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Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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In patients with renal impairment, drugs undergo significant changes in their pharmacokinetics, which require dosage adjustments to ensure safe and effective therapy.
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Related Experiment Video

Updated: Mar 19, 2026

5/6th Nephrectomy in Combination with High Salt Diet and Nitric Oxide Synthase Inhibition to Induce Chronic Kidney Disease in the Lewis Rat
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Protective Effects of Riociguat Against Contrast-Induced Nephropathy: An Experimental and Machine Learning-Based

Mustafa Begenc Tascanov1, Kenan Toprak2, Sibel Turedi3

  • 1Department of Cardiology, Samsun University, Faculty of Medicine, Samsun, Turkiye.

The Kaohsiung Journal of Medical Sciences
|March 18, 2026
PubMed
Summary

Riociguat effectively protected against contrast-induced nephropathy (CIN) in rats by reducing kidney damage and inflammation. Machine learning identified key predictors but lacked immediate clinical application for predicting renal injury.

Keywords:
acute kidney injurycontrast‐induced nephropathymachine learningnitric oxideriociguat

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Area of Science:

  • Nephrology
  • Pharmacology
  • Biomedical Engineering

Background:

  • Contrast-induced nephropathy (CIN) is a significant cause of acute kidney injury.
  • Effective preventive strategies for CIN are limited.
  • Soluble guanylate cyclase stimulators represent a potential therapeutic avenue.

Purpose of the Study:

  • To investigate the renoprotective effects of riociguat in a rat model of CIN.
  • To explore machine learning for predicting renal injury using various biomarkers.
  • To elucidate the mechanisms underlying riociguat's potential renoprotective action.

Main Methods:

  • Experimental rat model of CIN induced by iohexol after dehydration.
  • Oral administration of riociguat for five days.
  • Assessment of renal injury via histopathology, TUNEL assay, and biochemical markers (creatinine, urea, TNF-α, NO, NGAL, AOPP).
  • Application of machine learning models (Random Forest, CatBoost, AdaBoost, XGBoost) for prediction and feature importance analysis.

Main Results:

  • Riociguat significantly reduced serum creatinine, urea, apoptotic index, and histopathological injury scores in CIN rats.
  • Riociguat decreased inflammatory and oxidative stress markers while increasing nitric oxide levels.
  • Machine learning identified apoptotic index and nitric oxide as key predictors, though requiring histological assessment.

Conclusions:

  • Riociguat exhibits significant renoprotective effects in CIN, mediated by anti-apoptotic, anti-inflammatory, and antioxidative mechanisms.
  • Machine learning provided valuable insights into potential predictive markers but did not yield a clinically deployable predictive model.
  • Further research is warranted to translate these findings into clinical practice for CIN prevention.