Preparing for Death While Investing in Life: A Narrative Inquiry and Case Report of Home-Based Paediatric Palliative, End-of-Life, and After-Death Care.

Paediatric palliative care is pivotal for addressing the complex needs of children with incurable diseases and their families. While home-based care offers a familiar and supportive environment, delivering comprehensive services in this context is challenging. The existing literature on home-based palliative care lacks detailed guidance for its organization and implementation. This qualitative narrative inquiry explores the organization and provision of home-based paediatric palliative care. Data were collected from healthcare practitioners using conversations, storytelling, and reflective journaling. Schwind's Narrative Reflective Process was applied to synthesize the data, resulting in an in-depth case description. The narrative approach illuminates the complexities of home-based paediatric palliative, end-of-life, and after-death care. Key findings encompass the importance of early-care coordination, interprofessional collaboration, effective symptom management, emotional and psychosocial support, and comprehensive end-of-life planning. Through the case study of the child patient, the challenges and strategies for providing holistic, family-centred care within the home environment are described. Practical insights gained from this report can inform the development and improvement of home-based palliative care programs, benefiting researchers, practitioners, and policymakers seeking to optimize care for children and families in similar contexts.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement.

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.