Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 13th International Conference on Metabolomics and Systems Biology Hilton Zurich Airport Hohenbuehlstrasse 10, 8152, Opfikon, Switzerland.

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Scientific Program Day 1
Scientific Program Day 2

Day 1 :

Keynote Forum

Tao Huang

Shanghai Institutes for Biological Sciences, China

Keynote: Deciphering the mechanisms of complex diseases using network and machine learning approaches

Time : 09:40-10:25

Conference Series Euro Metabolomics 2018 International Conference Keynote Speaker Tao Huang photo
Biography:

Tao Huang has completed his Postdoctoral Fellowship in the Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York City, USA. He is currently a Professor and the Director of Bioinformatics Core Facility at the Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences ( P R China). His research interests include bioinformatics, computational biology, systems genetics and big data research. He has published over 100 articles in peer-reviewed journals. His works have been cited for 3138 times with an h-index of 26 and an i10-index of 64. He has been an Independent Reviewer for 30 journals. He is the Editor of the book: “Computational Systems Biology - Methods and Protocols” published in Springer; Guest Editor for BBA Molecular Basis of Disease, BBA General Subjects, Artificial Intelligence in Medicine, BioMed Research International, Combinatorial Chemistry & High Throughput Screening and Computational and Mathematical Methods in Medicine.

Abstract:

The complex diseases, such as cancer, involves dysfunctions on multiple levels. There have been several methods to identify the dysfunctions on each level. On genomics level, GWAS (Genome-wide association study) can detect the disease phenotype associated SNPs (single nucleotide polymorphisms). But many GWAS identified SNPs locate in intergenic region and can't be annotated to specific genes. With the help of eQTL (expression quantitative trait loci) method, the downstream genes of the GWAS SNPs can be found. And based on the co-expression network, regulatory network, protein-protein interaction network, or even protein-chemical interaction network of these genes, the possible cascade of the signal transduction from the genetic perturbations can be discovered. By integrating all these omics data, we can establish a systems biology model of how the GWAS SNPs affect the expression of their direct target genes, how these direct targets affect secondary target genes, or proteins, or metabolites, and eventually cause the catastrophic pathological changes on network or pathway. With the comprehensive network, we can not only study the mechanism of one disease, but also the relationship of several traits. For example, schizophrenia and anti-tuberculosis drug-induced hepatotoxicity co-occurred frequently but they do not share common genes. We mapped their genes onto the network, the common drivers of these two diseases were revealed through two-way RWR analysis. Another example is that the lung cancer dysfunctions on several levels but how the different level dysfunctions connect are still unknown. We investigated the mutation, methylation, mRNA and microRNA expression difference between cancer and normal tissues using machine learning methods, then mapped the dysfunction of each level onto the comprehensive network. By connecting each other with shortest paths, the highly frequent shortest path genes that can cause the tumorigenesis on multiple levels were discovered. Overall, the network and machine learning approaches are effective to dissect the complex system and reveal the mechanisms of diseases.

Break: Networking & Refreshments 10:25-10:45 @ Europa Foyer

Keynote Forum

Andrea Ragusa

University of Salento, Italy

Keynote: Metabolomics and lipidomic profile of clinical serum samples from patients with liver disease

Time : 10:45-11:30

Conference Series Euro Metabolomics 2018 International Conference Keynote Speaker Andrea Ragusa photo
Biography:

Andrea Ragusa pursued his PhD from the University of Southampton (UK) in 2005. He dedicated himself to nanomedicine and metabolomics by developing novel smart nanovectors for the targeted delivery of drugs and studying their effects in vitro through NMR and HPLC-MS investigations. He is an Organic Chemist with an expertise in analytical techniques. More recently, he shifted his efforts towards the analysis of clinical samples for better understanding the molecular mechanisms involved in several liver-related diseases, with particular attention to HCC (Hepatocellular carcinoma), as well as for identifying novel biomarkers and their exploitation, in combination with multivariate statistical analysis, in the early diagnosis and treatment of the disease.

