ISEL - Engenharia Química e Biológica
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Browsing ISEL - Engenharia Química e Biológica by Field of Science and Technology (FOS) "Engenharia e Tecnologia::Engenharia Química"
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- Analysis of integrated calcium looping alternatives in a cement plantPublication . Amorim, Ana; Filipe, Rui; Matos, Henrique A.Calcium looping is a promising post-combustion CO2 capturing technology, highly compatible with the cement industry, one of the major industrial sources of CO2 emissions. Limestone, a raw material for clinker, forms lime, a calcium looping adsorbent. Thus, it is possible to maximize the synergies between a cement plant and a calcium looping unit by establishing an integrated configuration. Nevertheless, the integration of calcium looping in cement plants has not yet been thoroughly studied. This study examines different integration alternatives, developing models for the preheater and calciner using Aspen Plus, validated with operational data, alongside an entrained-flow carbonator model considering adsorbent deactivation. By combining these models, six integrated configurations are proposed and compared with the tail-end calcium looping configuration. The integrated configurations show a reduction in fuel consumption and net energy consumption for the same CO2 avoided emissions. The most promising configuration was identified and a comparative techno-economic analysis was conducted.
- Chitosan nanoparticles for enhanced immune response and delivery of multi-epitope helicobacter pylori vaccines in a BALB/c mouse modelPublication . Amaral, Rita; Concha, Tomás ; Vítor, Jorge; Almeida, António J.; Calado, Cecília; Diogo Gonçalves, Lídia MariaHelicobacter pylori is the leading cause of chronic gastritis, peptic ulcer, gastric adenocarcinoma, and mucosal-associated lymphoma. Due to the emerging problems with antibiotic treatment against H. pylori in clinical practice, H. pylori vaccination has gained more interest. Oral immunization is considered a promising approach for preventing initial colonization of this bacterium in the gastrointestinal tract, establishing a first line of defense at gastric mucosal surfaces. Chitosan nanoparticles can be exploited effectively for oral vaccine delivery due to their stability, simplicity of target accessibility, and beneficial mucoadhesive and immunogenic properties. Methods: In this study, new multi-epitope pDNA- and recombinant protein-based vaccines incorporating multiple H. pylori antigens were produced and encapsulated in chitosan nanoparticles for oral and intramuscular administration. The induced immune response was assessed through the levels of antigen-specific IgGs, secreted mucosal SIgA, and cytokines (IL-2, IL-10, and IFN-γ) in immunized BALB/C mice. Results: Intramuscular administration of both pDNA and recombinant protein-based vaccines efficiently stimulated the production of specific IgG2a and IgG1, which was supported by cytokines levels. Oral immunizations with either pDNA or recombinant protein vaccines revealed high SIgA levels, suggesting effective gastric mucosal immunization, contrasting with intramuscular immunizations, which did not induce SIgA. Conclusions: These findings indicate that both pDNA and recombinant protein vaccines encapsulated into chitosan nanoparticles are promising candidates for eradicating H. pylori and mitigating associated gastric diseases in humans.
- 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.
- Cytokine-based insights into bloodstream infections and bacterial gram typing in ICU COVID-19 patientsPublication . Araújo, Rúben Alexandre Dinis; Ramalhete, Luís; Von Rekowski, Cristiana; Henrique Fonseca, Tiago Alexandre; Calado, Cecília; Bento, LuísTimely and accurate identification of bloodstream infections (BSIs) in intensive care unit (ICU) patients remains a key challenge, particularly in COVID-19 settings, where immune dysregulation can obscure early clinical signs. Methods: Cytokine profiling was evaluated to discriminate between ICU patients with and without BSIs, and, among those with confirmed BSIs, to further stratify bacterial infections by Gram type. Serum samples from 45 ICU COVID-19 patients were analyzed using a 21-cytokine panel, with feature selection applied to identify candidate markers. Results: A machine learning workflow identified key features, achieving robust performance metrics with AUC values up to 0.97 for BSI classification and 0.98 for Gram typing. Conclusions: In contrast to traditional approaches that focus on individual cytokines or simple ratios, the present analysis employed programmatically generated ratios between pro-inflammatory and anti-inflammatory cytokines, refined through feature selection. Although further validation in larger and more diverse cohorts is warranted, these findings underscore the potential of advanced cytokine-based diagnostics to enhance precision medicine in infection management.
- 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.
