Publications by authors named "J D Rodgers"
AbstractUnderstanding the relationship between the environment parents experience during reproduction and the environment embryos experience in the nest is essential for determining the intergenerational responses of populations to novel environmental conditions. Thermal stress has become commonplace for organisms inhabiting areas affected by rising temperatures. Exposure to body temperatures that approach, but do not exceed, upper thermal limits often induces adverse effects in organisms, but the propensity for these temperatures to have intergenerational consequences has not been explored in depth.
View Article and Find Full Text PDFAim: To understand, from a nursing perspective, factors affecting the use of prophylactic dressings to prevent pressure injuries in acute hospitalised adults.
Background: Pressure injury causes harm to patients and incurs significant costs to health services. Significant emphasis is placed on their prevention.
Inter-cohort shifts in chronic disease, dementia, and mortality.
Biodemography Soc Biol
November 2024
Article Synopsis
- - This study analyzes generational shifts in disease incidence and mortality among older adults in England, similar to previous findings in the U.S., using data from the English Longitudinal Study of Ageing (ELSA).
- - Researchers found that diseases like memory complaints, heart conditions, and cancer have higher incidence rates in later-born cohorts, paralleling trends observed in the U.S., but with more negative outcomes in England.
- - While some diseases showed no significant difference between men and women, when differences were present, women generally exhibited lower risks. The findings suggest a potential increase in disease burden for future generations.
Purpose: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.
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