Investigating Inpatient Acceptance of a Unique Telemedicine Service Trialled in the Acute Ward in Rural Australia.

This study investigated inpatient acceptance of a unique telemedicine clinical service piloted from December 2022 to June 2025 in 3 rural acute wards in Victoria, Australia. The use of virtual care was complementary to the visiting general practitioner (GP) model common in rural hospitals. The qualitative study employed 3 researcher-designed questions: Did you feel safe using the virtual healthcare doctor?; Did you feel the care you experienced was as it should be? And; If you were offered virtual care again, would you use it? Participants ( = 38) were predominantly over 65 years (95%).

Gait pattern differences in unilaterally affected children with cerebral palsy and children with acquired brain insult.

Background: Children with cerebral palsy (CP) or acquired brain insult (ABI) present with motor disorders affecting movement, muscle tone, and posture. While CP is commonly a consequence of perinatal brain insult (PBI), pediatric ABI can occur between birth and adolescence, with movement patterns that may not be consistent with CP.

Research Question: Are gait patterns associated with CP different from those with pediatric ABI?

Materials/methods: Children with unilateral motor impairment and history of ABI at ≥18 months of age were identified from gait lab records and matched with children with CP having a history of PBI at ≤ 1 year old.

Intratumoral STING agonist reverses immune evasion in PD-(L)1-refractory Merkel cell carcinoma: mechanistic insights from detailed biomarker analyses.

Background: Antibodies blocking programmed death (PD)-1 or its ligand (PD-L1) have revolutionized cancer care, but many patients do not experience durable benefits. Novel treatments to stimulate antitumor immunity are needed in the PD-(L)1 refractory setting. The stimulator of interferon genes (STING) protein, an innate sensor of cytoplasmic DNA, is a promising target with several agonists in development.

Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization.

Background And Purpose: Treatment planning is a time-intensive task that could be automated. We aimed to develop a "single-click" workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM).

Materials And Methods: Automatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially.