Supporting regional pandemic management by enabling self-service reporting-A case report

BACKGROUND

The COVID-19 pandemic revealed a need for better collaboration among research, care, and management in Germany as well as globally. Initially, there was a high demand for broad data collection across Germany, but as the pandemic evolved, localized data became increasingly necessary. Customized dashboards and tools were rapidly developed to provide timely and accurate information. In Saxony, the DISPENSE project was created to predict short-term hospital bed capacity demands, and while it was successful, continuous adjustments and the initial monolithic system architecture of the application made it difficult to customize and scale.

METHODS

To analyze the current state of the DISPENSE tool, we conducted an in-depth analysis of the data processing steps and identified data flows underlying users' metrics and dashboards. We also conducted a workshop to understand the different views and constraints of specific user groups, and brought together and clustered the information according to content-related service areas to determine functionality-related service groups. Based on this analysis, we developed a concept for the system architecture, modularized the main services by assigning specialized applications and integrated them into the existing system, allowing for self-service reporting and evaluation of the expert groups' needs.

RESULTS

We analyzed the applications' dataflow and identified specific user groups. The functionalities of the monolithic application were divided into specific service groups for data processing, data storage, predictions, content visualization, and user management. After composition and implementation, we evaluated the new system architecture against the initial requirements by enabling self-service reporting to the users.

DISCUSSION

By modularizing the monolithic application and creating a more flexible system, the challenges of rapidly changing requirements, growing need for information, and high administrative efforts were addressed.

CONCLUSION

We demonstrated an improved adaptation towards the needs of various user groups, increased efficiency, and reduced burden on administrators, while also enabling self-service functionalities and specialization of single applications on individual service groups.

Hyperspectral Imaging during Normothermic Machine Perfusion—A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks

Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550–995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision.

Strengthening regulatory science in academia: STARS, an EU initiative to bridge the translational gap

Truly disruptive medicine innovation and new treatment paradigms tend to start in non-commercial research institutions. However, the lack of mutual understanding between medicine developers and regulators when it comes to medicine development significantly delays or even prevents the access of patients to these innovations. Here, we outline what regulatory-related barriers hamper the translational development of novel products or new treatment paradigms initiated in academia, and propose key steps towards improved regulatory dialogue among academia, funding bodies and regulatory authorities. Moreover, we briefly describe how the STARS (Strengthening Training of Academia in Regulatory Science) project aims to reach out to medicine innovators in academia to bridge the regulatory knowledge gap and enhance this dialogue to facilitate the implementation of academic research findings in clinical practice.

Fusion of visual and infrared thermography images for advanced assessment in non-destructive testing

For better evaluation of infrared measurements in non-destructive testing, especially for objects with complex geometry or small dimensions, it is beneficial to combine with the same viewing angle an image of a camera in the visible range with the image of an infrared camera. In the hybrid camera developed by us, a beam splitter is used which combines the visible and the infrared wavelength regions under the same viewing angle to form a hybrid image. The applications of this new technique range from the localization and the verification of false indications in non-destructive testing applications to the retrieval of 3D surface information with a hybrid picture as texture with defect indications and the filtering of laser markings displayed in the IR image to area and process monitoring.

Calcium rubidium nitrate: mode-coupling beta scaling without factorization

The fast dynamics of viscous calcium rubidium nitrate is investigated by depolarized light scattering, neutron scattering, and dielectric loss. Fast beta relaxation evolves as in calcium potassium nitrate. The dynamic susceptibilities can be described by the asymptotic scaling law of mode-coupling theory with a shape parameter lambda=0.79; the temperature dependence of the amplitudes extrapolates to T(c) approximately equal 378 K. However, the frequencies of the minima of the three different spectroscopies never coincide, in conflict with the factorization prediction, indicating that the true asymptotic regime is unreachable.