides in urogenital tract cancer.

Materials & Methods: Concentrations of 12 nucleosides determined by LC-MS/MS were subjected to correlation, association and interaction analyses.

Results: We identified six pairs of nucleosides differently correlated in the group of patients and controls (p < 0.

Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography.

The objective of this study was to model the retention of nucleosides and pterins in hydrophilic interaction liquid chromatography (HILIC) via QSRR-based approach. Two home-made (Amino-P-C18, Amino-P-C10) and one commercial (IAM.PC.

The application of connected QSRR and QSAR strategies to predict the physicochemical interaction of acridinone derivatives with DNA.

Acridinone derivatives as imidazoacridinones and triazoloacridinones are the new potent antitumor agents characterized by different mechanisms of action related to their ability to interact with DNA. The analysis undertaken in this study involves searching of QSAR (Quantitative Structure-Activity Relationship) and QSRR (Quantitative Structure- Retention Relationship) models, which would allow to predict the biological activity of acridinones expressed as the ability to stabilize the secondary structure of DNA (ΔT), based on their structural parameters and chromatographic retention data. For this purpose, 20 acridinone derivatives were subjected to chromatographic analyses and molecular modeling, followed by statistical analyses using multiple linear regression method (MLR).