ISEL - Instituto Superior de Engenharia de Lisboa
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Browsing ISEL - Instituto Superior de Engenharia de Lisboa by contributor "Tomar, Rajesh Singh"
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- Comparison of the serum whole molecular composition with the serum metabolome to acquire the pathophysiological statePublication . Correia, Inês; Henrique Fonseca, Tiago Alexandre; Pataco, Jéssica; Oliveira, Mafalda; Caldeira, Viviana; Domingues, N.; Von Rekowski, Cristiana; Araújo, Rúben Alexandre Dinis; Bento, Luís; Calado, Cecília; Domingues, Nuno; Tomar, Rajesh Singh; Mahamud, TosapornOmics Sciences serve as an essential tool to advance precision medicine. Since conventional omics sciences rely on laborious, complex and time-consuming analytical processes, this study evaluated whether the serum molecular fingerprint, captured by FTIR spectroscopy, could predict mortality risk in critically ill patients. Both the whole serum and the serum metabolome (i.e., serum after removal of macromolecules) were analyzed. PCA-LDA models demonstrated strong performance in predicting patients’ pathophysiological state. A significantly more accurate model for predicting the patients’ pathophysiological state was achieved using the serum metabolome (94%) compared to the whole serum (81%). This is consistent with metabolomics, which provides a more direct view of the systems’ functionality. These promising results highlight the importance of FTIR spectroscopy analysis of the serum metabolome, offering a rapid, cost-effective, and high-throughput method for assessing patients' pathophysiological state.
- Infection biomarkers at intensive care unitsPublication . Araújo, Rúben Alexandre Dinis; Ramalhete, Luís; Henrique Fonseca, Tiago Alexandre; Von Rekowski, Cristiana; Bento, Luís; Calado, Cecília; Domingues, Nuno; Tomar, Rajesh Singh; Mahamud, TosapornIt is relevant to discover infection biomarkers, especially for critically ill patients in intensive care units (ICU), as these patients often present non-infectious inflammatory processes that obscure typical infectious markers. This study focused on 20 ICU patients, half of whom had acquired bacterial blood infections (bacteremia). Due to the significance of inflammatory processes in these patients, it was evaluated how 21 serum cytokines could be used to develop predictive models for bacteremia. Feature selection using a Gain Information algorithm allowed for the construction of an excellent Naïve Bayes model, achieving an AUC of 0.950. These promising results strongly support future studies with larger cohorts, to further evaluate these types of platforms for infection diagnosis in such critical populations.
- Trends in COVID-19 patient characteristics and mortality throughout the pandemic: insights from a portuguese single-centre studyPublication . Von Rekowski, Cristiana; Pinto, Iola; Henrique Fonseca, Tiago Alexandre; Araújo, Rúben Alexandre Dinis; Ferreira, Artur; Calado, Cecília; Bento, Luís; Domingues, Nuno; Tomar, Rajesh Singh; Mahamud, TosapornAs SARS-CoV-2 continues to circulate globally and new variants emerge, it remains relevant to gather data on the affected patients’ clinical characteristics and outcomes to understand how individual factors and public health measures affect prognosis. Thus, we analyzed data of 870 ICU patients admitted for COVID-19 across two distinct phases of the pandemic: before and after the introduction of immunization. Experimental results showed that vaccination significantly impacted patient demographics after the third wave, and that waves number two and three, dominated by the EU1 and Alpha variants, had higher mortality. Older age, the need for invasive mechanical ventilation, and hematologic cancer were significantly associated with an increased risk of death in the adjusted multivariable model (AUC: 0.778, 95% CI 0.746-0.810, p<0.001). As the pandemic progressed, while some public health interventions influenced the observed trends, individual patient characteristics had a more substantial impact on their outcome.