Abstract:

Statement of the Problem: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer deaths worldwide. Unfortunately, early-stage HCC is asymptomatic and most patients already have an advanced stage at the time of diagnosis. Alpha fetal protein (AFP) is the current clinical biomarker used for the diagnosis and follow-up of HCC treatments. Nevertheless, its sensitivity is not satisfactory and there is an urgent need for the identification of novel more efficient biomarkers. In this regard, metabolomics can provide unique clinical information and it offers the possibility to understand the system-level effects of metabolites through its integration into multi-functional approaches. Nuclear magnetic resonance (NMR) spectroscopy already showed to be a valuable tool for metabolomic investigations, providing a system capable of studying and identifying novel biomarkers, as well as for yielding a metabolic fingerprint useful for the classification of several pathologies.
Methodology: 1D and 2D 1H NMR spectroscopy, in combination with univariate and multivariate statistical analysis (MVA), was used to investigate the metabolic profile of serum samples from HCC, type-2 diabetes mellitus (T2DM), and control subjects. The patients’ response to metformin or insulin treatment was also investigated. A similar analysis was also performed on the corresponding serum lipid extracts, yielding a more global view of the metabolic alterations occurring.
Findings: Several metabolites directly or indirectly related to energy pathways were found to be either over- or under-expressed in pathological patients with respect to control patients. Among them, a substantial increase in lactate production, with the concomitant decrease of branched amino acids, was observed. Similarly, an increase in the total fatty acids (FA) with a concomitant decrease of unsaturated FA, phosphocholines, phospholipids, and cholesterol was also detected.
Conclusion & Significance: NMR spectroscopy, in combination with MVA, allowed to gain a better insight into the metabolic alterations involved in HCC disease. The obtained metabolic fingerprints could be exploited in the early diagnosis of the disease
as well as in its follow-up upon treatment.

Keynote Forum

Gerald C Hsu

eclaireMD, USA

Keynote: Relationship between metabolism and obesity along with type 2 diabetes and cardiovascular risk

Time : 11:30-12:15

Conference Series Euro Metabolomics 2018 International Conference Keynote Speaker Gerald C Hsu photo
Biography:

Gerald C Hsu received an honorable PhD in mathematics and majored in engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted research during 2014-2018. His approach is “math-physics and quantitative medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning, and AI. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have. The author has not received any financial assistance from any organization.

Abstract:

Introduction: By using the big data on one patient (author), this clinical paper describes the relationship between the metabolism state and medical conditions, including obesity, diabetes, and cardiovascular risk.
Methodology: The obese patient was diagnosed with type 2 diabetes (T2D), hyperlipidemia, hypertension for over 25 years along with suffering five cardiac episodes during 1994-2006. The study processed ~1.5M detailed metabolic conditions and lifestyle data (2012-2018) based on a math-physical medicine approach (mathematics, physics, engineering modeling, artificial intelligence or AI), rather than the traditional biochemical medicine method. The author defined two new terms: Metabolism Index (MI) and General Health Status Unit (GHSU) to evaluate a person’s overall metabolism and associated chronic diseases. The research steps are 1. observing the patient’s physical phenomena and metabolic changes, collecting relevant big data. 2. building up engineering models and deriving inter-relationship equations, applying statistics tools for variance study. 3. using machine learning and AI to predict important metabolic changes.
Results: Metabolic factors and health symptoms show some key results within eight years.
Conclusions: This math-physical medicine approach has proven the close relationship between metabolic changes quantitatively
due to the improvement of lifestyle management and chronic disease conditions.

