Healthcare 4.0 demands healthcare data to be shaped into a common standardized and interoperable format for achieving more efficient data exchange. What is also needed is for this healthcare data to be both easily stored and securely accessed from anywhere, and vice versa. Currently, this is achieved through the secure storage of the healthcare data in different cloud repositories and infrastructures, which however increase the difficulty of accessing it in emergency situations from healthcare practitioners, or even from the citizens' themselves. The latter need to have specific credentials for accessing healthcare data in private cloud repositories, which can be almost impossible in urgent situations where this data must be accessed no matter what. For that reason, in this paper we are proposing a new health record indexing methodology that facilitates the access of non-privileged users (e.g. healthcare practitioners), to the healthcare data stored in cloud repositories of citizens-in-need, under the circumstances of emergency cases.
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http://dx.doi.org/10.3233/SHTI200534 | DOI Listing |
Clin J Gastroenterol
January 2025
University of Connecticut, Connecticut, USA.
Marginal ulcers are a common complication following Roux-en-Y bypass surgeries with an approximate incidence of 4.6%. The pathophysiology is complex and risk factors include smoking, nonsteroidal anti-inflammatory drugs (NSAIDs) use, Helicobacter pylori infection, and a larger pouch size.
View Article and Find Full Text PDFCancer Causes Control
January 2025
Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, State University of New York at Buffalo, 265 Farber Hall, Buffalo, NY, 14214, USA.
Purpose: Historical redlining, a 1930s-era form of residential segregation and proxy of structural racism, has been associated with breast cancer risk, stage, and survival, but research is lacking on how known present-day breast cancer risk factors are related to historical redlining. We aimed to describe the clustering of present-day neighborhood-level breast cancer risk factors with historical redlining and evaluate geographic patterning across the US.
Methods: This ecologic study included US neighborhoods (census tracts) with Home Owners' Loan Corporation (HOLC) grades, defined as having a score in the Historic Redlining Score dataset; 2019 Population Level Analysis and Community EStimates (PLACES) data; and 2014-2016 Environmental Justice Index (EJI) data.
Cell Death Differ
January 2025
Division of Hepatobiliary and Transplantation Surgery, Department of General Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
The importance of SUMOylation in tumorigenesis has received increasing attention, and research on therapeutic agents targeting this pathway has progressed. However, the potential function of SUMOylation during hepatocellular carcinoma (HCC) progression and the underlying molecular mechanisms remain unclear. Here, we identified that SUMO-Specific Peptidase 3 (SENP3) was upregulated in HCC tissues and correlated with a poor prognosis.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Genetic Epidemiology Group, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Experiencing a traumatic event may lead to Posttraumatic Stress Disorder (PTSD), including symptoms such as flashbacks and hyperarousal. Individuals suffering from PTSD are at increased risk of cardiovascular disease (CVD), but it is unclear why. This study assesses shared genetic liability and potential causal pathways between PTSD and CVD.
View Article and Find Full Text PDFBioData Min
January 2025
School of Computer Science, Fudan University, Shanghai, China.
This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions.
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