N-formyl peptide receptors (FPRs) are critical regulators of host defense in phagocytes and are also expressed in epithelia. FPR signaling and function have been extensively studied in phagocytes, yet their functional biology in epithelia is poorly understood. We describe a novel intestinal epithelial FPR signaling pathway that is activated by an endogenous FPR ligand, annexin A1 (ANXA1), and its cleavage product Ac2-26, which mediate activation of ROS by an epithelial NADPH oxidase, NOX1. We show that epithelial cell migration was regulated by this signaling cascade through oxidative inactivation of the regulatory phosphatases PTEN and PTP-PEST, with consequent activation of focal adhesion kinase (FAK) and paxillin. In vivo studies using intestinal epithelial specific Nox1(-/-IEC) and AnxA1(-/-) mice demonstrated defects in intestinal mucosal wound repair, while systemic administration of ANXA1 promoted wound recovery in a NOX1-dependent fashion. Additionally, increased ANXA1 expression was observed in the intestinal epithelium and infiltrating leukocytes in the mucosa of ulcerative colitis patients compared with normal intestinal mucosa. Our findings delineate a novel epithelial FPR1/NOX1-dependent redox signaling pathway that promotes mucosal wound repair.
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http://dx.doi.org/10.1172/JCI65831 | DOI Listing |
Chemo-resistance in ovarian cancer is currently a major obstacle to the treatment and recovery of ovarian cancer. Therefore, identifying factors associated with chemo-resistance in ovarian cancer may reverse chemo-sensitization. Using isobaric tags for relative and absolute quantitation (ITRAQ) technology, we found a small molecule peptide with annexin 1 (ANXA1) as a precursor protein.
View Article and Find Full Text PDFJ Neural Eng
December 2024
School of Life Sciences, Tiangong University, NO.399, Binshuixi Road, Xiqing District, Tianjin, P.R.China., Tianjin, Tianjin, 300387, CHINA.
Objective: Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.
Approach: We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain.
J Imaging Inform Med
December 2024
Department of Computer Science and Engineering, Sri Sairam Engineering College, Anna University, Chennai, India.
Heart disease is a fatal disease that causes significant mortality rates worldwide. The accurate and early detection of heart diseases is the most challenging task to save valuable lives. To avoid these issues, the Deep Convolutional Generative Adversarial Network (DCGAN) model is proposed that generates synthetic cardiac images.
View Article and Find Full Text PDFJ Neural Eng
November 2024
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures.
View Article and Find Full Text PDFSci Rep
October 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Ukraine.
Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification.
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