Anal Cell Pathol (Amst)
January 2025
Trauma and burns are leading causes of death and significant global health concerns. RNA-binding proteins (RBPs) play a crucial role in post-transcriptional gene regulation, influencing various biological processes of cellular RNAs. This study aims to review the emerging trends and key areas of research on RBPs in the context of trauma and burns.
View Article and Find Full Text PDFAcute rejection (AR) is a significant complication in liver transplantation, impacting graft function and patient survival. Kupffer cells (KCs), liver-specific macrophages, can polarize into pro-inflammatory M1 or anti-inflammatory M2 phenotypes, both of which critically influence AR outcomes. Angiopoietin-like 4 (ANGPTL4), a secretory protein, is recognized for its function in regulating inflammation and macrophage polarization.
View Article and Find Full Text PDFAim: To explore the potential roles of mitochondrial dysfunction in the initiation of inflammation in periodontal macrophages and to determine the mechanism underlying the involvement of dynamin-related protein 1 (Drp1) in macrophage inflammatory responses through its interaction with hexokinase 1 (HK1).
Materials And Methods: Gingival tissues were collected from patients diagnosed with periodontitis or from healthy volunteers. Drp1 tetramer formation and phosphorylation were analysed using western blot.
Purpose: This study aimed to evaluate the diagnostic efficacy of time-dependent diffusion magnetic resonance imaging (td-dMRI) and dynamic contrast-enhanced MRI (DCE-MRI)-based kinetic heterogeneity in differentiating suspicious breast lesions (categorised as Breast Imaging Reporting and Data System 4 or 5).
Methods: This prospective study included 51 females with suspicious breast lesions who underwent preoperative breast MRI, including DCE-MRI and td-dMRI. Six kinetic parameters, namely peak, persistent, plateau, washout component, predominant curve type, and heterogeneity, were extracted from the DCE series using MATLAB and SPM software.
Background: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored.
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