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- A new method to predict genotoxic effects based on serum molecular profilePublication . Araújo, Rúben; Ramalhete, Luís; Paz, Hélder; Ladeira, Carina; Calado, CecíliaIt is critical to develop new methods to assess genotoxic effects in human biomonitoring since the conventional methods are usually laborious, time-consuming, and expensive. It is aimed to evaluate if the analysis of a drop of serum by Fourier Transform Infrared spectroscopy, allow to assess genotoxic effects in occupational exposure to cytostatic drugs in hospital professionals, as obtained by the lymphocyte cytokinesis-block micronucleus assay. It was considered peripheral blood from hospital professionals exposed to cytostatic drugs (n = 22) and from a non-exposed group (n = 36). It was observed that workers occupationally exposed presented a higher number of micronuclei (p < 0.05) in lymphocytes, in relation to the non-exposed group. The serum Fourier Transform Infrared spectra from exposed workers presented diverse different peaks (p < 0.01) in relation to the non-exposed group. The hierarchical cluster analysis of serum spectra separated serum samples of the exposed group from the non-exposed group with 61% sensitivity and 88% specificity. A support vector machine model of serum spectra enables to predict exposure with high accuracy (0.91), precision (0.89), sensitivity (0.86), F1 score (0.87) and AUC (0.96). Therefore, Fourier Transform Infrared spectroscopic analysis of a drop of serum enabled to predict in a rapid and simple mode the genotoxic effects of cytostatic drugs. The method presents therefore potential for high-dimension screening of exposure of genotoxic substances, due to its simplicity and rapid setup mode.
- Predicting cellular rejection of renal allograft based on the serum proteomic fingerprintPublication . Ramalhete, Luís; Vieira, Miguel Bigotte; Araújo, Rúben; Vigia, Emanuel; Aires, Inês; Ferreira, Aníbal; Calado, CecíliaKidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.
- A Simple, label-free, and high-throughput method to evaluate the epigallocatechin-3-gallate impact in plasma molecular profilePublication . Araújo, Rúben; Ramalhete, Luís; Da Paz, Helder; Ribeiro, Edna; Calado, CecíliaEpigallocatechin-3-gallate (EGCG), the major catechin presente in green tea, presents diverse appealing biological activities, such as antioxidative, anti-inflammatory, antimicrobial, and antiviral activities, among others. The present work evaluated the impact in the molecular profile of human plasma from daily consumption of 225 mg of EGCG for 90 days. Plasma from peripheral blood was collected from 30 healthy human volunteers and analyzed by high-throughput Fourier transform infrared spectroscopy. To capture the biochemical information while minimizing the interference of physical phenomena, several combinations of spectra pre-processing methods were evaluated by principal component analysis. The pre-processing method that led to the best class separation, that is, between the plasma spectral data collected at the beginning and after the 90 days, was a combination of atmospheric correction with a second derivative spectra. A hierarchical cluster analysis of second derivativespectraalsohighlightedthefactthatplasmaacquiredbeforeEGCGconsumptionpresented a distinct molecular profile after the 90 days of EGCG consumption. It was also possible by partial least squares regression discriminant analysis to correctly predict all unlabeled plasma samples (not used for model construction) at both timeframes. We observed that the similarity in composition among the plasma samples was higher in samples collected after EGCG consumption when compared with the samples taken prior to EGCG consumption. Diverse negative peaks of the normalized second derivative spectra, associated with lipid and protein regions, were significantly affected (p < 0.001) by EGCG consumption, according to the impact of EGCG consumption on the patients’ blood, low density and high density lipoproteins ratio. In conclusion, a single bolus dose of 225 mg of EGCG, ingested throughout a period of 90 days, drastically affected plasma molecular composition in all participants, which raises awareness regarding prolonged human exposure to EGCG. Because the analysis was conducted in a high-throughput, label-free, and economic analysis, it could be applied to high-dimension molecular epidemiological studies to further promote the understanding of the effect of bio-compound consumption mode and frequency.
- Discriminating B and T-lymphocyte from its molecular profile acquired in a label-free and high-throughput methodPublication . Ramalhete, Luís; Araújo, Rúben; Calado, CecíliaB and T-lymphocytes are one of the main players of the adaptive immune system and consequently, the ability to discriminate these cells is relevant in diverse pathophysiological cases. In the present work, FTIR spectroscopy was used to acquire the cells molecular profile in a label-free, simple, rapid, economic, and high-throughput mode. It was possible, by hierarchical cluster analysis of the second derivative spectra, between 900 to 1500 cm-1, to correctly classify 98% of samples as B and T-lymphocyte. It was also possible to develop a very good support vector machine model, which could predict B and T-lymphocyte from the second derivative spectra, with high accuracy (0.95), precision (0.95), recall (0.95), and F1 score (0.95). The method developed presents therefore, appealing characteristics to promote a new diagnostic tool to analyze and discriminate B from T-lymphocytes.
- 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.
- Early mortality prediction in intensive care unit patients based on serum metabolomicPublication . Araújo, Rúben; Ramalhete, Luís; Von Rekowski, Cristiana; Fonseca, Tiago AH; 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.
- 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.
- 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.
- Discovery of delirium biomarkers through minimally invasive serum molecular fingerprinting.Publication . Viegas, Ana; Araújo, Rúben; Ramalhete, Luís; Von Rekowski, Cristiana; Fonseca, Tiago AH; Bento, Luís; Calado, CecíliaDelirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.