ISEL - Eng. Quim. Biol. - Comunicações
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- 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.
- Predict cells viability, proliferation, and metabolic status, based in one unique and simple assayPublication . Caldeira, Viviana; Araújo, Rúben; Ramalhete, Luís; Calado, CecíliaA new method to simultaneously predict cells viability, proliferation and metabolic status, in a rapid, simple but also specific and sensitive mode was developed. The method is based on mid-infrared (MIR) spectroscopic analysis of cells. As model system were used Human embryonic kidney (HEK) 293 cells and T lymphocytes. After submitting cells to different environments as the toxic dimethyl sulfoxide, or metabolic activation, cells viability was analyzed by optical microscopy after coloration with trypan blue, and the cell count was determined with a Neubauer hemocytometer. The principal component analysis (PCA) of the cells second derivative spectra enabled to discriminate the cells viability and the cells proliferation as assayed by conventional methods, while spectra PCA and Hierarchical Cluster Analysis (HCA) enabled to discriminate T cells metabolic activation. The new methods, based on MIR spectroscopy, present the advantages of being applicable in automatic, simple and high-throughput mode in relation to the onventional methods.
- Validation of a miniaturised near infrared spectrometer for contaminant assessment in biodieselPublication . Monteiro, Luísa; Zoio, Paulo; Carvalho, M. B. P.; Fonseca, Luís P. P.; Calado, CecíliaA new method to simultaneously predict cells viability, proliferation and metabolic status, in a rapid, simple but also specific and sensitive mode was developed. The method is based on mid-infrared (MIR) spectroscopic analysis of cells. As model system were used Human embryonic kidney (HEK) 293 cells and T lymphocytes. After submitting cells to different environments as the toxic dimethyl sulfoxide, or metabolic activation, cells viability was analyzed by optical microscopy after coloration with trypan blue, and the cell count was determined with a Neubauer hemocytometer. The principal component analysis (PCA) of the cells second derivative spectra enabled to discriminate the cells viability and the cells proliferation as assayed by conventional methods, while spectra PCA and Hierarchical Cluster Analysis (HCA) enabled to discriminate T cells metabolic activation. The new methods, based on MIR spectroscopy, present the advantages of being applicable in automatic, simple and high-throughput mode in relation to the conventional methods.
- 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.
- Production of synthesis gas obtained via alkaline water electrolysis and added biomassPublication . Gomes, João; Puna, Jaime; Santos, Maria TeresaThis paper presents the results of the research currently being carried out at ISEL with the objective of developing new electrochemistry-based processes to obtain renewable synthetic fuels from alkaline water electrolysis using a carbon source. In the developed process, the gas mixture obtained from alkaline water electrolysis and a carbon source is not separated into their components but rather is introduced into a catalyzed reactor, in order to achieve conversion to synthetic 2nd generation biofuels, such as biomethane, biomethanol, bio-dimethyl ether, etc. Tests have been previously executed in a pilot electrolyzer and reactor of 1 kW, and are now being scaled up to a pilot electrolyzer and reactor of 5 kW, producing 250 l/h CH4, as an intermediate step to a pilot of 100 kW.
- The role of higher education towards sustainable consumption behavioural change: topics discussed in sustainable consumption educationPublication . João, Isabel; Silva, JoãoMany factors have contributed to the urgency of adopting sustainable consumption patterns. The main challenges related to sustainable consumption include respecting our planet in all its complexity and diversity, taking care of communities and life and, to this end, adopting consumption and production patterns that protect human rights and the well-being of communities. The theme of education for sustainable consumption is multidisciplinary with elements from different curriculum topics and addresses all aspects of everyday life. Establishing a common understanding of sustainable consumption education will create a shared understanding of the topic and improve the necessary cooperation between educators and students, making sustainable consumption education an established and more understandable topic. The objective of this study is to identify the main topics within sustainable consumption education teaching and learning, the main barriers and propose some actions that contribute to a sustainable behavioural change in higher education. A literature review was carried out to categorize the main topics related to teaching and learning processes in higher education environments to fill the gap in this underrepresented research topic of education for sustainable consumption. The review allowed the collection of information reported in this new millennium and the identification, selection and critical evaluation of research related to education for sustainable consumption.