- Impact of neutrophil-activating protein conservation on diagnostic tests and vaccine designPublication . Diogo Gonçalves, Lídia Maria; Almeida, António J.; Calado, CecíliaBACKGROUND: The neutrophil activating protein (NAP) is a highly immunogenic and virulence factor of Helicobacter pylori, presenting inflammatory and immunomodulatory activity. Consequently, NAP has been explored as a diagnostic and therapeutic target. However, when evaluating a target protein to design diagnostic methods or vaccines, it is critical to determine the protein conservation among the bacterial population, as well the impact of alterations of amino acid residues on the protein antigenic profile. RESULTS: In the present work, NAP conservation and theoretical antigenicity were determined among 51 sequences from H. pylori isolated from patients worldwide. A high NAP conservation (83%) was observed, where 17 amino acid residues, among the 144 residues of the protein, were polymorphic. Alterations at these polymorphic sites had a theoretically low impact on predicted antigenicity, where only 5 NAPs out of 51 NAPs presented a slightly different antigenic profile in relation to the consensus sequence. According to that, it was possible to recognize in western blotting 93% of NAP from different bacteria (n = 15) using polyclonal antibodies developed against a specific NAP. CONCLUSIONS: It was predicted that when working with polyclonal antibodies or large NAP fragments for diagnostic and vaccine design, slight variation in protein sequence will have a minimal impact on NAP recognition. However, if a NAP monoclonal antibody or small NAP epitopes are considered, it is critical to select the most conserved and antigenic NAP regions, to maximize the coverage of NAP variants.
- 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.
- Integration of FTIR spectroscopy and machine learning for kidney allograft rejection: a complementary diagnostic toolPublication . Ramalhete, Luís; Araújo, Rúben Alexandre Dinis; Bigotte Vieira, Miguel; Vigia, Emanuel; Aires, Inês; Ferreira, Aníbal; Calado, CecíliaKidney transplantation is a life-saving treatment for end-stage kidney disease, but allograft rejection remains a critical challenge, requiring accurate and timely diagnosis. The study aims to evaluate the integration of Fourier Transform Infrared (FTIR) spectroscopy and machine learning algorithms as a minimally invasive method to detect kidney allograft rejection and differentiate between T Cell-Mediated Rejection (TCMR) and Antibody-Mediated Rejection (AMR). Additionally, the goal is to discriminate these rejection types aiming to develop a reliable decision-making support tool. Methods: This retrospective study included 41 kidney transplant recipients and analyzed 81 serum samples matched to corresponding allograft biopsies. FTIR spectroscopy was applied to pre-biopsy serum samples, and Naïve Bayes classification models were developed to distinguish rejection from non-rejection and classify rejection types. Data preprocessing involved, e.g., atmospheric compensation, second derivative, and feature selection using Fast Correlation-Based Filter for spectral regions 600–1900 cm−1 and 2800–3400 cm−1. Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The Naïve Bayes model achieved an AUC-ROC of 0.945 in classifying rejection versus non-rejection and AUC-ROC of 0.989 in distinguishing TCMR from AMR. Feature selection significantly improved model performance, identifying key spectral wavenumbers associated with rejection mechanisms. This approach demonstrated high sensitivity and specificity for both classification tasks. Conclusions: The integration of FTIR spectroscopy with machine learning may provide a promising, minimally invasive method for early detection and precise classification of kidney allograft rejection. Further validation in larger, more diverse populations is needed to confirm these findings’ reliability.
- One-pot microwave-assisted synthesis of fluorescent carbon dots from tomato industry residues with antioxidant and antibacterial activitiesPublication . Barata, Patrícia; Costa, Alexandra; Martins, Sónia; Semedo, Magda; Prata, José Virgílio; Costa, AlexandraTomato waste (TW) was employed as a sustainable source for the synthesis of fluorescent carbon dots (CDs) via a microwave-assisted hydrothermal carbonization (Mw-HTC) method, aiming at its valorization. Several amines were used as nitrogen additives to enhance the fluorescence quantum yield (QY) of CDs, and a set of reaction conditions, including additive/TW mass ratio (0.04–0.32), dwell time (15–60 min), and temperature (200–230 °C) of the HTC process, were scrutinized. The structural analysis of the tomato waste carbon dots (TWCDs) was undertaken by FTIR and 1H NMR techniques, revealing their most relevant features. In solid state, transmission electron microscopy (TEM) analysis showed the presence of nearly spherical nanoparticles with an average lateral size of 8.1 nm. Likewise, the topographical assessment by atomic force microscopy (AFM) also indicated particles’ heights between 3 and 10 nm. Their photophysical properties, revealed by UV–Vis, steady-state, and time-resolved fluorescence spectroscopies, are fully discussed. Higher photoluminescent quantum yields (up to 0.08) were attained when the biomass residues were mixed with organic aliphatic amines during the Mw-HTC process. Emission tunability is a characteristic feature of these CDs, which display an intensity average fluorescence lifetime of 8 ns. The new TWCDs demonstrated good antioxidant properties by the ABTS radical cation method (75% inhibition at TWCDs’ concentration of 5 mg/mL), which proved to be related to the dwell time used in the CDs synthesis. Moreover, the synthesized TWCDs suppressed the growth of Escherichia coli and Staphylococcus aureus at concentrations higher than 2000 μg/mL, encouraging future antibacterial applications.
- 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.