Background: Completing pre-transplant evaluations may be a greater barrier to renal transplantation for blacks with end-stage renal disease (ESRD) than for whites.
Objective: To determine whether social support networks facilitate completing the pre-transplant evaluation and reduce racial disparities in this aspect of care.
Design, Setting, And Participants: We surveyed 742 black and white ESRD patients in four regional networks 9 months after they initiated dialysis in 1996 and 1997. Patients reported instrumental support networks (number of friends or family to help with daily activities), emotional support networks (number of friends or family available for counsel on personal problems) and dialysis center support (support from dialysis center staff and patients). The completion of pre-transplant evaluations, including preoperative risk stratification and testing, was determined by medical record reviews.
Outcome Measurement: Complete renal pre-transplant evaluations.
Results: Compared to patients with low levels of instrumental support, those with high levels were more likely to have complete evaluations (25% versus 46%, respectively, p < .001). In adjusted analyses, high levels of instrumental support were associated with higher rates of complete evaluations among black women (p < .05), white women (p < .05), and white men (p < .05), but not black men. Among black men, but not other groups, private insurance was a significant predictor of complete evaluations.
Conclusions: Instrumental support networks may facilitate completing renal pre-transplant evaluations. Clinical interventions that supplement instrumental support should be evaluated to improve access to renal transplantation. Access to supplemental insurance may also promote complete evaluations for black patients.
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http://dx.doi.org/10.1007/s11606-008-0628-7 | DOI Listing |
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
Life Sci Alliance
April 2025
National Cancer Institute, Center for Cancer Research, Laboratory of Receptor Biology and Gene Expression, Bethesda, MD, USA
Centromeres are marked by the centromere-specific histone H3 variant CENP-A/CENH3. Throughout the cell cycle, the constitutive centromere-associated network is bound to CENP-A chromatin, but how this protein network modifies CENP-A nucleosome conformations in vivo is unknown. Here, we purify endogenous centromeric chromatin associated with the CENP-C complex across the cell cycle and analyze the structures by single-molecule imaging and biochemical assays.
View Article and Find Full Text PDFEnviron Res
January 2025
School of Ecology and Environment, Ningxia University, Yinchuan 750021, China. Electronic address:
Salinization processes profoundly impact soil quality and health, altering physical structure, chemical composition, and biological activity, particularly concerning soil microbial populations. Microbial communities play a pivotal role in maintaining soil ecosystem multifunctionality (EMF). Understanding the response of microbial communities to salinity stress is crucial for sustainable soil management and enhancing ecosystem resilience in arid and semi-arid regions.
View Article and Find Full Text PDFInt J Radiat Oncol Biol Phys
January 2025
National Cancer Institute, Bethesda, MD. Electronic address:
This white paper examines the potential of pioneering technologies and artificial intelligence (AI)-driven solutions in advancing clinical trials involving radiotherapy. As the field of radiotherapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiotherapy, image-guided radiation therapy (IGRT), and AI promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect (LET/RBE), and the combination of radiotherapy and immunotherapy create new avenues for innovation in clinical trials.
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