Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

MOTIVATION: Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. RESULTS: We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. AVAILABILITY AND IMPLEMENTATION: The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

[Liver transplantation in aged patients]. / Lebertransplantation bei alten Patient:innen.

Due to the demographic changes and the increasing incidence of chronic, especially nutritively toxic liver diseases, the number of patients over 65 years of age with indications for liver transplantation is rising considerably. Patient age alone is not a contraindication for organ transplantation; however, in order to ensure the postoperative outcome, a structured interdisciplinary assessment is necessary, especially in older potential organ recipients. With knowledge of comorbidities, individualized prehabilitation enables the perioperative risk to be minimized. The postoperative morbidity in aged patients appears to be comparable to that of younger patients, especially after careful evaluation. Overall, there is a clear survival advantage compared with the best conservative treatment for liver disease. In addition to the perioperative procedure, differences in follow-up care and long-term outcome should also be considered. In this context, predominantly the pharmacological peculiarities, such as polypharmacy and the mutual influence of immunosuppression and comorbidities, have to be taken into account. In addition to old organ recipients, livers from old donors (so-called marginal organs) increasingly play a crucial role in transplantation medicine due to the organ shortage. These are more susceptible to ischemia reperfusion injury and thus put the recipient at a higher risk for delayed or lack of organ function recovery. New ethical issues are raised by the increasing age of donors and recipients, complicating decision making about organ acceptance or rejection for the transplantation physician.