Coronary artery disease (CAD), which is now regarded as a chronic inflammatory disease, is the leading cause of death worldwide. Nuclear factor (NF)-κB is a transcription factor that plays an important role in the regulation of the immune system. NF-κBIA is the inhibitory version of NF-κB. This study is the first investigation of the association between CAD and NF-κBIA-297 C/T, -826 C/T, -881 A/G polymorphisms in a Turkish population using PCR-RFLP method. The study population comprised 201 cases with CAD and 201 healthy controls. There was no significant difference in NF-κB1A-297 C/T and -881 A/G in allele and genotype frequencies between case and control populations. The genotype frequency of NF-κBIA-826TT in the patients with CAD was significantly higher than that of the controls (p = 0.015, adjusted OR = 7.09, 95% CI = 1.95-25.70). The patients with CAD also had significantly higher carriage rate of NF-κBIA-826T allele than the controls (p = 0.03, OR = 1.43, 95% CI = 1.03-1.99). Linkage analysis indicated a close linkage among these three variants of NF-κBIA (for case, χ(2 ) = 85.35 and p < 0.001; for control, χ(2 ) = 21.58 p < 0.001) and TTG, TTA and TCG haplotypes were associated with CAD (adjusted OR = 2.54, 95% CI = 0.88-7.27; p = 0.001, adjusted OR = 1.61, 95% CI: 0.64-4.02; p = 0.04, adjusted OR = 0.08, 95% CI = 0.01-0.64; p < 0.001, respectively). NF-κBIA-826TT genotype may be a significant risk factor and a valuable marker for the development of CAD.
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http://dx.doi.org/10.1111/bcpt.12085 | DOI Listing |
J Mater Sci Mater Med
January 2025
Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, PR China.
In-stent restenosis (ISR) following interventional therapy is a fatal clinical complication. Current evidence indicates that neointimal hyperplasia driven by uncontrolled proliferation of vascular smooth muscle cells (VSMC) is a major cause of restenosis. This implies that inhibiting VSMC proliferation may be an attractive approach for preventing in-stent restenosis.
View Article and Find Full Text PDFPharmacoepidemiol Drug Saf
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Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.
Purpose: Increases in adult stimulant prescribing pose a potential risk due to the higher prevalence of contraindicated conditions among this population. We sought to identify patient, provider, and visit characteristics predictive of potentially inappropriate adult stimulant prescriptions.
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Eur J Cardiothorac Surg
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Coronary Center, Department of Thoracic and Cardiovascular Surgery, Miller Family Heart, Vascular, & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Open Heart
January 2025
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).
Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1.
Open Heart
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Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
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