This article focuses on primary liver cancer and tries to present a possible CBT-Cognitive Behavioral Therapy group intervention of 6 sessions and a follow up based on a scientific review. We present an international national data analyze of the problem and characterize it in terms of symptoms, diagnosis and treatment. From the psychological point of view our goal is to show and reflect on the connection between theory and practice in research mental health and the quality of life of people suffering from liver cancer. CBT interventions programme have shown efficacy and effectiveness on improving quality of life of patients. The expected outcomes of CBT intervention are mainly a better adaptation to cancer and a better perception on quality of live.

Breast cancer is the most prevalent cancer in women worldwide, with approximately two million new cases every year. The number of cases increases despite the high survival rate. The aim of this study is, therefore, to understand this cancer by finding out what has been studied in this area using scientific evidence published between 2003 and 2023. A search was therefore carried out for scientific articles and other relevant sources on the subject with free access, and 48 documents were then analyzed. According to the analysis, many studies have been conducted in the area, particularly on quality of life and body image. However, little has been done in terms of environmental sustainability and breast cancer.

Avian influenza (bird flu) occurs sporadically in American poultry flocks, decimating these flocks and causing substantial economic losses. Avian influenza also impacts the beliefs of food handlers and preparers in the home (home cooks). Although those who properly handle and prepare processed poultry products cannot succumb to avian influenza, there is a widespread belief that one can contract the bird flu from these foods. Beliefs about getting avian influenza from poultry products and intentions to avoid consuming poultry products are the focus of this study of 285 home cooks. False beliefs about getting avian influenza from handling, preparing, and consuming poultry products are apparent in this sample. Correlational analysis also shows that those holding the false beliefs intend to act upon those beliefs by planning not to consume poultry products. Moreover, the false beliefs about contracting avian influenza from poultry products are correlated with a bias to see oneself as less likely to produce food that contains foodborne diseases. These findings are consistent with, and contribute to, the research literatures on belief formation and change, behavioral intentions, and with research showing how guilt by association thought processes underlie false beliefs related to food safety. This research has important implications for poultry and other food processing industries, and for campaigns to persuade the public about real and imaginary risks associated with particular food products.

Mathematical modeling plays a central role in infectious disease epidemiology, shaping outbreak response strategies and informing public health policy. The COVID-19 pandemic demonstrated the value of these models but also exposed persistent limitations related to data fragility, lack of transparency, limited stakeholder engagement, and insufficient consideration of social and political contexts. Rather than critiquing modeling as a discipline, this perspective argues for a reorientation of infectious disease modeling toward a more responsive, equity-centered, and participatory paradigm. We propose a conceptual framework built on three interrelated principles: adaptability through real-time data integration, transparency via open-source and reproducible practices, and relevance through interdisciplinary and co-produced model design. Drawing on illustrative examples from COVID-19 and dengue control efforts, we highlight how integrating behavioral dynamics, local knowledge, and policy feedback can improve model usefulness and public trust. Reconceptualizing models as dynamic systems of inquiry rather than static forecasting tools can enhance decision-making and promote more equitable and effective responses to future public health emergencies.