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- Alternative sérum biomarkers of bacteraemia for intensive care unit patientsPublication . Araújo, Rúben; Von Rekowski, Cristiana; Bento, Luís; Fonseca, Tiago AH; Calado, CecíliaThe diagnosis of infections in hospital or clinical settings usually involves a series of time-consuming steps, including biological sample collection, culture growth of the organism isolation and subsequent characterization. For this, there are diverse infection biomarkers based on blood analysis, however, these are of limited use in patients presenting confound processes as inflammatory process as occurring at intensive care units. In this preliminary study, the application of serum analysis by FTIR spectroscopy, to predict bacteraemia in 102 critically ill patients in an ICU was evaluated. It was analysed the effect of spectra pre-processing methods and spectral sub-regions on t-distributed stochastic neighbour embedding. By optimizing Support Vector Machine (SVM) models, based on normalised second derivative spectra of a smaller subregion, it was possible to achieve a good bacteraemia predictive model with a sensitivity and specificity of 76%. Since FTIR spectra of serum is acquired in a simple, economic and rapid mode, the technique presents the potential to be a cost-effective methodology of bacteraemia identification, with special relevance in critically ill patients, where a rapid infection diagnostic will allow to avoid the unnecessary use of antibiotics, which ultimately will ease the load on already fragile patients' metabolism.
- Infection biomarkers based on metabolomicsPublication . Araújo, Rúben; Bento, Luís; Fonseca, Tiago AH; Von Rekowski, Cristiana; Ribeiro Da Cunha, Bernardo; Calado, CecíliaCurrent infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.
- Comparison of analytical methods of serum untargeted metabolomicsPublication . Fonseca, Tiago AH; Araújo, Rúben; Von Rekowski, Cristiana; Justino, Gonçalo C.; Oliveira, Maria Da Conceiçao; Bento, Luís; Calado, CecíliaMetabolomics has emerged as a powerful tool in the discovery of new biomarkers for medical diagnosis and prognosis. However, there are numerous challenges, such as the methods used to characterize the system metabolome. In the present work, the comparison of two analytical platforms to acquire the serum metabolome of critically ill patients was conducted. The untargeted serum metabolome analysis by ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) enabled to identify a set of metabolites statistically different between deceased and discharged patients. This set of metabolites also enabled to develop a very good predictive model, based on linear discriminant analysis (LDA) with a sensitivity and specificity of 80% and 100%, respectively. Fourier Transform Infrared (FTIR) spectroscopy was also applied in a high-throughput, simple and rapid mode to analyze the serum metabolome. Despite this technique not enabling the identification of metabolites, it allowed to identify molecular fingerprints associated to each patient group, while leading to a good predictive model, based on principal component analysis-LDA, with a sensitivity and specificity of 100% and 90%, respectively. Therefore, both analytical techniques presented complementary characteristics, that should be further explored for metabolome characterization and application as for biomarkers discovery for medical diagnosis and prognosis.
