Drug-related macular edema: a real-world FDA Adverse Event Reporting System database study.

Purpose: This study aims to assess the risks associated with drug-induced macular edema and to examine the epidemiological characteristics of this condition.

Methods: This study analyzed data from the U.S.

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.

The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification.

Clinical and prognostic insights into Chlamydia trachomatis in pediatric acute respiratory infections: evidence from targeted next-generation sequencing of 5,021 cases.

Background: Chlamydia trachomatis (CT) is a major cause of respiratory tract infections in children. The primary objective of this research was to evaluate the infection status and clinical manifestations associated with C. trachomatis in these pediatric patients.

Magnetic beads-based double-stranded DNA fluorescent aptasensor biosensor for deoxynivalenol detection.

Deoxynivalenol (DON) is one of the most harmful mycotoxins that poses great health threats to human and animals. Herein, a simple and sensitive magnetic beads-based fluorescent biosensor was successfully prepared for detection of DON in cereals. A stable double-stranded DNA (dsDNA, biotin-sDNA+FAM-cDNA/AP) was formed on the surface of streptavidin-coated magnetic beads (SMBs).