ISEL - Eng. Quim. Biol. - Comunicações
Permanent URI for this collection
Browse
Browsing ISEL - Eng. Quim. Biol. - Comunicações by Author "Araújo, R."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Predicting Critically Ill Patients Outcome in the ICU usinh UHPLC-HRMS dataPublication . Calado, Cecília; Fonseca, T.A.H.; Rekowski, C.P. Von; Araújo, R.; Oliveira, M. Conceição; Bento, L.; Justino, G.C.The available scores to predict patients’ outcomes in specific settings generally present low sensitivities and specificities when applied to intensive care units’ (ICUs) populations. Advancements in analytical techniques, notably Ultra-High Performance Liquid Chromatography- Mass Spectrometry (UHPLC HRMS) transformed biomarker identification, enabling a comprehensive profiling of biofluids, including serum. In the current work, untargeted metabolomics, utilizing UHPLC-HRMS serum analysis, was performed on 16 ICU patients, categorized as either discharged (n=8), or deceased (n=8) in average seven days post sample collection. Linear discriminant analysis (LDA) or principal component analysis (PCA)-LDA models involving different metabolite sets were developed, enabling to predict patients’ outcomes in the ICU with 92% accuracy and 83% sensitivity on validation datasets. These results highlight the advantages of UHPLC-HRMS as a platform capable of providing a set of clinically significant biomarkers to predict patients’ outcome.
- Streamlining bacterial infection diagnosis: rapid gram classification using FTIR spectroscopyPublication . Araújo, R.; Ramalhete, L.; Fonseca, T.; von Rekowski, C.; Bento, L.; Calado, CecíliaIn a hospital setting, diagnosing infections typically involves a complex process that includes the collection of biological samples and growing a culture for organism isolation, followed by its characterization. However, these methods are slow, require multiple steps and are often limited by the need of specialized equipment and skilled personnel. In this preliminary study, it was analysed the serum, by FTIR spectroscopy, of 29 critically ill COVID-19 patients in an ICU. It was analysed the effect of varied preprocessing methods and spectral sub-regions on t-SNE. Through the optimization of SVM models, it was possible to achieve a very good gram predictive model with a sensitivity and specificity of 90 and 89% respectively. As an accurate classification of bacterial strains is crucial to guide effective antimicrobial therapy and prevent the spread of multidrug-resistant bacteria, FTIR spectra, acquired in a simple, economic, and rapid mode, presents therefore the potential for development of new classification methods that would greatly enhance the ability to manage bacterial infections.