  • Computational Methodologies | Systems Biology | Metabolomics
Location: Athens
Speaker

Chair

Tao Huang

Shanghai Institutes for Biological Sciences, China

Speaker

Co-Chair

Andrea Ragusa

University of Salento, Italy

Session Introduction

Abdullah Basoglu

Selcuk University, Turkey

Title: Effects of boron supplementation on peripartum dairy cows’ health: A metabolomic approach

Time : 12:15-12:45

Speaker
Biography:

Abdullah Basoglu, Prof.Dr has been working at Selcuk University, as an expert on NMR based matabolomics, boron metabolism and metabolic diseases seen in animals.He coordinated some proposals related to metabolomics at University of Florence, CERM in Italy.

Abstract:

Although many different dietary studies on the prevention of negative energy balance-related diseases are often encountered, this is the first study investigating the effects of boron supplementation on peripartum dairy cows’ health in the light of an omics approach. Twenty-eight healthy cows (1 control and 3 experimental groups) were enrolled from 2 months before predicted calving until 2 months after calving. Experimental groups were assigned to receive boron at increasing doses as an oral bolus. Production parameters, biochemical profile, Nuclear magnetic resonance-based metabolomics profile, and mRNA abundance of gluconeogenic enzymes and lipid oxidation genes were determined. Pivotal knowledge was obtained on boron distribution in the body. Production parameters and mRNA abundance of the genes were not affected by the treatments. Postpartum nonesterified fatty acids, β-hydroxybutyrate, and triglyceride concentrations were significantly decreased in experimentals. The primary differences among groups were in lipid-soluble metabolites. There were significant differences in metabolites including postpartum valine, β-hydroxybutyrate, polyunsaturated fatty acid and citrate, propionate, isobutyrate, choline metabolites (betaine, phosphatidylcholine, and sphingomyelin) and some types of fatty acids and cholesterol in experimentals. Boron appears to be effective in minimizing negative energy balance and improving health of postpartum dairy cows.

Alla Karnovsky

University of Michigan, USA

Title: Tools for data-driven network analysis of metabolomics data

Time : 12:45 - 13:15

Speaker
Biography:

Alla Karnovsky pursued her PhD in Cell and Developmental Biology from the Russian Academy of Sciences, Russia. She did her Postdoctoral work at the University of Colorado Boulder, USA followed by nine years of bioinformatics work in pharmaceutical industry. Currently, she is a Research Assistant Professor of Computational Medicine and Bioinformatics at the University of Michigan, USA. Her research interests involve the analysis of high throughput omics data, focusing primarily on metabolomics, and the development of computational methods and tools for the analysis and integration of metabolomics data with other types of genomic data.

Abstract:

Metabolomics has established itself as a powerful platform to interrogate cellular biochemistry, identify novel biomarkers and provide insights into biochemical mechanisms of disease. As metabolomics data sets become increasingly large and complex, there is a growing need for data analysis and visualization tools to help interpret experimentally observed changes and put them into relevant biological context. A common approach to interpreting the results of metabolomics and lipidomics experiments is to map and visualize experimentally measured metabolites in the context of known biochemical pathways. Several tools for performing this type of analysis have been developed including our own tool Metscape. Some tools have adopted Functional Enrichment Testing methods developed for gene expression data for the analysis of metabolomics data. However, the scope of their application has been limited to known compounds from large, well-annotated pathways, which are often occupied by a small portion of the experimentally measured metabolome. An alternative to knowledge-based data analysis is to infer meaningful associations between metabolites/lipids from experimental data and build data-driven metabolic networks to help generate biological insights. We developed a new Differential Network Enrichment Analysis method (DNEA) that uses joint structural sparsity estimation to build partial correlation networks from the data (for two or more experimental conditions), performs consensus clustering to identify highly connected network components (subnetworks) and uses Network-based Gene Set Analysis (NetGSA) to identify the differentially enriched subnetworks. We will present the applications of DNEA for the analysis of metabolomics and lipidomics data and demonstrate that it allows identifying alterations in both network structure and expression levels of interacting biomolecules that impact disease phenotypes.

