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.

Analysis of the mouse proteome. (I) Brain proteins: separation by two-dimensional electrophoresis and identification by mass spectrometry and genetic variation

The total protein of the mouse brain was fractionated into three fractions, supernatant, pellet extract and rest pellet suspension, by a procedure that avoids any loss of groups or classes of proteins. The supernatant proteins were resolved to a maximum by large-gel two-dimensional electrophoresis. Two-dimensional patterns from ten individual mice of the commonly used inbred strain C57BL/6 (species: Mus musculus) were prepared. The master pattern was subjected to densitometry, computer-assisted image analysis and treatment with our spot detection program. The resulting two-dimensional pattern, a standard pattern for mouse brain supernatant proteins, was divided into 40 squares, calibrated, and specified by providing each spot with a number. The complete pattern and each of the 40 squares are shown in our homepage (http://www.charite.de/ humangenetik). The standard pattern comprises 8767 protein spots. To identify the proteins known so far in the brain fraction investigated, a first set of 200 spots was analyzed by matrix-assisted laser desorption/ionization - mass spectrometry (MALDI-MS) after in-gel digestion. By screening protein databases 115 spots were identified; by extending the analysis to selected, genetically variant protein spots, 166 spots (including some spot series) were identified in total. This number was increased to 331 by adding protein spots identified indirectly by a genetic approach. By comparing the two-dimensional patterns from C57BL/6 mice with those of another mouse species (Mus spretus), more than 1000 genetically variant spots were detected. The genetic analysis allowed us to recognize spot families, i.e., protein spots that represent the same protein but that are post-translationally modified. If some members of the family were identified, the whole family was considered as being identified. Spot families were investigated in more detail, and interpreted as the result of protein modification or degradation. Genetic analysis led to the interesting finding that the size of spot families, i.e., the extent of modification or degradation of a protein, can be genetically determined. The investigation presented is a first step towards a systematic analysis of the proteome of the mouse. Proteome analysis was shown to become more efficient, and, at the same time, linked to the genome, by combining protein analytical and genetic methods.

Identification of mouse crystallins in 2D protein patterns by sequencing and mass spectrometry. Application to cataract mutants

The eye lens proteins of the mouse were separated into 1940 polypeptide spots by two-dimensional electrophoresis in large gels. All 16 crystallins ubiquitous in mammals were identified by protein sequencing and mass spectrometry except for (gamma)-F, which shows an almost identical sequence with (gamma)-E. Two crystallins, (beta)-A2 and (gamma)-S, were shown for the first time to occur in the mouse lens. An investigation of the murine cataract mutant Cat2(nop)((gamma)-B gene) demonstrated that a monogenic mutation might affect a broad spectrum of proteins.