ISEL - Eng. Quim. Biol. - Comunicações
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- Gold nanoparticles functionalized with antibodies: a colorimetric analysis for implementation in PoC devicesPublication . Serafinelli, Caterina; Fantoni, Alessandro; Alegria, Elisabete C.B.A.; Pacheco, Rita; Vieira, Manuela; Fantoni, Alessandro; Alegria, Elisabete; Pacheco, Rita; Vieira, ManuelaThe growing interest in the fast and easy identification of many biomarkers for several diseases, supporting a future precision medicine, results in the development of miniaturized, user-friendly, automated, and portable sensing systems able to provide a real-time and reliable response. Herein we evaluate the sensing capability of gold nanoflowers (AuNFs) for their integration in the setup of the Color Picker System, a Point-of-Care (PoC) sensing device developed by our group for a user-friendly colorimetric detection of molecules. A simulation study measures the color changes arising from the variations in the dielectric environment around the AuNFs surface. The process starts with the synthesis of AuNFs and their functionalization with 3-Mercaptopropionic acid. The measured transmittance spectra of the AuNFs before and after the functionalization, have been combined with the spectra of two different light sources to simulate the spectral response of the light transmitted through the colloidal dispersion. The calculated spectra have been converted into vectors in different color spaces producing the color chart. The sensitivity of the AuNFs has been evaluated as Euclidian distance between two consecutive points in the color chart, but also as differences in the RGB channels, that are the one returned from the Color Picker System. The AuNFs showed the greatest distances in the color chart and the greatest differences in the RGB when using the Cold LED as light source. The chromatic changes arising from their use make the AuNFs a potential sensing platform to be inserted into the Color Picker System for antibodies sensing. © 2025 SPIE.
- Gold nanoflowers for colorimetric detection of NGAL antibodiesPublication . Serafinelli, Caterina; Fantoni, Alessandro; Alegria, Elisabete C. B. A.; Pacheco, Rita; Vieira, Manuela; Vieira, Manuela; Pacheco, Rita; Alegria, Elisabete; Fantoni, AlessandroColorimetric sensors are a type of sensing platform enabling visual measurements that offers an instant report. Due to their intrinsic advantages, such as low cost, simplicity, low acquisition time and no need for expensive instrumentation, colorimetric transducers are attracting a growing attention for the PoC devices. In light of their brilliant colors arising from their Localized Surface Plasmon Resonance (LSPR), gold nanoparticles have revealed a great potential to develop new analytical techniques and advanced assays. Stimulated by the need of PoC devices for an affordable and real-time detection of biomarkers outside clinical environment, in this work we explore the potential of gold nanoflowers (AuNFs) as sensing system to integrate in a PoC device. Firstly, gold AuNFs functionalized with NGAL antibodies along with antibodies conjugated onto AuNFs surface binding their complementary antigens will be synthesized and their transmittance spectra measured. In a successive step, the measured spectra have been converted into vectors in the L*a*b*, xyY and RGB Color spaces by means of simulation study. Transferring the study into real world application, the AuNFs will be integrated into the fiber network of paper to create the plasmonic paper that will be the transducer of a colorimetric device.
- Survivability prediction based on the serum molecular fingerprint in critically ill patientsPublication . Correia, Inês; Araújo, Rúben Alexandre Dinis; Henrique Fonseca, Tiago Alexandre; Von Rekowski, Cristiana; Bento, Luís; Calado, Cecília; Domingues, Nuno; Cardoso, L. M.; Thaweesak, Y.It is relevant to discover biomarkers enabling to predict critically ill patients’ survival. This study focused on 45 patients, from which 22 deceased and 23 were discharged from an Intensive Care Unit (ICU). It was considered the serum molecular fingerprint, as acquired by Fourier Transform Infra-Red (FTIR) spectroscopy, obtained 3 days before the patients discharged or death at the ICU. It was possible to obtain ratios of bands of the sera spectra, statistically different between the two groups of patients. Furthermore, good Naïve Bayes models were developed based on the second derivative spectra enabling an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.77. These promising outputs suggest further investigation with a larger cohort.
- Trends in COVID-19 patient characteristics and mortality throughout the pandemic: insights from a portuguese single-centre studyPublication . Von Rekowski, Cristiana; Pinto, Iola; Henrique Fonseca, Tiago Alexandre; Araújo, Rúben Alexandre Dinis; Ferreira, Artur; Calado, Cecília; Bento, Luís; Domingues, Nuno; Tomar, Rajesh Singh; Mahamud, TosapornAs SARS-CoV-2 continues to circulate globally and new variants emerge, it remains relevant to gather data on the affected patients’ clinical characteristics and outcomes to understand how individual factors and public health measures affect prognosis. Thus, we analyzed data of 870 ICU patients admitted for COVID-19 across two distinct phases of the pandemic: before and after the introduction of immunization. Experimental results showed that vaccination significantly impacted patient demographics after the third wave, and that waves number two and three, dominated by the EU1 and Alpha variants, had higher mortality. Older age, the need for invasive mechanical ventilation, and hematologic cancer were significantly associated with an increased risk of death in the adjusted multivariable model (AUC: 0.778, 95% CI 0.746-0.810, p<0.001). As the pandemic progressed, while some public health interventions influenced the observed trends, individual patient characteristics had a more substantial impact on their outcome.
- 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.
- 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.
- 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.
- 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.