Tracing and analyzing COVID-19 dissemination using knowledge graphs.

The COVID-19 (SARS-CoV-2) spread around the globe could have been halted if we had had a better understanding of the situation and applied more restrictive measures for travel adapted to each country. This is due to a lack of efficient tools to visualize, analyze and control the virus dissemination. In the context of virus proliferation, analyzing flight connections between countries and COVID-19 data seems helpful to understand spatial and temporal information about the virus and its possible spread. To manage these complex, massive, and heterogeneous data, we propose a methodology based on knowledge graphs models. Several analyses and visualization tools can be applied, and our results show that these knowledge graph models may be a promising way to study the dissemination of any virus. These graphs can also be easily enriched with additional information that could be useful in the future to analyze or predict other interesting indicators.

Analyzing SARS-CoV-2 Sequence Patterns by Semantic Trajectories.

Since the beginning of the pandemic due to the SARS-CoV-2 emergence, several variants has been observed all over the world. One of the last known, Omicron, caused a large spread of the virus in few days, and several countries reached a record number of contaminations. Indeed, the mutation in the Spike region of the virus played an important role in altering its behavior. Therefore, it is important to understand the virus evolution by extracting and analyzing the virus structure of each variant. In this work we show how patterns sequence could be analyzed and extracted by means of semantic trajectories modeling. To do so, we designed a graph-based model in which the genome organization is handled using nodes and edges to represent respectively the nucleotides and sequence connection (point of interest and routes for trajectories). The modeling choices and pattern extraction from the graph allowed to retrieve a region where a mutation occurred in Omicron (NCBI version:OM011974.1).

Circulating tumor DNA (ctDNA) in adjuvant therapy of early stage colon cancer: current status and future perspectives.

BACKGROUND: This article reviews the current knowledge on circulating tumor DNA (ctDNA) in early stage colon cancer and ongoing trials on ctDNA-guided treatment in the adjuvant setting. METHODS: A literature search of Pubmed was performed to identify studies on ctDNA in early stage colon cancer and neoadjuvant or adjuvant treatment. For ongoing trials, we searched clinicaltrials.gov and the Australian New Zealand Clinical Trials Registry (ANZCTR). RESULTS: Several studies show that ctDNA is a strong predictor for recurrence and survival after surgery and adjuvant chemotherapy. The specificity of this marker is extremely high, and the sensitivity is increasing with the development of technology. Recurrences can be detected very early and the analysis can potentially be used to guide neoadjuvant and adjuvant treatment. Ongoing and planned studies are now looking into escalation and de-escalation of therapy according to ctDNA-status after surgery. CONCLUSION: Serial measurement of ctDNA shows great promise as a marker for both prognosis and response to treatment in early colon cancer. Future studies will show whether we can use this analysis for tailoring treatment for patients in the adjuvant and neoadjuvant setting. With improved technology, ctDNA has the potential of becoming a 'game-changer' in the treatment of early stage colon cancers.

OntoRepliCov: an Ontology-Based Approach for Modeling the SARS-CoV-2 Replication Process.

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.