In this study, we analyzed health-advertising tactics of digital medicine companies (n = 5) to evaluate varying types of cross-site-tracking middleware (n = 32) used to extract health information from users. More specifically, we examine how browsing data can be exchanged between digital medicine companies and Facebook for advertising and lead generation and advertising purposes. Our analysis focused on companies offering services to patient advocates in the cancer community who frequently engage on social media. We co-produced this study with public cancer advocates leading or participating in breast cancer groups on Facebook. Following our analysis, we raise policy questions about what constitutes a health privacy breach based on existing federal laws such as the Health Breach Notification Rule and The HIPAA Privacy Rule. We discuss how these common marketing practices enable surveillance and targeting of medical ads to vulnerable patient populations without consent.
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http://dx.doi.org/10.1016/j.patter.2022.100561 | DOI Listing |
Mol Neurodegener
January 2025
Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA.
Alzheimer's disease (AD) is a debilitating neurodegenerative disease that is marked by profound neurovascular dysfunction and significant cell-specific alterations in the brain vasculature. Recent advances in high throughput single-cell transcriptomics technology have enabled the study of the human brain vasculature at an unprecedented depth. Additionally, the understudied niche of cerebrovascular cells, such as endothelial and mural cells, and their subtypes have been scrutinized for understanding cellular and transcriptional heterogeneity in AD.
View Article and Find Full Text PDFJ Prosthodont
January 2025
The Office of Assistant Dean for Research, School of Dental Medicine, Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico.
Purpose: This study aimed to evaluate and compare the fracture resistance of long-span fixed provisional restorations fabricated using milling, three-dimensional (3D) printing, and conventional methods.
Materials And Methods: Sixty specimens were prepared, divided into four groups of 15 each, corresponding to four fabrication methods: computer-aided design and computer-aided manufacturing (CAD-CAM) milled provisional resins, 3D-printed provisional resins, 3D-printed permanent resins, and conventional bis-acryl restorations reinforced with wire. The specimens underwent a three-point bending test using a universal testing machine to measure fracture resistance, quantified as maximum force (in Newtons).
BMC Nurs
January 2025
College of Medicine and Health Sciences, School of Nursing and Midwifery, University of Rwanda, Po. Box: 3286, Kigali, Rwanda.
Background: Pressure injuries are costly and can lead to mortality and psychosocial consequences if not managed effectively. Proper management of pressure injuries is crucial for quality nursing care. However, there is limited research on nurses' knowledge and practices in preventing and managing pressure injuries among critically ill patients in Rwanda.
View Article and Find Full Text PDFBMC Med
January 2025
Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria.
Background: Patients at need for ventilation often are at risk of acute respiratory distress syndrome (ARDS). Although lung-protective ventilation strategies, including low driving pressure settings, are well known to improve outcomes, clinical practice often diverges from these strategies. A clinical decision support (CDS) system can improve adherence to current guidelines; moreover, the potential of a CDS to enhance adherence can possibly be further increased by combination with a nudge type intervention.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts.
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