MODERN TECHNOLOGY FOR DIAGNOSING CHRONIC GLOMERULONEPHRITIS

Аннотация

This article explores the modern advancements in diagnosing chronic glomerulonephritis (CGN), a progressive kidney disease that can lead to end-stage renal disease (ESRD). It highlights the importance of early and accurate diagnosis for effective disease management. Traditional diagnostic methods, such as urinalysis, serum creatinine levels, and renal biopsy, are still widely used; however, recent technological advancements have greatly improved the accuracy and non-invasiveness of CGN diagnostics. The article focuses on various biomarkers (such as Neutrophil Gelatinase-Associated Lipocalin, Kidney Injury Molecule-1, and transforming growth factor-beta), proteomics, genomics, and advanced imaging techniques, including multiparametric MRI, ultrasound elastography, and PET-CT. Additionally, it examines the role of artificial intelligence (AI) and machine learning in automating diagnosis and predicting disease progression. These innovations allow for early detection, personalized treatment, and better monitoring of CGN. Ultimately, the integration of these technologies aims to improve patient outcomes by reducing the need forinvasivediagnostic procedures. Keywords: Chronic glomerulonephritis (CGN), end-stage renal disease (ESRD), diagnostic methods, renal biopsy, biomarkers, artificial intelligence (AI), NGAL, KIM-1, TGF-β, microRNAs (miRNAs), proteomics, genomics, multiparametric MRI, DWI, magnetization TransferMRI, ultrasound elastography, PET-CT, fibrosis tracers, AI, machine learning. Introduction: Chronic glomerulonephritis (CGN) is a progressive kidney disease characterized by persistent inflammation and damage to the glomeruli, leading to renal dysfunction and, ultimately, end-stage renal disease (ESRD). Early and accurate diagnosis is crucial for effective management and treatment. Traditional diagnostic methods, such as urinalysis, serum creatinine levels, and renal biopsy, remain valuable. However, modern technology has significantly improved the accuracy, efficiency, and non-invasiveness of diagnosing CGN. This article explores the latest advancements in diagnostic technology for CGN, including biomarkers, imaging techniques, and artificial intelligence (AI)-driven approaches. Biomarkers and MolecularDiagnostics. 1. Urinary and Blood Biomarkers. Recent advances in molecular diagnostics have identified various biomarkers that improve the detection and monitoring of CGN. These include: Neutrophil Gelatinase-Associated Lipocalin (NGAL): Elevated NGAL levels in urine and blood indicate kidney injury, allowing early detection of CGN (Mishra et al., 2021). Kidney Injury Molecule-1 (KIM-1): A marker for tubular damage, helping differentiate CGN from other kidney disorders (Vaidya et al., 2020). Transforming Growth Factor-beta (TGF-β): A key regulator of fibrosis, TGF-β levels correlate with the severity of CGN progression (Higgins et al., 2019). MicroRNAs (miRNAs): Specific miRNA profiles in 98,7:ROTCAFTCAPMI.SECNEICSLACIDEM ,5202 ,lirpA,5emuloV gro.srehsilbupcimedaca.www urine and blood provide insights into glomerular inflammation and fibrosis (Chen et al., 2022). These biomarkers enhance early diagnosis, predict disease progression, and guide personalized treatment strategies. 2. Proteomics and Genomics. Advances in proteomics and genomics have revolutionized CGN diagnostics. Proteomics: Mass spectrometry-based techniques analyze urine and serum protein patterns to identify disease-specific signatures (Good et al., 2020). Genomic Sequencing: Next-generation sequencing (NGS)helps detect genetic predispositionstoCGN, improvingearly risk assessment (Gharavi et al., 2021). Advanced Imaging Techniques: 1. Multiparametric Magnetic Resonance Imaging (MRI). MRI is now used beyond anatomical assessment, incorporating functional techniques such as Diffusion-Weighted Imaging (DWI): Evaluates kidney tissue integrity and detects early fibrosis (Erdbrügger et al., 2019). Blood Oxygen Level-Dependent (BOLD) MRI: Measures renal oxygenation, identifying hypoxia-related glomerular damage (Pruijm et al., 2020). Magnetization Transfer MRI: Quantifies collagen deposition, aiding in the assessment of kidney fibrosis (Berchtold et al., 2021). These advanced MRI techniques provideanon-invasive alternativeto biopsyfor evaluating diseaseprogression. 2. Ultrasound Elastography. Elastography, a specialized ultrasound technique, assesses renal stiffness, reflecting fibrosis severity in CGN. Techniques include: Shear Wave Elastography (SWE): Which measures tissue stiffness in real time, offering an indirect but reliable assessment of glomerular scarring (Grenier et al., 2021). Acoustic Radiation Force Impulse (ARFI) Imaging: Differentiates between normal and fibrotic kidney tissue (Sethna et al.,2020). 3. Positron Emission Tomography-Computed Tomography (PET-CT). PET-CT, combined with novel radiotracers, enhances functional imaging of kidney inflammation and fibrosis. 18F-FDG PET: Detects active inflammation in CGN, distinguishing i