IL-6 is one of the major mediators of the hyper-inflammatory responses with complex biological functions as it can signal via different modes of action. IL-6 by classical signalling has anti-inflammatory and antibacterial activities, while trans-signalling mediates pro-inflammatory effects. The net biological effect of IL-6 is established by multiple factors beyond its absolute concentration. Here, we assess the relationship between IL-6 signalling variables [IL-6, soluble IL-6R (sIL-6R) and soluble gp130 (sgp130)] and outcomes in a cohort of 366 COVID-19 patients. The potential trans-signalling was evaluated by a ratio between the pro-inflammatory binary IL-6:sIL-6R complex and the inactive ternary IL-6:sIL-6R:sgp130 complex (binary/ternary complex) and the fold molar excess of sgp130 over sIL-6R (FME). Our data provide new evidence that high levels of IL-6, sIL-6R, sgp130, binary/ternary complex ratio, and low FME are independent predictors of COVID-19 severity in survivor patients (without death), and the combination of IL-6 + sIL-6R + sgp130 exhibited the most robust classification capacity. Conversely, in a subgroup of patients with a very poor prognosis, we found that high levels of IL-6 and low levels of sIL-6R, sgp130, and binary/ternary complex ratio were predictors of death. In this context, the highest predictive capacity corresponded to the combined analysis of IL-6 + FME + lymphopenia + creatinine. Herein, we present IL-6 signalling variables as a helpful tool for the early identification and stratification of patients with clear implications for treatment and clinical decision-making.

Software verification and modeling quality are permanent challenges in software development. So, smarter and more cohesive methods for the creation and maintenance of data models without loss of quality are required as model complexity increases in current academic and industrial MDE-based system designs. In-place endogenous model transformations (refactorings) are an efficient and straightforward approach to deal with data model complexity, but ad-hoc and frequent transformations must be performed to maintain model quality. In this paper we explore an alternative method to ensure the quality of data models: correction by contract. We propose a new method for the creation and maintenance of static data models (relational, entity-relationship or class models) with enhanced quality. We will use an executable logic for functional dependencies to characterize data model redundancy and we define a set of OCL constraints to guide the construction and maintenance of the models. We also illustrate this approach with a simplified intermediate metamodel (FDMM) for functional dependencies over a data model to show the potential benefits of the method.

In this work we summarize the state of the art in the use of database functional dependencies. We compare the low impact that it has in the commercial environment with its successful acceptation in the academic environment. Particularly we remark that there does not exists any commercial development tool which uses the information provided by functional dependencies and this useful information is also deprecated by the database management systems. As a result of this, large database designs have to be rebuilt a few years after their establishment. In this work we identify the reasons which causes this situation and we propose a guideline to wide spread the effective use of Functional Dependencies in commercial design and tools.