Break: Lunch Break 13:20-14:20 @ La Place AB

Yan Ji

Shanghai Institutes for Biological Sciences, China

Title: SR splicing, splicing-ratio based splicing detection method

Time : 14:20-14:50

Speaker
Biography:

Yan Ji obtained his PhD Degree in Bioinformatics from Shanghai Institutes for Biological Science of the Chinese Academy of Sciences, P R China. Before May 2015, he worked as a Translational Researcher at the Innovation Center of China, AstraZeneca (a pharmaceutical company). Since June 2015, he has been an Assistant Professor in Shanghai Institutes for Biological Science of the Chinese Academy of Sciences, P R China and studied some tumour types which are prevalent in China by using high-throughput data analysis from different perspectives including splicing and long non-coding RNAs. He has published more than 8 papers in reputed journals. In this conference, he will present a novel method detecting splicing events which has been successfully used to discover PTEN-regulated splicing events.

Abstract:

Through alternative splicing, most human genes express multiple isoforms that often differ in function. To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), a pipeline is developed to detect differential alternative splicing events between RNA-Seq samples of treatment and control conditions. It is based on a metric defined as splicing ratio (SR) which was used in some splicing-related studies (SR Splicing is flexible to handle different types of study design. Either control group or treatment group can have single or replicated/pooled samples. It can analyze all major types
of alternative splicing patterns and use RNA-Seq reads and use RNA-Seq reads mapped to splice junctions. By comparing with other commonly used software, the performance of SR Splicing is evaluated from three aspects: sensitivity, accuracy, and validation. SR Splicing has comparable sensitivity and higher accuracy. Experimental validation using qRT-PCR (quantitative real time polymerase chain reaction) confirmed a selected set of splicing events that are significantly changed in Pten knock out data of the mouse MEF cell line, demonstrating the utility of the approach applied on experimental biological data sets.SR Splicing used different statistics and less filtering to return a list of significantly changed splicing events, with associated p values and false discovery corrections. It includes detailed information on the detected splicing differences such as which exon/ junctions are involved, alternative splice type (skipped exon, mutually exclusive exons, retained intron, alternative 5' splice site, and alternative 3' splice site), magnitude of difference, and coverage.

Maroun Bou Sleiman

Swiss Federal Institute of Technology in Lausanne, Switzerland

Title: Enabling discovery through multi-omics and systems genetics

Time : 14:50-15:20

Speaker
Biography:

Maroun Bou Sleiman obtained his PhD in the Laboratories of Professor Bart Deplancke and Professor Bruno Lemaitre, working on the genetic and molecular bases of infection in the Drosophila Genetic Reference Panel. He is a Scientist in the Laboratory of Integrative Systems Physiology headed by Professor Johan Auwerx at the Swiss Federal Institute of Technology (EPFL) in Lausanne (Switzerland). He works at the frontier between genetics, systems biology, and multi-omics in order to understand complex traits and diseases in different panels such as the BXD, HDP, and ITP in mice, as well as Caenorhabditis elegans RIAILs (recombinant inbred advanced intercross lines). These systems genetics approaches pave the way for a better mechanistic understanding of complex biological systems and pave the way for mechanistically-informed precision medicine.

Abstract:

Our laboratory is using systems approaches to map the signaling networks that govern mitochondrial function and as such regulate organismal metabolism in health, aging and disease. We apply a state-of-the-art biological toolkit to study a variety of model systems, ranging from the plant Arabidopsis thaliana, over the nematode Caenorhabditis elegans, to the mouse and all the way to humans. Our research has not only allowed the development of new methodologies and scientific approaches applied to population, as exemplified by the development of cross-species multi-layered genetics/omics gene mapping strategies, but also contributed to improved understanding of how signaling pathways control mitochondrial function and metabolism. Although our research addresses basic biomedical questions, we aim at translating our research into novel preventive and therapeutic strategies for common diseases, such as type 2 diabetes, frailty, and obesity, as well as rare inherited mitochondrial diseases. The translational value of our work is testified by the fact that several drugs targeting processes and pathways which we elucidated are currently used in the clinic.

