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- 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.
- Relating workaholism to job stress: serial mediating role of job satisfaction and psychological capital of nurses in AngolaPublication . Geremias, Rosa LuteteBackground/Objectives: Previous studies conducted in sub-Saharan African countries have concentrated on examining the challenges of nursing training and the organizational commitment of healthcare professionals, with little attention paid to exploring the mechanisms that contribute to reducing nurses’ job stress. Consequently, the present study addresses a significant gap in the literature by offering an overview of the factors contributing to understanding job stress among nurses in Angola. This study aimed to analyze the direct and indirect relationship between workaholism and job stress with job satisfaction and psychological capital mediating this relationship. Methods: Using the quantitative methodology with a cross-sectional design, a questionnaire was administered to 340 nurses (172 men and 168 women). Results: The results confirmed that workaholism is positively related to job stress and that job satisfaction and psychological capital serially mediate the relationship between workaholism and job stress. Conclusions: These findings highlight the importance of fostering job satisfaction and psychological capital by establishing favorable work environments and promoting nurses’ physical and emotional well-being. In addition, these results may encourage healthcare leaders to create well-designed break areas for nurses to take restorative breaks.