Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent, entity classes.

Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak emerged, countless efforts are being made worldwide to understand the molecular mechanisms underlying the coronavirus disease 2019 (COVID-19) in an attempt to identify the specific clinical characteristics of critically ill COVID-19 patients involved in its pathogenesis and provide therapeutic alternatives to minimize COVID-19 severity. Recently, COVID-19 has been closely related to sepsis, which suggests that most deceases in intensive care units (ICU) may be a direct consequence of SARS-CoV-2 infection-induced sepsis. Understanding oxidative stress and the molecular inflammation mechanisms contributing to COVID-19 progression to severe phenotypes such as sepsis is a current clinical need in the effort to improve therapies in SARS-CoV-2 infected patients. This article aims to review the molecular pathogenesis of SARS-CoV-2 and its relationship with oxidative stress and inflammation, which can contribute to sepsis progression. We also provide an overview of potential antioxidant therapies and active clinical trials that might prevent disease progression or reduce its severity.

BackgroundIn Latin America, 18 million people are infected with Trypanosoma cruzi, the agent of Chagas' disease, with the greatest economic burden. Vertebrate calreticulins (CRT) are multifunctional, intra- and extracellular proteins. In the endoplasmic reticulum (ER) they bind calcium and act as chaperones. Since human CRT (HuCRT) is antiangiogenic and suppresses tumor growth, the presence of these functions in the parasite orthologue may have consequences in the host/parasite interaction. Previously, we have cloned and expressed T. cruzi calreticulin (TcCRT) and shown that TcCRT, translocated from the ER to the area of trypomastigote flagellum emergence, promotes infectivity, inactivates the complement system and inhibits angiogenesis in the chorioallantoid chicken egg membrane. Most likely, derived from these properties, TcCRT displays in vivo inhibitory effects against an experimental mammary tumor.Methodology and Principal FindingsTcCRT (or its N-terminal vasostatin-like domain, N-TcCRT) a) Abrogates capillary growth in the ex vivo rat aortic ring assay, b) Inhibits capillary morphogenesis in a human umbilical vein endothelial cell (HUVEC) assay, c) Inhibits migration and proliferation of HUVECs and the human endothelial cell line Eahy926. In these assays TcCRT was more effective, in molar terms, than HuCRT: d) In confocal microscopy, live HUVECs and EAhy926 cells, are recognized by FITC-TcCRT, followed by its internalization and accumulation around the host cell nuclei, a phenomenon that is abrogated by Fucoidin, a specific scavenger receptor ligand and, e) Inhibits in vivo the growth of the murine mammary TA3 MTXR tumor cell line.Conclusions/SignificanceWe describe herein antiangiogenic and antitumor properties of a parasite chaperone molecule, specifically TcCRT. Perhaps, by virtue of its capacity to inhibit angiogenesis (and the complement system), TcCRT is anti-inflammatory, thus impairing the antiparasite immune response. The TcCRT antiangiogenic effect could also explain, at least partially, the in vivo antitumor effects reported herein and the reports proposing antitumor properties for T. cruzi infection.

Semantic similarity plays an important role in geographic information systems as it supports the identification of objects that are conceptually close, but not identical. Similarity assessments are particularly important for retrieval of geospatial data in such settings as digital libraries, heterogeneous databases, and the World Wide Web. Although some computational models for semantic similarity assessment exist, these models are typically limited by their inability to handle such important cognitive properties of similarity judgements as their inherent asymmetry and their dependence on context. This paper defines the Matching-Distance Similarity Measure (MDSM) for determining semantic similarity among spatial entity classes, taking into account the distinguishing features of these classes (parts, functions, and attributes) and their semantic interrelations (is-a and part-whole relations). A matching process is combined with a semantic-distance calculation to obtain asymmetric values of similarity that depend on the degree of generalization of entity classes. MDSM's matching process is also driven by contextual considerations, where the context determines the relative importance of distinguishing features. Based on a human-subject experiment, MDSM results correlate well with people's judgements of similarity. When contextual information is used for determining the importance of distinguishing features, this correlation increases; however, the major component of the correlation between MDSM results and people's judgements is due to a detailed definition of entity classes.