The current study evaluated a range of social influences including misdemeanor arrests, drug arrests, cocaine consumption, alcohol consumption, firearm availability, and incarceration that may be associated with changes in gun-related homicides by racial/ethnic group in New York City (NYC) from 1990 to 1999. Using police precincts as the unit of analysis, we used cross-sectional, time series data to examine changes in Black, White, and Hispanic homicides, separately. Bayesian hierarchical models with a spatial error term indicated that an increase in cocaine consumption was associated with an increase in Black homicides. An increase in firearm availability was associated with an increase in Hispanic homicides. Last, there were no significant predictors for White homicides. Support was found for the crack cocaine hypotheses but not for the broken windows hypothesis. Examining racially/ethnically disaggregated data can shed light on group-sensitive mechanisms that may explain changes in homicide over time.
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http://dx.doi.org/10.1177/1088767911416917 | DOI Listing |
JMIR Hum Factors
January 2025
Hackensack Meridian School of Medicine, 123 Metro Blvd, Nutley, NJ, 07110, United States, 1 7248419463.
Background: Transgender and nonbinary (TGNB) individuals are increasingly intentionally becoming pregnant to raise children, and hospital websites should reflect these trends. For prospective TGNB parents, a hospital website is the only way they can assess their safety from discrimination while receiving perinatal care. Cisnormativity enforced by communication gaps between medical institutions and TGNB patients can and has caused delays in receiving urgent care during their pregnancy.
View Article and Find Full Text PDFCureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
View Article and Find Full Text PDFNeurooncol Adv
November 2024
Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, Saint Louis, Missouri, USA.
Background: Alterations in cellular metabolism affect cancer survival and can manifest in metrics of body composition. We investigated the effects of various body composition metrics on survival in patients with glioblastoma (GBM).
Methods: We retrospectively analyzed patients who had an abdominal and pelvic computed tomography (CT) scan performed within 1 month of diagnosis of GBM (178 participants, 102 males, 76 females, median age: 62.
Ann Thorac Surg Short Rep
December 2024
Department of Cardiac Surgery, San Raffaele University Hospital, Milano, Italy.
Purpose: To report the outcomes of the early feasibility study of transapical transcatheter mitral valve replacement (TMVR) with the SATURN System (InnovHeart, Milano, Italy) to treat patients with severe functional mitral regurgitation.
Description: Five high surgical risk patients underwent transapical transcatheter mitral valve replacement with the SATURN System at a single center. One-year follow-up is complete for all patients.
Br J Dermatol
January 2025
Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
Recessive dystrophic epidermolysis bullosa (RDEB) is a genetic disorder due to pathogenic variants in the COL7A1 gene. In this study we determined the association between different categories of COL7A1 variants and clinical disease severity in 236 RDEB patients in North America. Published reports or in-silico predictions were used to assess the impact of pathogenic variants in COL7A1 on type VII collagen (C7) protein function.
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