Semi-Automatic Export of Electrophysiological Metadata to NFDI4Health Local Data Hubs: Use Case of Microneurography odML-Tables - A Technical Case Report.

INTRODUCTION: The Local Data Hub (LDH) is a platform for FAIR sharing of medical research (meta-)data. In order to promote the usage of LDH in different research communities, it is important to understand the domain-specific needs, solutions currently used for data organization and provide support for seamless uploads to a LDH. In this work, we analyze the use case of microneurography, which is an electrophysiological technique for analyzing neural activity. METHODS: After performing a requirements analysis in dialogue with microneurography researchers, we propose a concept-mapping and a workflow, for the researchers to transform and upload their metadata. Further, we implemented a semi-automatic upload extension to odMLtables, a template-based tool for handling metadata in the electrophysiological community. RESULTS: The open-source implementation enables the odML-to-LDH concept mapping, allows data anonymization from within the tool and the creation of custom-made summaries on the underlying data sets. DISCUSSION: This concludes a first step towards integrating improved FAIR processes into the research laboratory's daily workflow. In future work, we will extend this approach to other use cases to disseminate the usage of LDHs in a larger research community.

NFDI4Health Local Data Hubs Implementing a Tailored Metadata Schema for Health Data.

INTRODUCTION: NFDI4Health is a consortium funded by the German Research Foundation to make structured health data findable and accessible internationally according to the FAIR principles. Its goal is bringing data users and Data Holding Organizations (DHOs) together. It mainly considers DHOs conducting epidemiological and public health studies or clinical trials. METHODS: Local data hubs (LDH) are provided for such DHOs to connect decentralized local research data management within their organizations with the option of publishing shareable metadata via centralized NFDI4Health services such as the German central Health Study Hub. The LDH platform is based on FAIRDOM SEEK and provides a complete and flexible, locally controlled data and information management platform for health research data. A tailored NFDI4Health metadata schema for studies and their corresponding resources has been developed which is fully supported by the LDH software, e.g. for metadata transfer to other NFDI4Health services. RESULTS: The SEEK platform has been technically enhanced to support extended metadata structures tailored to the needs of the user communities in addition to the existing metadata structuring of SEEK. CONCLUSION: With the LDH and the MDS, the NFDI4Health provides all DHOs with a standardized and free and open source research data management platform for the FAIR exchange of structured health data.

Curcumin as a natural potential drug candidate against important zoonotic viruses and prions: A narrative review.

Zoonotic diseases are major public health concerns and undeniable threats to human health. Among Zoonotic diseases, zoonotic viruses and prions are much more difficult to eradicate, as they result in higher infections and mortality rates. Several investigations have shown curcumin, the active ingredient of turmeric, to have wide spectrum properties such as anti-microbial, anti-vascular, anti-inflammatory, anti-tumor, anti-neoplastic, anti-oxidant, and immune system modulator properties. In the present study, we performed a comprehensive review of existing in silico, in vitro, and in vivo evidence on the antiviral (54 important zoonotic viruses) and anti-prion properties of curcumin and curcuminoids in PubMed, Google Scholar, Science Direct, Scopus, and Web of Science databases. Database searches yielded 13,380 results, out of which 216 studies were eligible according to inclusion criteria. Of 216 studies, 135 (62.5%), 24 (11.1%), and 19 (8.8%) were conducted on the effect of curcumin and curcuminoids against SARS-CoV-2, Influenza A virus, and dengue virus, respectively. This review suggests curcumin and curcuminoids as promising therapeutic agents against a wide range of viral zoonoses by targeting different proteins and signaling pathways.

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis.

BACKGROUND: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. RESULTS: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. CONCLUSION: MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis.

Background: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. Results: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. Conclusion: MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis.

Background: In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

BACKGROUND: In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

MLcps: machine learning cumulative performance score for classification problems.

BACKGROUND: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. RESULTS: We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. CONCLUSIONS: By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/.