Elsevier,
Australian Critical Care, Volume 37, July 2024
Although Indigenous Australians critically ill with sepsis have similar short and long-term mortality rates, they present to hospital, die in-hospital, and die post-discharge significantly younger. Unique cohort characteristics may explain these outcomes, and assist clinicians, researchers and policy-makers in targeting interventions to these characteristics to best reduce the burden of sepsis in this cohort and improve their healthcare outcomes.
Elsevier,
Australian and New Zealand Journal of Public Health, Volume 48, June 2024
Māori and Pacific people want to take charge of their heart health but face challenges. Participants described important obligations to family, community and tikanga (the culturally correct way of doing things). Participants described times when health care undermined existing responsibilities, their dignity and/or their mana, and they felt excluded from treatment as a result.
Elsevier,
Journal of Anthropological Archaeology, Volume 74, June 2024
This archaeological anthropology paper discusses women's profound contributions to Arctic society. It looks at how needles are, and were, important women’s tools in the Iamal region of Siberia. How women’s sewing skills are crucial for the well-being of Arctic. families. How they form part of the women's cultural identities.
Elsevier,
Journal of Nutrition, Volume 154, June 2024
The purpose of this scoping review was to determine the extent to which accessibility and acceptability of federal food assistance programs in the United States have been evaluated among indigenous peoples and to summarize what is currently known. Overall, it was found that little attention has been paid to the accessibility and acceptability of federal food assistance programs among indigenous peoples and more research is needed to understand and improve the participation experiences and health trajectories of these priority populations.
Journal of Pharmaceutical Analysis, 2024, 101022, ISSN 2095-1779
This study developed four deep learning models to identify potential Alzheimer's disease treatments from traditional Chinese medicine, specifically the Kaixinsan formula. The models successfully predicted compounds that showed significant anti-Alzheimer's activities in various experimental validations.
