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Introduction: Gouty nephropathy (GN) arises from factors like excessive purine intake, metabolic disorders or abnormal synthesis, and uric acid hypersaturation in the blood, leading to urate crystal deposition in kidney tissue. DaiTongXiao (DTX) is a remedy used by the Dai people of China. It shows efficacy in lowering uric acid levels and exhibits anti-inflammatory and kidney-protective properties.Methods: A GN rat model was induced using adenine and potassium oxonate. Following DTX administration, various parameters were assessed in urine, serum, and kidney tissue. Western blot analysis evaluated TLR4/MyD88/NF-κB signaling proteins, while immunofluorescence examined NF-κB nuclear expression.Results: DTX treatment improved kidney morphology, increased body weight, and kidney index and enhanced urinary levels of blood urea nitrogen (Bun), 24-h urinary protein, uric acid (UA), and allantoin in GN rats, reducing UA, Bun, creatinine (Cre), cystatin C (CysC), serum amyloid A (SAA), α1-microglobulin (MG), and β2-MG in serum analysis. Renal tissue assessments showed decreased xanthine oxidase (XOD), hydroxyproline (Hyp), α-smooth muscle actin (α-SMA), and collage type Ⅳ (COL-Ⅳ). Kidney damage severity was notably reduced. DTX lowered serum inflammatory factors like interleukin (IL) −18, tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), transforming growth factor-β1 (TGF-β1), and IL-1β in the rat serum, reducing chemokine monocyte chemoattractant protein-1 (MCP-1) and adhesion factor vascular cell adhesion molecule-1(VCAM-1). Western blotting demonstrated the downregulation of TLR4/MyD88/NF-κB pathway proteins, and immunofluorescence revealed reduced NF-κB expression in renal tissue.Discussion: DTX exhibits significant anti-GN effects by modulating TLR4/MyD88/ NF-κB pathway protein expression, reducing inflammatory factor release, and inhibiting GN progression.
Introduction: Gouty nephropathy (GN) arises from factors like excessive purine intake, metabolic disorders or abnormal synthesis, and uric acid hypersaturation in the blood, leading to urate crystal deposition in kidney tissue. DaiTongXiao (DTX) is a remedy used by the Dai people of China. It shows efficacy in lowering uric acid levels and exhibits anti-inflammatory and kidney-protective properties.Methods: A GN rat model was induced using adenine and potassium oxonate. Following DTX administration, various parameters were assessed in urine, serum, and kidney tissue. Western blot analysis evaluated TLR4/MyD88/NF-κB signaling proteins, while immunofluorescence examined NF-κB nuclear expression.Results: DTX treatment improved kidney morphology, increased body weight, and kidney index and enhanced urinary levels of blood urea nitrogen (Bun), 24-h urinary protein, uric acid (UA), and allantoin in GN rats, reducing UA, Bun, creatinine (Cre), cystatin C (CysC), serum amyloid A (SAA), α1-microglobulin (MG), and β2-MG in serum analysis. Renal tissue assessments showed decreased xanthine oxidase (XOD), hydroxyproline (Hyp), α-smooth muscle actin (α-SMA), and collage type Ⅳ (COL-Ⅳ). Kidney damage severity was notably reduced. DTX lowered serum inflammatory factors like interleukin (IL) −18, tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), transforming growth factor-β1 (TGF-β1), and IL-1β in the rat serum, reducing chemokine monocyte chemoattractant protein-1 (MCP-1) and adhesion factor vascular cell adhesion molecule-1(VCAM-1). Western blotting demonstrated the downregulation of TLR4/MyD88/NF-κB pathway proteins, and immunofluorescence revealed reduced NF-κB expression in renal tissue.Discussion: DTX exhibits significant anti-GN effects by modulating TLR4/MyD88/ NF-κB pathway protein expression, reducing inflammatory factor release, and inhibiting GN progression.
Background: Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. Methods: We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles. In total, 148 (34.02%) patients were with kidney stones, 34 (7.82%) with ureter stones, and 163 (34.83%) with multiple location stones, all of whom had detailed urinary metabolite data. Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. The Super Learner Ensemble Method also demonstrated high predictive performance with slightly lower AUC values compared to Random Forest. Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. Results: We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. Conclusion: Machine learning techniques show promise in revealing the links between urological stone disease and 24-h urinary metabolic data. Enhancing the prediction accuracy of these models leads to improved dietary or pharmacological prevention strategies.
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