Ryohei Eguchi

Nara Institute of Science and Technology, Japan

Title: Classification of biosynthesis pathways of alkaloids using graph convolutional neural networks

Time : 15:20-15:50

Speaker
Biography:

Ryohei Eguchi obtained his Doctor’s Degree in Engineering at the Graduate School of Information Science of Nara Institute of Science and Technology, Japan
(2018) and in the same year joined the institute as a Doctoral Researcher at the Data Driven Science Creation Center. He is involved in the research on gene expression analysis and metabolic pathway prediction of secondary metabolites by applying multivariate analysis and machine learning techniques, mainly in the
field of bioinformatics and chemoinformatics.

Abstract:

Secondary metabolites are defined as a group of natural compounds that are not directly involved in growth, development or reproduction of organisms. The term ‘secondary’ in the context of metabolic pathways, was introduced in 1891. The functions of secondary metabolites are related to their prevalent potent biomolecular activity acquired in evolution process involving pest and pathogen defense, UV-B-sunscreens etc. The classification of secondary metabolites taking chemical structure and metabolic pathway into consideration could provide important clues to activities of metabolites which lead to interpretation of function acquisition mechanisms of secondary metabolites in evolutional process. And, various chemical descriptors such like molecular fingerprints have been long discussed to represent biochemical features, in order to embed molecular structures into a numerical space and quantify their activities. However, it is still difficult to predict bioactivities from molecular structures since it depends on the choices of those chemical descriptors. Recently, machine learning methods based on Graph Convolutional Neural Networks (GCNN) have been proposed that can automatically optimize a model for molecular feature extraction from the given training sets. This study introduces an application of GCNN to predict metabolic pathways of alkaloids, namely, one of the largest family of secondary metabolites in plants. The author trained and tested GCNN model on 578 alkaloid compounds and the mean accuracy of 20 runs with random sampling is about 94% (Number of epoch: 200). It is greatly expected that it will lead to an understanding of the evolution of metabolic system unique to organisms.

Break: Networking & Refreshments 15:50-16:10 @ Europa Foyer
  • Metabolomics in Drug Discovery | Cancer Metabolomics | Transcriptomics and Proteomics
Location: Athens
Speaker

Chair

Philip J Jackson

University of Sheffield, UK

Speaker

Co-Chair

Eloiza H Tajara

University of São Paulo, Brazil

Session Introduction

William Hidalgo

Industrial University of Santander, Colombia

Title: Transcriptomic and metabolic analysis of the plant hormones signalling pathways in the resistant “Calcutta 4” and susceptible “Williams” banana genotypes during the interaction with the pathogenic fungus Pseudocercospora fijiensis

Time : 12:35-13:05

Speaker
Biography:

William Hidalgo pursued Master of Science Degree in Biotechnology from National University of Colombia (Colombia). He is a Chemist from University of Nariño (Colombia); Doctor in Natural Science from Friedrich Schiller University Jena (Germany). Since March 2017, he joined the Industrial University of Santander (Bucaramanga, Colombia) as an Assistant Lecturer and Researcher in the Chemistry School. His scientific background is focused on the study of plant chemical defenses especially in the biological system “Musa-Mycosphaerella fijiensis” (causing the Black Sigatoka Disease in Banana plants), biosynthesis of natural products (by using 13C-tracer) and metabolomics studies by using NMR and MS spectrometry techniques. He has participated in several international conferences organized mainly by the International Society of Chemical Ecology and Latin American Association of Chemical Ecology, with a scientific contribution of ten international publications.

