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- Growth Performance after Agouti-Signaling Protein 1 (Asip1) Overexpression in Transgenic ZebrafishPublication . Godino-Gimeno, Alejandra; Sánchez, Elisa; Guillot, Raúl; Rocha, Ana; Angotzi, Anna Rita; Leal, Esther; Rotllant, Josep; Cerdá-Reverter, José MiguelThe melanocortin system is a key structure in the regulation of energy balance. Overexpression of inverse agonists, agouti-signaling protein (ASIP), and agouti-related protein (AGRP) results in increased food intake, linear growth, and body weight. ASIP regulates dorsal-ventral pigment polarity through melanocortin 1 receptor (MC1R) and overexpression induces obesity in mice by binding to central MC4R. Asip1 overexpression in transgenic zebrafish (asip1-Tg) enhances growth, yet experiments show fish overexpressing Asip1 do not develop obesity even under severe feeding regimes. Asip1-Tg fish do not need to eat more to grow larger and faster; thus, increased food efficiency can be observed. In addition, asip1-Tg fish reared at high density are able to grow far more than wild-type (WT) fish reared at low density, although asip1-Tg fish seem to be more sensitive to crowding stress than WT fish, thus making the melanocortin system a target for sustainable aquaculture, especially as the U.S. Food and Drug Association has recently approved transgenic fish trading.
- Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysisPublication . Rocha, Ana; Costeira, Cristina; Barbosa, Raul; Gonçalves, Florbela; Castelo-Branco, Miguel; Viana, Joaquim; Gaudêncio, Margarida; Ventura, FilipaBackground Oncology nurses face unique and intense demands due to the nature of their work, caring for patients with life-threatening illnesses. The emergence of professional burnout among these nurses is influenced by several factors, highlighting the importance of identifying protective and risk factors to mitigate its impact. This study aims to identify burnout profiles and protective socio-demographic and work-related patterns associated with reduced burnout among oncology nurses. Methods A cross-sectional study was conducted with 150 oncology nurses at a specialized hospital exclusively dedicated to adult oncology treatment in Portugal. Data collection included a self-administered questionnaire incorporating the validated Portuguese version of Maslach Burnout Inventory (MBI). Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. Results Six protective patterns against burnout were identified, characterized by conditions of permanent contracts, work-life balance, and supportive work environments. Moreover, factors such as holding management roles and being a parent of two or more children might even be protective in some circumstances, suggesting a nuanced relation between personal and professional factors. Machine learning analyses made apparent the unpredictability of burnout and highlighted the critical role of protective factors in mitigating its impact. Conclusions This study underscores the importance of resilience-building strategies and promoting protective factors, such as job stability, learned experience, and adequate rest, to reduce burnout risk among oncology nurses. Future research should validate these findings through hypothesis-driven analyses to inform targeted and context-specific burnout prevention programs.