The Potential Influence of Advanced Glycation End Products and (s)RAGE in Rheumatic Diseases

Delrue, Charlotte; Speeckaert, Reinhart; Delanghe, Joris; Speeckaert, Marijn M.

doi:10.3390/ijms24032894

Cited by 7 publications

(2 citation statements)

References 128 publications

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“…Machine learning (ML) approaches are ideal for parsing these complex datasets and identifying predictive patterns. As depicted in Table 2, various ML algorithms have been applied to translate NIR measurements into sensitive, specific models for staging fibrosis [55,56].…”

Section: Machine Learning Approaches For Near-infrared Fibrosis Detec...mentioning

confidence: 99%

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

Addissouky,

El Sayed,

Ali

et al. 2024

Arch Gastroenterol Res

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Background: Liver fibrosis staging is critical for patient selection and management prior to transplantation, but biopsy is invasive and serum biomarkers lack accuracy. Near-infrared spectroscopy (NIRS) is an emerging non-invasive technology that can detect liver fibrosis via changes in tissue composition. Machine learning (ML) enables analysis of NIRS data for diagnostic modeling. Purpose: To review the potential of NIRS-ML approaches for rapid, point-of-care liver fibrosis detection, including technological principles, promising applications, current limitations, and future directions. Main body of the abstract: NIRS leverages unique near-infrared absorbance patterns reflecting collagen accumulation, lipid reduction, and other chemical alterations in fibrotic liver. Handheld/hyperspectral systems acquire tissue spectroscopic data in minutes. Multiple human studies correlate NIRS with histological fibrosis scores. ML techniques like partial least squares regression, neural networks, support vector machines, and random forests analyze spectra to develop optimized diagnostic algorithms. Initial models differentiate mild versus advanced fibrosis and stage cirrhosis with high accuracy, outperforming traditional biomarkers. Recent advances include smartphone-based scanning, cloud computing, and integrated user-friendly platforms. However, further large validation trials, standardization, assessment of confounding factors, improved ML methodology, and cost-effectiveness data are required before widespread clinical implementation. Conclusion: With ongoing research to address remaining barriers, NIRS-ML approaches hold great disruptive potential for rapid, non-invasive point-of-care quantification of liver fibrosis, including optimizing transplant surgery planning and management.

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Section: Machine Learning Approaches For Near-infrared Fibrosis Detec...mentioning

confidence: 99%

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

Addissouky,

El Sayed,

Ali

et al. 2024

Arch Gastroenterol Res

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“…Circulating Nε-carboxymethyllysine levels are increased in RA, and its levels are associated with severity of the disease [147]. Of note, however, is how decreased levels of sRAGE in both blood and synovial fluid have been reported in patients with RA [148,149].…”

Section: Autoimmune Diseases 751 Rheumatoid Arthritismentioning

confidence: 99%

The RAGE Axis: A Relevant Inflammatory Hub in Human Diseases

Rojas,

Lindner,

Schneider

et al. 2024

Biomolecules

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In 1992, a transcendental report suggested that the receptor of advanced glycation end-products (RAGE) functions as a cell surface receptor for a wide and diverse group of compounds, commonly referred to as advanced glycation end-products (AGEs), resulting from the non-enzymatic glycation of lipids and proteins in response to hyperglycemia. The interaction of these compounds with RAGE represents an essential element in triggering the cellular response to proteins or lipids that become glycated. Although initially demonstrated for diabetes complications, a growing body of evidence clearly supports RAGE’s role in human diseases. Moreover, the recognizing capacities of this receptor have been extended to a plethora of structurally diverse ligands. As a result, it has been acknowledged as a pattern recognition receptor (PRR) and functionally categorized as the RAGE axis. The ligation to RAGE leads the initiation of a complex signaling cascade and thus triggering crucial cellular events in the pathophysiology of many human diseases. In the present review, we intend to summarize basic features of the RAGE axis biology as well as its contribution to some relevant human diseases such as metabolic diseases, neurodegenerative, cardiovascular, autoimmune, and chronic airways diseases, and cancer as a result of exposure to AGEs, as well as many other ligands.

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Pathological role of RAGE underlying progression of various diseases: its potential as biomarker and therapeutic target

Sarkar

2024

Naunyn-Schmiedeberg's Arch Pharmacol

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The Potential Influence of Advanced Glycation End Products and (s)RAGE in Rheumatic Diseases

Cited by 7 publications

References 128 publications

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

The RAGE Axis: A Relevant Inflammatory Hub in Human Diseases

Pathological role of RAGE underlying progression of various diseases: its potential as biomarker and therapeutic target

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