Abstract:

Statement of the Problem: Bananas (Musa spp.) represent one of the most important crops throughout the world. Black Sigatoka disease (BSD), caused by the ascomycete fungus Pseudocercospora fijiensis, is still one of the main phytosanitary problems facing the crop.
Methodology: In order to understand the physiological mechanism behind the plant defences in Musa during the interaction with P. fijiensis, a transcriptomic and metabolomic analysis were led with two Musa genotypes differing in their resistance to the BSD development: The susceptible genotype “Williams” and the resistant genotype “Calcutta 4”. RNA-seq with Illumina technology, Nuclear Magnetic Resonance and Mass Spectrometry were the analytical techniques used for sample analysis.
Findings: The results clearly showed a fast and early plant response in the resistant genotype “Calcutta 4”, mainly with the induction of a group of genes and metabolites involved in pathogen recognition, hormonal signal transduction and pathogenesis-related proteins whereas a poor induction of those physiological responses were detected in the susceptible genotype “Williams”.
Conclusion & Significance: Our results support new insights about the role of JA-Et signalling pathways play in the response of
the resistant banana genotype Calcutta 4 during the pathogen attack. Furthermore, it opens the possibility to explore whether
these defence mechanism reported here are similar for other resistant Musa genotypes.

Philip J Jackson

University of Sheffield, UK

Title: Proteomic adaptation in high and low light ecotypes of the marine picocyanobacterium Prochlorococcus marinus, an important global primary producer

Time : 13:05-13:35

Speaker
Biography:

Philip J Jackson obtained his PhD in Biochemistry from the University of Leeds, UK where his research is centered on the control of ATP synthesis in mitochondria.His subsequent postdoctoral positions, also in Leeds, involved the characterization of potential glycoprotein tumour biomarkers and the structural analysis of several membrane-intrinsic proteins including a proton-translocating ATPase. He then joined an instrumentation manufacturer as a product application specialist in proteomics and lipidomics. In 2010, he returned to Postdoctoral research to work in the Laboratories of Professors Neil Hunter, FRS and Mark Dickman at the University of Sheffield (UK). He specializes in biological mass spectrometry, applying this technology to (1) quantitative proteomics of complexes involved in light harvesting and energy transfer in photosynthesis, (2) analysis of protein-protein interactions within complexes that direct photosystem assembly and (3) structural characterization of the enzyme systems responsible for chlorophyll biosynthesis.

Abstract:

Prochlorococcus marinus, an oceanic phototrophic picocyanobacterium, is probably the most abundant organism on earth with an estimated population of 2.9±0.1 x 1027 cells. Consequently it is an important global primary producer responsible for the fixation of 4.0 x 106 tonnes of carbon per annum. In terms of habitat, Prochlorococcus has been recovered from diverse locations at depths down to 200 m. Given the wide distribution of Prochlorococcus within the euphotic zone, genetically distinct ecotypes have evolved in response to the level of sunlight penetration and nutrient availability. The four examples used for this study were: (1) MED4 (Mediterranean 5 m), (2) NATL2A (N. Atlantic 10 m), (3) SS120 (Sargasso 120 m) and (4) MIT9313 (Gulf Stream 135 m). Cells were grown under very low intensity illumination, similar to that experienced by the deep water ecotypes. After isolating thylakoid membranes, proteins were extracted, digested with endoproteinase Lys-C and trypsin, then analyzed by nanoLC-MS/MS. Identified proteins were quantified by the label-free iBAQ method which was validated by the expected PSI:PSII ratio of 3-4 for Synechocystis as determined by spectroscopy. The Prochlorococcus ecotypes showed markedly different PSI:PSII ratios to Synechocystis: near to 1:1 in MED4 and MIT9313, and 0.5 in NATL2A and SS120. Therefore there appears not to be a simple relationship between PSI:PSII ratio and illumination. On the other hand, amounts of high light inducible proteins (HLIPs), associated with photoprotection, and the relative amounts of light dependent and independent POR enzymes, occurring in the chlorophyll biosynthesis pathway did reflect the expected habitat illumination levels in the Prochlorococcus ecotypes and Synechocystis. As the Prochlorococcus ecotypes were grown under the same low light intensity, the expression patterns observed in this study appear to be an inherent feature of ecotypic adaptation to light intensity within their specific habitats.

