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- 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 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.
- Plasma versus serum analysis by FTIR spectroscopy to capture the human physiological statePublication . Araújo, Rúben; Ramalhete, Luis; Ribeiro, Edna; Calado, CecíliaFourier Transform InfraRed spectroscopy of serum and plasma has been highly explored for medical diagnosis, due to its general simplicity, and high sensitivity and specificity. To evaluate the plasma and serum molecular fingerprint, as obtained by FTIR spectroscopy, to acquire the system metabolic state, serum and plasma spectra were compared to characterize the metabolic state of 30 human volunteers, between 90 days consumption of green tea extract rich in Epigallocatechin 3-gallate (EGCG). Both plasma and serum spectra enabled the high impact of EGCG consumption on the biofluid spectra to be observed, as analyzed by the spectra principal component analysis, hierarchical-cluster analysis, and univariate data analysis. Plasma spectra resulted in the prediction of EGCG consumption with a slightly higher specificity, accuracy, and precision, also pointing to a higher number of significant spectral bands that were different between the 90 days period. Despite this, the lipid regions of the serum spectra were more affected by EGCG consumption than the corresponding plasma spectra. Therefore, in general, if no specific compound analysis is highlighted, plasma is in general the advised biofluid to capture by FTIR spectroscopy the general metabolic state. If the lipid content of the biofluid is relevant, serum spectra could present some advantages over plasma spectra.
- 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.
- 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.
- 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.