Knowledge Discovery in Datasets of Proteomics by Systems Modeling in Translational Research on Pancreatic Cancer.

Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between and studies and clinical applications. Here, we propose a 'systems modeling' workflow for KDD. This framework includes data collection of composition model (various research models) and processing model (proteomics) and analytical model (bioinformatics, artificial intelligence/machine leaning and pattern evaluation), knowledge presentation, and feedback loops for hypothesis generation and validation.

Reducing time delays and enhancing reperfusion eligibility related to stroke suspicion by the Emergency Medical Dispatch Centre: a registry-based observational study.

Background And Aims: Research on the importance of the Emergency Medical Dispatch Centre (EMDC) role in reducing the time delays for patients with acute ischaemic stroke (AIS) is limited. This study aimed to analyse how Norwegian EMDCs' accurate suspicions can impact the clinical care times in this patient group.

Methods: We collected clinical care time metrics and acute reperfusion treatment data from the Norwegian Stroke Registry on patients with AIS in Western Norway who were evaluated by the EMDC and had an ambulance dispatched in 2021.

Anaerobic digestion of microalgae: microbial response and recovery after organic loading disturbances.

Unlabelled: Industrial anaerobic digestion (AD) represents a relevant energy source beyond today's fossil fuels, wherein organic matter is recycled to methane gas via an intricate and complex microbial food web. Despite its potential, anaerobic reactors often undergo process instability over time, which is frequently caused by substrate composition perturbations, making the system unreliable for stable energy production. To ensure the reliability of AD technologies, it is crucial to identify microbial and system responses to better understand the effect of such perturbations and ultimately detect signatures indicative of process failure.

Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models.