- Development of Predictive Models for COVID-19 Prognosis based on Patients’ Demographic and Clinical DataPublication . Von Rekowski, Cristiana; Calado, Cecília Ribeiro da Cruz; Pinto, Iola; Bento, LuísBackground – Cases of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were first reported in late December 2019. Due to the large spectrum of clinical presentations and outcomes, the disease was named Coronavirus Disease 2019 (COVID-19) and characterized as a pandemic due to the elevated number of cases worldwide, the high transmission rate and the lack of action measures. Since then, a lot of progress has been made, but the study of demographic and clinical information and the determination of possible laboratory biomarkers for COVID-19 prognosis is crucial. Purpose – Determine predictive biomarkers for COVID-19’s outcome (death or survival), in critically ill patients, using clinical, demographic and laboratory data from the intensive care unit (ICU). Methods – Demographic, clinical and laboratory data from 337 COVID-19 patients admitted to the ICU of Centro Hospitalar Universitário Lisboa Central, Portugal, between March 2020 and March 2021, was extracted from the hospital’s electronic medical record system, pre-processed, and analyzed. Comparisons were made regarding death, the need of invasive mechanic ventilation (IMV), the first three COVID-19 waves and age groups. Longitudinal data was gathered over the course of the patients stay in the ICU. To infer about the evolution of the patients' condition in the first week of ICU admission, a comparative analysis was carried out between the data from the 2nd (335 patients) and 7th days (216 patients). Comparisons of laboratory parameters between discharged and deceased patients, at these time points were performed. The associations between the several biomarkers and death were tested by means of Univariate Generalized Estimating Equations (GEEs) models. Additionally, to analyze the impact of some biomarkers in mortality, crude odds ratios were estimated and interpreted, with the corresponding 95% confidence intervals (CIs). Death event-free survival rates were obtained by the Kaplan-Meier estimator. All P values were considered statistically significant at P<0.05. Results – Deceased patients were considerably older, had more comorbidities, required more IMV, and spent less time in the hospital than discharged patients. Death rates did not differ significantly between COVID-19 waves. Patients from the 1st wave were significantly older and relied more on IMV and extracorporeal membrane oxygenation (ECMO). Most of the detected differences regarding laboratory biomarkers were found between discharged and deceased patients from the 2nd and 3rd waves, being that the deceased ones had almost always worse results. In general, worse results were obtained in the 1st wave and in the 7th day of ICU admission. In 2nd day of ICU admission, 2nd wave, higher mortality rates were observed for patients with lymphocyte (LYM) levels under normality ranges. In the 3rd wave, mortality rates were higher for patients with high sensitivity troponin I (hs-cTn I) levels above normality ranges in the 2nd day of ICU admission, with LYM levels under normality ranges in the 7th day of ICU admission, and with platelet (PLT) levels below normality ranges, either in the 2nd or 7th days of ICU admission. Through the univariate logistic regression’s results in 2nd day of ICU admission, 2nd wave, hs-cTn I, red blood cell (RBC) counts, platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) showed significant association with the risk of death. In 7th day of ICU admission, C-reactive protein (CRP), RBC counts, hematocrit (HCT), hemoglobin (HGB), white blood cell (WBC) and neutrophil (NEU) counts, eosinophil (EO) counts and NLR, revealed significant association with the risk of death. In the 2nd day of ICU admission, 3rd wave, hs-cTn I, PLT counts, lactate dehydrogenase (LDH) and CRP showed significant association with the risk of death. For the 7th day, PCT, CRP, WBC and NEU counts, LYM counts, NLR and PLT counts results were also associated with higher risks of death. Univariate GEEs models results demonstrated that, in the 1st wave, hs-cTn I, myoglobin, EO counts, results were associated with higher risks of death. In the 2nd wave, the risk of death was significantly associated with hs-cTn I, myoglobin levels, EO counts, WBC and NEU counts, LYM counts, and INR. Finally, in the 3rd wave, hs-cTn I, CK, EO counts, WBC and NEU counts, LYM counts, NLR and PLT counts, were also associated with the risk of death. Conclusion - This study provides useful information for prognostic evaluation that can be used to guide treatment and monitoring. Most importantly, it consists of valuable data that can be employed as the foundation of a variety of future research. Aside from the positive results, more research is needed to develop reliable and robust biomarkers for COVID-19’s outcomes.
- The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR SpectroscopyPublication . Fonseca, Tiago AH; Von Rekowski, Cristiana; Araújo, Rúben; Oliveira, Maria Da Conceiçao; Justino, Gonçalo C.; Bento, Luís; Calado, CecíliaBiofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient’s survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.
- 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.
- An interactive dashboard for statistical analysis of intensive care unit COVID-19 dataPublication . Dias, Rúben; Ferreira, Artur; Pinto, Iola; Brás-Geraldes, Carlos; Von Rekowski, Cristiana; Bento, LuísCOVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19’s evolution, yielding insights for this pandemic and for future pandemics.