Break: Lunch Break 13:35-14:35 @ La Place AB

Masahiro Onuma

Trisguide CO.,LTD, Japan

Title: Electronic water can reduce oxidative stress in cancer and diabetes patients for 3 weeks drinking

Time : 14:35-15:05

Speaker
Biography:

Masahiro Onuma pursued his Bachelors in Biochemistry from Tokyo University of Agriculture and Technology. He is the President of TriSGuide Ltd. He has expertise in oxidative disease prevention to use non-medical product based on GSK’s experience of allopurinol which is the strongest anti-oxidant in this world.

Abstract:

Oxidative stress means a state where there is an imbalance between the oxidizing action and the reducing action due to reactive oxygen species (ROS) in a living body, resulting in the oxidizing action becoming dominant. Oxidative stress arises as the balance between production and removal is disrupted through excessive production of ROS and impairment of the antioxidant system. Oxidative stress has been reported to be involved in the onset and progress of various diseases. Characteristics of Type 2 diabetes are insulin secretion failure and insulin resistance, but it seems that oxidative stress is greatly involved in insulin secretion failure. In the insulin secretion-inducing β cells of Langerhans islets in the pancreas, the amount of superoxide dismutase (SOD), which is representative of the ROS elimination system, is small and resistance to oxidative stress is considered to be weak. Regarding cancer, it is well known that chronic inflammatory conditions increase the risk of carcinogenesis. Cells
such as neutrophils and macrophages are activated in the inflammation area leading to increase in production of active oxygen and nitric oxide. These free radicals cause DNA mutation and cell proliferation thereby promoting cancer development. When chronic inflammation is present, cancer develops more easily. Electronic water, which was developed to generate electron in water, was consumed for three weeks, after meals, between meals and before sleeping 6 times a day, and according to the test subjects' possible time periods. The amount of drinking water was 750-1000 mL, and BAP and d-ROMs checks for all cases were carried out at 4:30 pm. The results of cancer patients and diabetes patients were seen as attached. As a result, the d-ROMs value in the degree of oxidative stress has reduced, and the BAP value, which is an indicator of plasma antioxidant capacity, has improved
significantly.

Halina Malina

Axanton Technology GmbH, Switzerland

Title: Elimination the chemically modified proteins from the cell membranes prevents the cell degeneration

Time : 15:05-15:35

Speaker
Biography:

Halina Malina pursued her PhD in antibiotic biosynthesis working at the Medical School of Lodz (Poland). She is a Chemical Engineer from Polytechnic of Lodz, Lodz. Since 1984, she worked at the Institute of Chemistry of Natural Substances (CNRS), with collaboration with Pasteur Institute of Paris (France). In 1990 she worked as project leader in CNRS University in Zurich. She discovered the IDO in the eye and the role of xanthurenic acid in the cataract development. She followed the research on xanthurenic acid-induced cell pathology at universities in Lausanne, and Bern and ETH. Her research on the chemical mechanism of the diseases with aging met a strong opposition of the academia healing the transgenic mice. All support for research was rejected in 2004. She continued on her own and established the technology curing the infection and the aging-associated pathology.

Abstract:

Statement of the Problem: Most young people are healthy, and with aging, they develop the degenerative diseases. In the last thirty years, it was thought that the disorders in aging are genetic and cannot be prevented. We present here the proof of concept that the aging-associated diseases are not genetic, but develops because of the chemically modified proteins. The discovery leads to effective prevention of the cell degeneration in aging and infection permitting to keep people healthy longer and improving the quality of life.
Methodology & Theoretical Orientation: The peptides corresponding to the intrinsically disordered sequences were synthesized in vitro and incubated with xanthurenic acid to obtain the covalently modified polymers observed on the SDSPAGE (patents). The polymers were injected into rabbits. The IgG's were isolated and tested in the primary cell cultures, in vivo in mice (outsourcing) and human, directly used by the investigator for her dermatitis and the metabolic disorder.
Findings: In a primary cell culture Xan led to the cell death caused by the covalently modified proteins. The regulatory sequences of the proteins, called intrinsically disordered sequences (IDSeqs) are preferentially modified. Any cell system cannot remove the chemically modified and polymerized IDSeqs. They alter the cell membranes leading to the caspases activation and cell degeneration.The author established a new technology targeting the chemically modified proteins. In mice, the molecule establishing membranes (MEMS) healed the Staphylococcus aureus and restored immunity in cyclophosphamide-treated mice using MEMS A-144 at five micrograms, weekly, on the mouth mucosa cured skin infection and cardiovascular disorder.
Conclusion & Significance: The chemically modified proteins must be eliminated to restore the cell homeostasis. MEMS stops the upstream cause of the pathology in infection or aging.

