ABSTRACT 2 Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non - infectious systemic inflamm atory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016 - 2017. A mass spectrometry - based approach was used to analyze the plasma proteins in the enrolled subjects . Subsequently, using recursive feature elimination (RFE) classification and cross - validation with a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein - protein interaction netwo rk was analyzed with String software. Results: A total of 277 patients (141 with sepsis and 136 with NISIRS) were included. Conclusion: There are proteomic patterns associated with sepsis compared to NISIRS with different strength of association. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutic targets in the future. Key words: Sepsis, Septic shock, SIRS, Proteomics, Omics, Diagnosis INTRODUCTION 3 Sepsis is known as a clinical syndrome where life - threatening organ dysfunction occurs due to a dysregulated host response to infection.

Purpose: Our research project focuses on improving the content update of the online European Smart Tourism Tools (STTs) Observatory by incorporating and categorizing STTs. The categorization is based on their taxonomy, and it facilitates the end user's search process. The use of a Smart ETL (Extract, Transform, and Load) process, where \emph{Smart} indicates the use of Artificial Intelligence (AI), is central to this endeavor. Methods: The contents describing STTs are derived from PDF catalogs, where PDF-scraping techniques extract QR codes, images, links, and text information. Duplicate STTs between the catalogs are removed, and the remaining ones are classified based on their text information using Large Language Models (LLMs). Finally, the data is transformed to comply with the Dublin Core metadata structure (the observatory's metadata structure), chosen for its wide acceptance and flexibility. Results: The Smart ETL process to import STTs to the observatory combines PDF-scraping techniques with LLMs for text content-based classification. Our preliminary results have demonstrated the potential of LLMs for text content-based classification. Conclusion: The proposed approach's feasibility is a step towards efficient content-based classification, not only in Smart Tourism but also adaptable to other fields. Future work will mainly focus on refining this classification process.