TY - JOUR A1 - Amro, Ahmad A1 - Moskalenko, Olga A1 - Hamarsheh, Omar A1 - Frohme, Marcus T1 - Spatiotemporal analysis of cutaneous leishmaniasis in Palestine and foresight study by projections modelling until 2060 based on climate change prediction JF - PLoS ONE N2 - Background Cutaneous leishmaniasis (CL) is a vector-borne parasitic diseases of public health importance that is prevalent in the West Bank but not in the Gaza Strip. The disease caused by parasitic protozoans from the genus Leishmania and it is transmitted by infected phlebotomine sand flies. The aim of our study is to investigate the eco-epidemiological parameters and spatiotemporal projections of CL in Palestine over a 30-years period from 1990 through 2020 and to explore future projections until 2060. Methodology/Principal findings This long-term descriptive epidemiological study includes investigation of demographic characteristics of reported patients by the Palestinian Ministry of Health (PMoH). Moreover, we explored spatiotemporal distribution of CL including future projection based on climate change scenarios. The number of CL patients reported during this period was 5855 cases, and the average annual incidence rate (AAIR) was 18.5 cases/105 population. The male to female ratio was 1.25:1. Patients-age ranged from 2 months to 89 years (mean = 22.5, std 18.67, and the median was 18 years). More than 65% of the cases came from three governates in the West Bank; Jenin 29% (1617 cases), Jericho 25% (1403), and Tubas 12% (658) with no cases reported in the Gaza Strip. Seasonal occurrence of CL starts to increase in December and peaked during March and April of the following year. Current distribution of CL indicate that Jericho, Tubas, Jenin and Nablus have the most suitable climatic settings for the sandfly vectors. Future projections until 2060 suggest an increasing incidence from northwest of Jenin down to the southwest of Ramallah, disappearance of the foci in Jericho and Tubas throughout the Jordan Vally, and possible emergence of new foci in Gaza Strip. Conclusions/Significance The future projection of CL in Palestine until 2060 show a tendency of increasing incidence in the north western parts of the West Bank, disappearance from Jericho and Tubas throughout the Jordan Vally, and emergence of new CL endemic foci in the Gaza Strip. These results should be considered to implement effective control and surveillance systems to counteract spatial expansion of CL vectors. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16166 SN - 1932-6203 VL - 17 IS - 6 PB - Public Library of Science (PLoS) ER - TY - JOUR A1 - Kuhls, Katrin A1 - Moskalenko, Olga A1 - Sukiasyan, Anna A1 - Manukyan, Dezdemonia A1 - Melik-Andreasyan, Gayane A1 - Atshemyan, Liana A1 - Apresyan, Hripsime A1 - Strelkova, Margarita V. A1 - Jaeschke, Anja A1 - Wieland, Ralf A1 - Frohme, Marcus A1 - Cortes, Sofia A1 - Keshishyan, Ara T1 - Microsatellite based molecular epidemiology of Leishmania infantum from re-emerging foci of visceral leishmaniasis in Armenia and pilot risk assessment by ecological niche modeling JF - PLoS Neglected Tropical Diseases N2 - Leishmaniasis is a vector-borne disease caused by protozoan parasites of the genus Leishmania. In Armenia visceral leishmaniasis (VL) is re-emerging since 1999 after a long break of 30 years, with 167 cases recorded until 2019. Molecular diagnosis of VL was implemented only in 2016, and the causative agent was identified as L. infantum. In the present study we expanded the investigation of the causative agent to a characterization at strain level and the identification of its phylogenetic position among the L. infantum genotypes circulating worldwide. This is the first study addressing genetic diversity and population structure of L. infantum in Armenia and in Transcaucasia. Armenia is an extremely interesting region due to its bio-geographic specificities e.g. the high number of different climates in this small mountainous country and the observed high diversity of sand fly species, part of which occurring in very high altitudes. Ecological niche modeling based on registered VL cases and sand fly vectors collected in active VL foci revealed that the risk of further spread of VL is very high due to climate change. Studies of this region should be expanded to enable targeted control measures. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-14004 SN - 1935-2735 VL - 15 IS - 4 SP - e0009288 PB - Public Library of Science (PLoS) ER - TY - JOUR A1 - Siptroth, Julienne A1 - Moskalenko, Olga A1 - Krumbiegel, Carsten A1 - Ackermann, Jörg A1 - Koch, Ina A1 - Pospisil, Heike T1 - Variation of butyrate production in the gut microbiome in type 2 diabetes patients JF - International Microbiology N2 - Background Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student’s t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. Results By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. Conclusions With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease. KW - NGS KW - type 2 diabetes KW - gut KW - butyrate KW - statistical analysis Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17802 VL - 26 SP - 601 EP - 610 PB - Springer Nature ER - TY - JOUR A1 - Siptroth, Julienne A1 - Moskalenko, Olga A1 - Krumbiegel, Carsten A1 - Ackermann, Jörg A1 - Koch, Ina A1 - Pospisil, Heike T1 - Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks JF - Discover Artificial Intelligence N2 - Background Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients. Methods Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways. Results It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674). Conclusion With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis. KW - type 2 diabetes mellitus KW - gut KW - NGS KW - artificial neural network KW - machine learning KW - explainable artificial intelligence (XAI) Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17497 SN - 2731-0809 VL - 3 IS - 1 PB - Springer Nature ER - TY - JOUR A1 - Amro, Ahmad A1 - Al-Dwibe, Hamida A1 - Gashout, Aisha A1 - Moskalenko, Olga A1 - Galafin, Marlena A1 - Hamarsheh, Omar A1 - Frohme, Marcus A1 - Jaeschke, Anja A1 - Schönian, Gabriele A1 - Kuhls, Katrin T1 - Spatiotemporal and molecular epidemiology of cutaneous leishmaniasis in Libya JF - PLoS Neglected Tropical Diseases N2 - Cutaneous leishmaniasis (CL) is a skin infection caused by a single-celled parasite that is transmitted by the bite of a phlebotomine sandfly. CL is the most common form of leishmaniasis characterized by localized lesions in the skin and mucous membranes. The disease is prevalent in all countries around the Mediterranean Basin. In this paper, we describe spatiotemporal and eco-epidemiological parameters of CL in Libya. Moreover, we explored current spatiotemporal distributions of CL cases and explored the future projection of the disease. Our study indicates the presence of higher risk of CL in the coastal regions of Libya. Future projection until 2060 showed a trend of increasing incidence of CL in the north-western part of Libya, a spread along the coastal region and a possible emergence of new endemics in the north-eastern districts of Libya. These scenarios should be considered by health authorities in order to develop appropriate intervention strategies and plan effective control programs. Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-10054 SN - 1935-2735 VL - 11 IS - 9 ER - TY - JOUR A1 - Pfeil, Juliane A1 - Siptroth, Julienne A1 - Pospisil, Heike A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Moskalenko, Olga A1 - Yateem, Murad A1 - Nechyporenko, Alina T1 - Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition JF - Big Data and Cognitive Computing N2 - Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera. KW - human intestinal microbiome KW - next-generation sequencing KW - type 2 diabetes KW - deep learning KW - image classification Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17184 SN - 2504-2289 VL - 7 IS - 1 PB - MDPI ER -