TY - JOUR A1 - Pfeil, Juliane A1 - Frohme, Marcus A1 - Schulze, Katja T1 - Mobile microscopy for the examination of blood samples JF - EMBnet.journal N2 - The analysis of blood is one of the best possibilities to diagnose and control diseases and deficiency symptoms. Common blood tests that are performed in medical laboratories are time-consuming and work-intensive. In under-developed areas, there is often also a lack of specialised staff and facilities. The development of a mobile microscopic system that contains an automated image analysis and that can be used via a smartphone, could represent a valuable help to improve the diagnostic care, especially in those areas. it aims to enable a very fast, cheap, location- and knowledge-independent application for many use cases. Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-10509 SN - 2226-6089 VL - 23 SP - e904 ER - TY - JOUR A1 - Pfeil, Juliane A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Schulze, Katja T1 - Examination of blood samples using deep learning and mobile microscopy JF - BMC Bioinformatics N2 - Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15802 SN - 1471-2105 VL - 23 PB - BioMed Central ER - TY - CHAP A1 - Hollmann, Susanne A1 - Regierer, Babette A1 - D'Elia, Domenica A1 - Frohme, Marcus A1 - Gruden, Kristina A1 - Pfeil, Juliane A1 - Baebler, Spela A1 - Sezerman, Ugur A1 - Evelo, Chris T. A1 - Erhart, Friederike A1 - Huppertz, Berthold A1 - Bongcam-Rudloff, Erik A1 - Trefois, Christophe A1 - Gruca, Aleksandra A1 - Duca, Deborah A1 - Colotti, Gianni A1 - Merino-Martinez, Roxana A1 - Ouzounis, Christos A1 - Hunewald, Oliver A1 - He, Feng A1 - Kremer, Andreas ED - Kalajdziski, Slobodan ED - Ackovska, Nevena T1 - Standardisation in life-science research - Making the case for harmonization to improve communication and sharing of data amongst researchers N2 - Modern, high-throughput methods for the analysis of genetic information, gene and metabolic products and their interactions offer new opportunities to gain comprehensive information on life processes. The data and knowledge generated open diverse application possibilities with enormous innovation potential. To unlock that potential skills in generating but also properly annotating the data for further data integration and analysis are needed. The data need to be made computer readable and interoperable to allow integration with existing knowledge leading to actionable biological insights. To achieve this, we need common standards and standard operating procedures as well as workflows that allow the combination of data across standards. Currently, there is a lack of experts who understand the principles and possess knowledge of the principles and relevant tools. This is a major barrier hindering the implementation of FAIR (findable, accessible, interoperable and reusable) data principles and the actual reusability of data. This is mainly due to insufficient and unequal education of the scientists and other stakeholders involved in producing and handling big data in life science that is inherently varied and complex in nature, and large in volume. Due to the interdisciplinary nature of life science research, education within this field faces numerous hurdles including institutional barriers, lack of local availability of all required expertise, as well as lack of appropriate teaching material and appropriate adaptation of curricula. KW - FAIR data KW - standardization KW - interoperability KW - standard operating procedures (SOPs) KW - quality management (QM) KW - quality control (QC) KW - education Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-20118 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 -