A Parallel Process of Staff-Family Distress in Long-Term Care: A Challenge to Collaboration.

Introduction: Supporting persons living with advanced dementia in long-term care (LTC) homes requires strong collaborative partnerships between staff, family members, and residents. Yet, relational tensions-such as differing expectations around care decisions-can inhibit the implementation of collaborative partnerships at this critical point in the trajectory of care.

Objective: This study aims to explore the emotional experiences of families and staff during shared decision-making processes for individuals with advanced dementia in LTC.

Incidental durotomy in lumbar decompressive surgery: incidence and risk-factors, and the effect of durotomy on hospital and patient metrics.

Purpose: To evaluate incidental lumbar durotomy incidence and risk-factors, and the association of durotomy with perioperative metrics and patient-reported outcomes.

Methods: A total 3140 cases of 1-3 level elective decompressive surgery from 2008 to 2023 at a single centre were included. Multivariable analysis was performed on literature derived variables to identify independent risk-factors for durotomy.

Energy-based bond graph models of glucose transport with SLC transporters.

The SLC (solute carrier) superfamily mediates the passive transport of small molecules across apical and basolateral cell membranes in nearly all tissues. In this paper, we employ bond-graph approaches to develop models of SLC transporters that conserve mass, charge, and energy, respectively, and can be parameterized for a specific cell and tissue type for which the experimental kinetic data are available. We show how analytic expressions that preserve thermodynamic consistency can be derived for a representative four- or six-state model, given reasonable assumptions associated with steady-state flux conditions.

Are more data always better? - Machine learning forecasting of algae based on long-term observations.

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool.