AIMC Topic: Cholestasis

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Development and validation of a screening model for early diagnosis of biliary atresia in neonates with cholestasis.

Pediatric surgery international
BACKGROUND: Biliary atresia (BA) is a progressive neonatal cholestatic liver disease that requires timely diagnosis and intervention. Differentiating BA from other causes of neonatal cholestasis remains a significant clinical challenge.

Plasma proteome correlations with liver stiffness in pediatric cholestasis implicate epithelial to mesenchymal transition.

Hepatology communications
BACKGROUND: Pediatric cholestatic liver diseases can be characterized by rapidly progressive fibrosis. A multicenter cross-sectional analysis of vibration-controlled elastography in biliary atresia (BA), alpha-1 antitrypsin deficiency (A1AT), and Ala...

Machine learning-based prediction model for post-ERCP cholangitis in patients with malignant biliary obstruction: a retrospective multicenter study.

Surgical endoscopy
BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is the preferred palliative treatment for patients with unresectable malignant biliary obstruction (MBO), which can relieve biliary obstruction and prolong survival. Post-ERCP cholangi...

In vitro test battery for testing molecular initiating events in chemical-induced cholestasis.

Toxicology
Cholestatic liver injury is a complex adversity leading to the toxic accumulation of.noxious bile salts in the liver and systemic circulation. Cholestasis can be instigated by a plethora of chemicals originating from several applicability domains. Cu...

Leveraging machine learning for precision medicine: a predictive model for cognitive impairment in cholestasis patients.

BMC gastroenterology
BACKGROUND: Cholestasis, characterized by impaired bile flow, impacts cognitive function through systemic mechanisms, including inflammation and metabolic dysregulation. Despite its significance, targeted predictive models for cognitive impairment in...

Artificial intelligence for automatic diagnosis and pleomorphic morphological characterization of malignant biliary strictures using digital cholangioscopy.

Scientific reports
Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangioscopy (D-SOC) holds promise for improving diagnostic accuracy in indeterminate BS. This multicenter st...

Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study.

Computers in biology and medicine
Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-bi...

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not bee...

Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers.

Scientific reports
Diagnosing distal bile duct obstruction remains challenging. This study aimed to examine the diagnostic ability of artificial intelligence (AI) based on clinical biomarkers in diagnosing malignant distal bile duct obstruction. A total of 206 patients...