Hyperbilirubinemia refers to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment produced during the normal breakdown of red blood cells. It is processed by the liver and excreted in bile. When bilirubin levels rise above normal, it can cause jaundice, a condition characterized by yellowing of the skin, eyes, and mucous membranes. Hyperbilirubinemia is common in newborns due to immature liver function and is usually transient. However, untreated severe cases can lead to complications like kernicterus (brain damage).there are three types of Hyperbilirubinemia: Physological jaundice due to hemolysis of blood cell, pathological jaundice due to pathological disorders such as rubella, and bacterial septicemia, Breast Milk Jaundice resulting from a combination of genetic and environmental factors such as the components of breast milk , Hemolytic Jaundice due to many reasons : Rhesus hemolytic disease, Blood group incompatibility, Glucose-6 phosphate dehydrogenase deficiency and minor blood group incompatibility.

Background: Magnetic resonance imaging (MRI) is the most sensitive technique for the detection of breast cancer in contemporary medical practice. Contrast-enhanced mammography (CEM), a novel technology, provides a contrast-enhanced imaging alternative to breast MRI. Objectives: to assess the diagnostic efficacy of CEM in the characterization of suspicious breast lesions, with histopathological results serving as the gold standard. Compare CEM with dynamic contrast enhanced MRI (DCE-MRI). Patients and Methods: 30 individuals with suspicious lesions identified using conventional mammography or ultrasonography underwent CEM and DCE-MRI examinations after receiving ethical approval. Proficient radiologists evaluated all discernible lesions using the Breast Imaging Reporting and Data System (BI-RADS) classifications (categories 1–6). Histopathological data were compared to the morphological descriptions of each lesion that were received from each modality. Results: 30 lesions were identified by the combination of breast MRI and CEM in the 30 patients who were enrolled in CEM/MRI investigations. Histopathology verified that 15 of the 30 lesions were malignant and 15 were benign. Twelve of the fifteen malignant lesions were invasive cancers, while three were in situ cancers. Both breast MRI and CEM demonstrated a sensitivity of 93.33%. The specificity of breast MRI was 93.33%, while CEM had a specificity of 80.00%. The breast MRI achieved an accuracy of 93.33%, while the CEM achieved an accuracy of 86.67%. Conclusions: Contrast-enhanced mammography (CEM) has the potential to replace dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a problem-solving instrument for the characterization of indeterminate breast lesions, particularly in situations where MRI is contraindicated or not promptly accessible. CEM provides a viable alternative for evaluating ambiguous lesions, as it utilizes similar principles of contrast enhancement and tumor vascularity. However, it has the additional benefit of being more broadly available, less expensive, and better tolerated by specific patient populations.

Machine learning has emerged as a transformative tool in healthcare, enabling predictive analytics for disease progression, patient management, and clinical decision-making. This study integrates three critical areas: mortality trends in the USA, heart failure survival prediction using machine learning (ML) models, and hospital readmission forecasting with artificial intelligence (AI)-driven methodologies. Using datasets from national health statistics, clinical trial data, and electronic health records, this research applies Logistic Regression, Random Forest, Support Vector Machines (SVM), Neural Networks, and Gradient Boosting models to enhance prediction accuracy. Results indicate that SVM achieves the highest predictive accuracy for heart failure survival (88.41%), while Gradient Boosting performs best for readmission prediction. Findings highlight ML’s potential in improving risk stratification, resource allocation, and targeted interventions, contributing to a growing body of AI applications in healthcare analytics. This study provides a foundation for future research on personalized medicine and predictive healthcare models, with broader implications for disease prevention and healthcare efficiency.

Psoriasis is a chronic inflammatory skin disease with a complex and multifactorial pathophysiology. Among the triggering factors, certain medications have been implicated as causing the appearance of new cases or exacerbations in previously diagnosed patients. This article reviews the latest evidence on medications associated with induced psoriasis, paradoxical eruptions, the pathophysiological mechanisms involved, and clinical implications. Strategies for its management and prevention are also discussed, highlighting the importance of pharmacovigilance in these patients.