- Laboratory biomarkers associated to death in the first three COVID-19 waves in PortugalPublication . Von Rekowski, Cristiana; Fonseca, Tiago; Calado, Cecília; Bento, Luís; Pinto, Iola; Araújo, RúbenBesides the pandemic being over, new SARS-CoV-2 lineages, and sub-lineages, still pose risks to global health. Thus, in this preliminary study, to better understand the characteristics of COVID-19 patients and the effect of certain hematologic biomarkers on their outcome, we analyzed data from 337 patients admitted to the ICU of a single-center hospital in Lisbon, Portugal, in the first three waves of the pandemic. Most patients belonged to the second (40.4%) and third (41.2%) waves. The ones from the first wave were significantly older and relied more on respiratory techniques like invasive mechanic ventilation and extracorporeal membrane oxygenation. There were no significant differences between waves regarding mortality in the ICU. In general, non-survivors had worse laboratory results. Biomarkers significantly associated with death changed depending on the waves. Increased high-sensitivity cardiac troponin I results, and lower eosinophil counts were associated to death in all waves. In the second and third waves, the international normalized ratio, lymphocyte counts, and neutrophil counts were also associated to mortality. A higher risk of death was linked to increased myoglobin results in the first two waves, as well as increased creatine kinase results, and lower platelet counts in the third wave.
- The characteristics and laboratory findings of SARS-CoV-2 infected patients during the first three COVID-19 waves in Portugal – a retrospective single-center studyPublication . Von Rekowski, Cristiana; Fonseca, Tiago AH; Araújo, Rúben; Brás-Geraldes, Carlos; Calado, Cecília; Bento, Luís; Pinto, IolaBackground and Objectives: Given the wide spectrum of clinical and laboratory manifestations of the coronavirus disease 2019 (COVID-19), it is imperative to identify potential contributing factors to patients’ outcomes. However, a limited number of studies have assessed how the different waves affected the progression of the disease, more so in Portugal. Therefore, our main purpose was to study the clinical and laboratory patterns of COVID-19 in an unvaccinated population admitted to the intensive care unit, identifying characteristics associated with death, in each of the first three waves of the pandemic. Materials and Methods: This study included 337 COVID-19 patients admitted to the intensive care unit of a single-center hospital in Lisbon, Portugal, between March 2020 and March 2021. Comparisons were made between three COVID-19 waves, in the second (n = 325) and seventh (n = 216) days after admission, and between discharged and deceased patients. Results: Deceased patients were considerably older (p = 0.021) and needed greater ventilatory assistance (p = 0.023), especially in the first wave. Differences between discharged and deceased patients’ biomarkers were minimal in the first wave, on both analyzed days. In the second wave significant differences emerged in troponins, lactate dehydrogenase, procalcitonin, C-reactive protein, and white blood cell subpopulations, as well as platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios (all p < 0.05). Furthermore, in the third wave, platelets and D-dimers were also significantly different between patients’ groups (all p < 0.05). From the second to the seventh days, troponins and lactate dehydrogenase showed significant decreases, mainly for discharged patients, while platelet counts increased (all p < 0.01). Lymphocytes significantly increased in discharged patients (all p < 0.05), while white blood cells rose in the second (all p < 0.001) and third (all p < 0.05) waves among deceased patients. Conclusions: This study yields insights into COVID-19 patients’ characteristics and mortality-associated biomarkers during Portugal’s first three COVID-19 waves, highlighting the importance of considering wave variations in future research due to potential significant outcome differences.
- A new method for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextractsPublication . Caldeira, Viviana; Fonseca, Tiago AH; N'Dembo, Luana; Araújo, Rúben; Von Rekowski, Cristiana; Sampaio, Pedro; Calado, CecíliaTo robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.