ISEL - Eng. Quim. Biol. - Comunicações
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- Survivability prediction based on the serum molecular fingerprint in critically ill patientsPublication . Correia, Inês; Araújo, Rúben Alexandre Dinis; Henrique Fonseca, Tiago Alexandre; Von Rekowski, Cristiana; Bento, Luís; Calado, Cecília; Domingues, Nuno; Cardoso, L. M.; Thaweesak, Y.It is relevant to discover biomarkers enabling to predict critically ill patients’ survival. This study focused on 45 patients, from which 22 deceased and 23 were discharged from an Intensive Care Unit (ICU). It was considered the serum molecular fingerprint, as acquired by Fourier Transform Infra-Red (FTIR) spectroscopy, obtained 3 days before the patients discharged or death at the ICU. It was possible to obtain ratios of bands of the sera spectra, statistically different between the two groups of patients. Furthermore, good Naïve Bayes models were developed based on the second derivative spectra enabling an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.77. These promising outputs suggest further investigation with a larger cohort.
- 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.
- 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.
- 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.
- Predicting critically ill patients outcome in the ICU using UHPLC-HRMS dataPublication . Henrique Fonseca, Tiago Alexandre; Von Rekowski, Cristiana; Araújo, Rúben Alexandre Dinis; Oliveira, Maria Conceição; Bento, Luís; Justino, Gonçalo; Calado, Cecília; Domingues, Nuno A. S.; Gomes, Vítor; Topcuoglu, BulentThe 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 (UHPLCHRMS) 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. 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 (UHPLCHRMS) 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.
- Validation of a prototype of a miniaturised infrared spectrometer on complex organic samplesPublication . Monteiro, L.; Zoio, P.; Carvalho, B. B.; Fonseca, L.P.; Calado, CecíliaFourier Transform Infrared (FTIR) spectroscopy focused on the near infrared (NIR) region has become crucial for quality control on diverse areas, from energy to biomedical applications, by enabling in-situ and in real time analysis of samples with complex organic compositions [1,2]. The development of portable and miniaturized NIR spectrometers (miniNIR) can further extend NIR spectroscopy applications [3,4], thus this work compares in-situ analysis based on a FT-NIR benchtop spectrometer with a miniNIR prototype to detect and quantify contaminants in biodiesel, such as vegetable oils, methanol, and glycerol. Good models based on principal component analysis-linear discriminant analysis of FT-NIR spectra were obtained, predicting contaminants with accuracies between 75 to 95%, while the miniNIR prototype’s delivered models with accuracies between 66 to 86%, showing the device’s potential for preliminary quality control of biodiesel, with the added advantages of low cost and portability.
- 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.
- A new approach for rapid detection of bioactive compounds using MIR spectroscopy and machine learning algorithmsPublication . Sampaio, P. N.; Duarte, Fernando B.; Calado, CecíliaNowadays, microbial infections and resistance to antibiotic drugs are the biggest challenges, which threaten the health of societies. Due to several pharmacological activities associated with Cynara cardunculus, such as hepatoprotective, antioxidative, anticarcinogenic, hypocholesterolemic, antibacterial, anti-HIV, among others, extracts from seeds, leaves, and flowers were tested in Escherichia coli cells. The sensibility of the Mid-infrared (MIR) spectroscopy allowed to perform a detailed analysis of the antimicrobial action of extracts in terms of their biomolecular changes. A comparative model based on several commercial antibiotics such as metronidazole, kanamycin, clarithromycin, chloramphenicol, and ampicillin, was developed. The clustering analysis was performed using unsupervised algorithms such as Principal Component Analysis (PCA), and Kohonen Self-Organizing Maps (SOM). The extracts characterized with antioxidant activity were clustered with antibiotics and presented a promissory antimicrobial activity. According to this preliminary result, it is possible to use the MIR spectroscopy and machine learning algorithm to discover promissory bio compounds characterized by antimicrobial properties, allowing to develop a platform to discover new bioactive molecules, reducing time and costs.
- 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.