Purpose: Primary open-angle glaucoma (POAG) is a complex inherited disorder. It has been demonstrated in other complex disorders that phenotypic heterogeneity may be the result of genetic heterogeneity and that stratification analysis can be used to increase the power of detection. Ordered subset analysis (OSA) is a recently described method that utilizes the variability of phenotypic traits to determine underlying genetic heterogeneity.
Methods: Eighty-six multiplex families with POAG were clinically ascertained for genetic analysis. Age at diagnosis (AAD) was used as a surrogate for age of onset in affected family members. Nine genetic markers within the 15q11-13 interval on chromosome 15 were used for OSA analysis.
Results: An 11-cM linkage interval with a peak LOD score of 3.24 centered at the GABRB3 locus (P = 0.013 by permutation test) was identified in a subset of 15 families, which represents 17% of the total dataset (15/86 families). The mean AAD for the affected OSA families was 44.1 +/- 9.1 years (SD). The mean AAD for the complementary group was 61.3 +/- 10.4 years. African-American and white families were well represented in the OSA subset.
Conclusions: Linkage was identified for POAG to an 11-cM region on chromosome 15, designated GLC1I. This result provides further evidence that AAD and other phenotypic traits can be used as stratification variables to identify genes in complex disorders such as POAG and suggests that the 15q11-13 locus is one of the largest genetic contributors to POAG identified to date.
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http://dx.doi.org/10.1167/iovs.04-1477 | DOI Listing |
Data Brief
February 2025
Cell Death, Lysosomes and Artificial Intelligence Group, Department of Experimental Medical Science, Faculty of Medicine, Lund University, BMC D10, 22184 Lund, Sweden.
Many forms of bioimage analysis involve the detection of objects and their outlines. In the context of microscopy-based high-throughput drug and genomic screening and even in smaller scale microscopy experiments, the objects that most often need to be detected are cells. In order to develop and benchmark algorithms and neural networks that can perform this task, high-quality datasets with annotated cell outlines are needed.
View Article and Find Full Text PDFJ Surg Res
January 2025
Department of Surgery, Albany Medical Center, Albany, New York; Division of Vascular Surgery, Albany Medical Center, Albany, New York.
Introduction: Surgical site infection (SSI) after lower extremity (LE) bypass surgery is associated with longer length of stay, higher hospital cost, increased morbidity, and even graft loss. Silver impregnated dressings have been used by other surgical subspecialties to decrease SSI with reported success. The National Surgical Quality Improvement Program (NSQIP) published a national expected rate of 7.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Pathology, General Hospital of Ningxia Medical University, Yinchuan 750004, PR China (N.Z.). Electronic address:
Rationale And Objectives: To develop a machine learning (ML) model based on clinicopathological and imaging features to predict the Human Epidermal Growth Factor Receptor 2 (HER2) positive expression (HER2-p) of breast cancer (BC), and to compare its performance with that of a logistic regression (LR) model.
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Asian J Transfus Sci
September 2022
Department of Transfusion Medicine and Immunohaematology, St. John's Medical College, Bengaluru, Karnataka, India.
Background: Although ABO and RhD are the clinically significant blood group antigens that are routinely tested for, other blood group antigens may become important in multiply transfused patients due to risk of alloimmunization. Knowledge of antigen prevalence in a population is important in the context of alloimmunization and antigen matching. This study aims to do the same in a population of voluntary blood donors of a center in South India.
View Article and Find Full Text PDFHealth Policy Plan
January 2025
University of Cape Town, Health Systems and Policy Division and London School of Hygiene and Tropical Medicine, Falmouth Road, Observatory, Cape Town 7925, South Africa.
Understanding health systems as comprising interacting elements of hardware and software acknowledges health systems as complex adaptive systems (CAS). Hardware represents the concrete components of systems, whereas software represents the elements which influence actions and underpin relationships, such as processes, values and norms As a specific call for research on health system software was made in 2011, we conducted a qualitative scoping review considering how and for what purpose the concept has been used since then. Our overall purpose was to synthesise current knowledge and generate lessons about how to deepen research on, and understanding of, health system software.
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