The role of pancreatic stone protein as a prognostic factor for COVID-19 patients.

OBJECTIVE: The outbreak of Severe Acute Respiratory Syndrome-CoronaVirus 2 (SARS-CoV-2) has rapidly spread throughout the world straining health care systems. Several biomarkers indicate the presence of hyper-inflammation and evaluate the severity of the disease. Our aim was to investigate the prognostic value of pancreatic stone protein plasma concentration in patients with SARS-CoV-2 pneumonia. PATIENTS AND METHODS: We prospectively studied 55 patients with acute SARS-CoV-2 pneumonia admitted to our tertiary hospital. Sepsis biomarkers, including pancreatic stone protein (PSP), were measured on admission. The role of these biomarkers in the prediction of in-hospital mortality (28 day) and length of hospital stay was investigated. RESULTS: Although Pancreatic stone protein did not have significant prognostic value for in-hospital mortality, there was a moderate accuracy for prolonged length of stay. The optimal cut-off value for prolonged hospital stay was 51 ng/dL (Sensitivity: 0.65, Specificity: 0.913). CONCLUSIONS: Pancreatic Stone Protein on admission could accurately identify patients requiring prolonged hospitalization. The results of this study can serve as a strong early basis for future validation studies of such an innovative approach.

Simulation and verification of P systems through communicating X-machines.

The aim of this paper is to prove the suitability of a parallel distributed computational model, communicating X-machines, to simulate in a natural way a well established model of molecular computation, P systems, and to present some further benefits of the approach allowing us to check for some formal properties. A set of rules to transform any P system with symbol-objects into a communicating X-machine model is presented and a variation of temporal logic for X-machines is briefly discussed, which facilitates model checking of desired properties of the system. Finally, the benefits resulting from the transformation are discussed.

A formal framework for modelling and validating medical systems.

Medical computerised systems which have a major effect on human lives (e.g. those used for diagnosis, therapy, surgery, in the intensive care units, etc) are considered as safety critical systems. Such systems are sometimes responsible for major damages and injuries due to unpredicted malfunction. Misleading user requirements, errors in the specification and in the implementation are the usual reasons responsible for non-safe systems. This paper advocates the use of an integrated formal framework based on a computational machine (X-Machine), in the development of safety critical medical systems. This formal framework gives the ability to intuitively as well as formally model a system, then automatically check if the produced model has all the desired properties, and finally test if the implementation is equivalent to the specification by applying a complete set of test cases. Therefore, the use of this framework in the development of systems in safety critical medical domains can assure that the final product is valid with respect to the user requirements by revealing errors during the whole development life cycle and subsequently add to the confidence of their use. The proposed framework is accompanied by an example, which demonstrates the use of X-Machines in specification, testing and verification.