Vivek Kamath

Heal The World, India

Title: Case study of insulin dependent diabetes: Healing without medicines

Time : 15:35-16:05

Speaker
Biography:

Vivek Kamath is the founder of heal the world organization is a Reiki Master, Mexican Healer, Melchizedek Healer, Crystal Healer, and Past Life Regression Therapy Expert. He has healed many diabetic patients (Type1, Type2, Type 3/1.5/LADA) without any medicines. He has also healed Cancer (stage 4) blood pressure (both high and low blood pressure), heart disease (removed the heart blockages), removed kidney stones, ovarian cysts, fibrosis of the breast, fatty liver, lungs disease, cured sinusitis, severe joint pain, lumbar L5 spinal disk pain, Sciatica pain, neck pain, constipation, rheumatoid arthritis, glaucoma, migraines, headaches, insomnia, stomach related problem, IBS, diabetic gum problems, skin problems( dry skin, eczema) and chronic nasal allergies, nasal blockages without any medicines. Some of the above treatments have been completed within a week to maximum 1-month duration.

Abstract:

As most of us are aware Type 2 diabetes can be controlled and cured completely with the diet, workouts (yoga), effective stress management and other healing methods. Testimonial of one of my patient aged 70 years, male suffering from diabetes from more than 12 years and took 3 insulin doses per day. He has completed Reiki Master Degree through some Reiki Master in India. He came to be with the problem that he is not able to feel the energy while doing his self healing. Moreover, he was suffering major health problems like (Insulin dependent diabetes for nearly 12 years, insomnia, constipation, stomach heaviness, etc.). I gave him Reiki Level 1 attunement a month back. On 25th July he has taken his random glucose reading. The random glucose level was 575 mg/dl. On July 27th I have given him Usui Reiki Level 2, 3 Attunement, Karuna Reiki Attunement and Mexican Healing attunement (Guru Poornima Day). Immediately after the attunement we checked his random glucose level it came down to 383 mg/Dl. After 1 Mexican Healing: On 28th July evening, I conducted Mexican healing training class to my patient along with others. I took his random glucose level before giving him Mexican healing and it was 344 mg/Dl. After 1 Mexican healing his glucose came down to 260 mg/Dl. All the people who attended the training class were astonished with the power of Mexican healing. To summarize in 3 days, using attunement (Reiki and Mexican attunement) and with 1 Mexican healing, his glucose level came down from 575 mg/dL to 260 mg/DL. What is the percentage in drop down? 54.8%. Is possible to bring this much of level down in 3 days without taking insulin? Please note that there is no change in his food/diet or workouts. Also note the difference between the readings after attunement and after healing. We are all made up of energy and it flows though the energy centers (chakra). We can’t necessarily see energy, but we can feel the energy. When our energy centers becomes weak, diseases related to corresponding energy centers will pop up. For Diabetes, there is energy center called “Solar Plexus” (Also called as Manipura Chakra). All diabetic patients will have low energy in their solar plexus energy. If you energize this energy center you will see the drastic reduction in your glucose level. You do not need to go for a pancreas transplant or tablets/insulin to treat your diabetes. It is all in the energy. Learn from your Reiki master or healers to energize all your weaker organs or energy centers.

Break: Networking & Refreshments 16:05-16:25 @ Europa Foyer