Percorrer por autor "Fonseca, Tiago Alexandre Henrique"
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- Biomarkers discovery for prognosis of COVID-19 based on metabolomicsPublication . Fonseca, Tiago Alexandre Henrique; Calado, Cecília C.; Oliveira, Conceição; Bento, LuísABSTRACT - Background and Goals: A novel coronavirus strain, SARS-CoV-2, emerged in late 2019, generating a viral epidemic. This new, highly transmissible strain outnumbered both SARS and MERS in terms of affected people. Symptoms of the novel virus included fever, cough, and chest pain, as well as dyspnea and bilateral lung infiltration in severe instances. Due to the relevance of the COVID pandemic, this thesis aims to develop a predictive model for the outcome of COVID-19 critically ill patients, in Intensive Care Unit (ICU) based on a metabolomic serum analysis, acquired by Fourier-transform infrared spectroscopy (FTIR) and liquid chromatography coupled to mass spectrometry (LC-MS). Methods: Two assay groups were analyzed based on Fourier-transform infrared (FTIR) spectroscopy and liquid chromatography associated with mass spectrometry (LC-MS). The first experiment aimed to evaluate the influence of two distinct metabolite extraction techniques on the samples' metabolome, namely methanol and acetonitrile: methanol:water solvent mixture on 6 patients. It was conducted prediction for the outcome of these patients as well as the evaluation of the sera’s metabolic profile with FTIR spectroscopic and LC-MS data. The second experiment used a larger patient sample size (n=24) and evaluated the serum metabolome extracted with acetonitrile:methanol:water protocol based on the patient’s condition, non-ventilated discharged from ICU (n=8), ventilated and deceased in ICU (n=8), and ventilated discharged from ICU (n=8), along with the development of an outcome prediction model, using metabolite analysis. Results: Methanol as a solvent for metabolite extraction resulted in extracting a higher content of lipids in comparison with acetonitrile:methanol:water solvent mixture, which resulted in a higher peptide output. On the first assay, based on FTIR spectroscopy, with was possible to predict the patients’ survivability with an Area Under the Curve (AUC) of 0.98 and a CA of 0.97 regardless of the extraction method for the first assay. In the second assay, metabolites were extracted based on the acetonitrile:methanol:water protocol. For FTIR spectral data, prediction algorithms achieved a CA of 0.85 for the prediction between non-ventilated and ventilated discharged patients, 0.85 for the distinction between non-ventilated discharged and ventilated deceased patients, and 0.77 for the distinction between ventilated discharged and ventilated deceased patients. Based on LC-MS data, it was possible to achieve CA’s of 1.00 when predicting the ventilation status between discharged patients and for non-ventilated discharged patients and outcome between non-ventilated and ventilated patients, and 0.96 for distinction between ventilated discharged patients and ventilated deceased patients. Conclusions: The metabolome extraction from serum based on acetonitrile:methanol:water protocol enabled to prediction of the outcome and condition regarding ventilation of COVID-19 patients in the ICU. These results were obtained by two different techniques, FTIR spectroscopy and LC-MS. Therefore, serum metabolomics presented as a useful technique that could significantly contribute to better management of critical patients, such as the ones with severe status of COVID-19. Irrespective of the positive results obtained with the algorithms for predicting patient outcomes, it is crucial to note that the study samples were quite small. As a conclusion, further research is necessary to confirm the results of this study.
- Biomarkers discovery for prognosis of COVID-19 based on metabolomicsPublication . Fonseca, Tiago Alexandre Henrique; Calado, Cecília R.C.; Oliveira, Conceição; Bento, LuísBackground and Goals: A novel coronavirus strain, SARS-CoV-2, emerged in late 2019, generating a viral epidemic. This new, highly transmissible strain outnumbered both SARS and MERS in terms of affected people. Symptoms of the novel virus included fever, cough, and chest pain, as well as dyspnea and bilateral lung infiltration in severe instances. Due to the relevance of the COVID pandemic, this thesis aims to develop a predictive model for the outcome of COVID-19 critically ill patients, at Intensive Care Unit (ICU) based on a metabolomic serum analysis, acquired by Fourier-transform infrared spectroscopy (FTIR) and liquid chromatography coupled to mass spectrometry (LC-MS). Methods: Two assay groups were analysed based on Fourier-transform infrared (FTIR) spectroscopy and liquid-chromatography associated to mass spectrometry (LC-MS). The first experiment aimed to evaluate the influence of two distinct metabolite extraction techniques on the samples metabolome, namely methanol and acetonitrile:methanol:water solvent mixture on 6 patients. It was conducted prediction for the outcome of these patients as well the evaluation of the sera’s metabolic profile with FTIR spectroscopic and LC-MS data. The second experiment used a larger patient sample size (n=24) and evaluated the serum metabolome extracted with acetonitrile:methanol:water protocol based on the patients’ condition, non-ventilated discharged from ICU (n=8), ventilated and deceased in ICU (n=8), and ventilated discharged from ICU (n=8), along with the development of an outcome prediction model, using metabolite analysis. Results: Methanol as a solvent for metabolite extraction resulted in extracting higher content of lipids in comparison with acetonitrile:methanol:water solvent mixture, which resulted in a higher peptide output. On the first assay, based on FTIR spectroscopy, with was possible to predict the patients’ survivability with an Area Under the Curve (AUC) of 0.98 and a CA of 0.97 regardless from the extraction method for the first assay. In the second assay, metabolites were extracted based on the acetonitrile:methanol:water protocol. For FTIR spectral data, prediction algorithms achieved a CA of 0.85 for prediction between non-ventilated and ventilated discharged patients, and 0.85 for distinction between non-ventilated discharged and ventilated deceased patients and 0.77 for distinction between ventilated discharged and ventilated deceased patients. Based on LC-MS data, it was possible to achieve CA’s of 1.00 when predicting the ventilation status between discharged patients and for non-ventilated discharged patients and outcome between non-ventilated and ventilated patients, and 0.96 for distinction between ventilated discharged patients and ventilated deceased patients. Conclusions: The metabolome extraction from serum based on acetonitrile:methanol:water protocol enabled to predict the outcome and condition regarding ventilation of COVID-19 patients in ICU. These results were obtained by two different techniques, FTIR spectroscopy and LC-MS. Therefore, serum metabolomics presented as a useful technique that could significantly contribute to a better management of critical patients, as the ones in severe status of COVID-19. Irrespective from the positive results obtained with the algorithms for predicting patient outcomes, it is crucial to note that the study samples were quite small. As a conclusion, further research is necessary to confirm the results of this study.