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The parental care-seeking behavior of children with osteogenesis imperfecta based on the Anderson's model: a qualitative study.
BMC Nurs
January 2025
Department of Orthopedic, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
Objective: This study aims to analyze the medical-seeking behavior of Osteogenesis Imperfecta(OI) children in Southwest China, summarize and analyze the issues in their medical process, and propose corresponding improvement strategies.
Methods: A phenomenological study involving semi-structured interviews with 20 OI caregivers at a tertiary centre for children from March to August 2021 was analyzed thematically, following Anderson's model.
Results: We identified eight themes in the data: 1)Regional disparities of OI management, 2)Big economic burden, 3)High-risk population, 4)Lack of health education, 5)Multiple treatments,6)Strict treatment indications,7)Disappointing therapeutic outcomes,8)Effective or ineffective treatment results.
CT-sensitized nanoprobe for effective early diagnosis and treatment of pulmonary fibrosis.
J Nanobiotechnology
January 2025
Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology; Basic Medicine Research and Innovation Center of Ministry of Education, Medical School of Southeast University, 87 Dingjiaqiao, Nanjing, 210009, China.
Early diagnosis is critical for providing a timely window for effective therapy in pulmonary fibrosis (PF); however, achieving this remains a significant challenge. The distinct honeycombing patterns observed in computed tomography (CT) for the primary diagnosis of PF are typically only visible in patients with moderate to severe disease, often leading to missed opportunities for early intervention. In this study, we developed a nanoprobe designed to accumulate at fibroblastic foci and loaded with the CT sensitizer iodide to enable effective early diagnosis of PF.
View Article and Find Full Text PDFCardiometabolic index as a predictor of gallstone incidence in U.S. adults: insights from NHANES 2017-2020.
BMC Gastroenterol
January 2025
Department of Nephrology, QingPu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, People's Republic of China.
Background: Gallstone disease (GSD) is associated with obesity. The Cardiometabolic Index (CMI), a metric that accurately assesses central adiposity and visceral fat, has not been extensively studied in relation to GSD risk. This study investigates the link between CMI and GSD incidence in U.
View Article and Find Full Text PDFCannabis consumption and risk of asthma: a systematic review and meta-analysis.
BMC Pulm Med
January 2025
Global Health and Infectious Diseases Control Institute, Nasarawa State University, Keffi, Nigeria.
Background: Cannabis is the third most widely used psychoactive substance globally, and its consumption has been increasing, particularly with the growing trend of legalization for medicinal and recreational use. Recent studies have raised concerns about the potential impact of cannabis on respiratory health, specifically the risk of asthma, a significant public health concern. This systematic review aimed to consolidate research on the association between cannabis use and the risk of asthma.
View Article and Find Full Text PDFAI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
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