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- Simplifying data analysis in biomedical research: an automated, user-friendly toolPublication . Araújo, Rúben; Ramalhete, Luís; Viegas, Ana; Von Rekowski, Cristiana; Fonseca, Tiago AH; Calado, Cecília; Bento, LuísRobust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than dispari ties between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool’s functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI’s GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.
- Early mortality prediction in intensive care unit patients based on serum metabolomicPublication . Araújo, Rúben; Ramalhete, Luís; Von Rekowski, Cristiana; Fonseca, Tiago AH; Bento, Luís; Calado, CecíliaPredicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.
- 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.
- Early mortality prediction in intensive care unit patients based on serum metabolomic fingerprintPublication . Araújo, Rúben Alexandre Dinis; Ramalhete, Luís; Von Rekowski, Cristiana; Henrique Fonseca, Tiago Alexandre; Bento, Luís; Calado, CecíliaPredicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast-Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.
- Discovery of delirium biomarkers through minimally invasive serum molecular fingerprinting.Publication . Viegas, Ana; Araújo, Rúben; Ramalhete, Luís; Von Rekowski, Cristiana; Fonseca, Tiago AH; Bento, Luís; Calado, CecíliaDelirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.