Researchers explored how machine learning and quantum computing can be used to improve early detection of chronic kidney disease, aiming to develop faster, more accurate diagnostic tools for clinicians.

Advances in molecular diagnostics have driven multiplex biomarker detection as a critical approach for enhanced diagnostic accuracy. The simultaneous quantification of carcinoembryonic antigen (CEA) and microRNA-21 (miR-21) holds particular clinical value in tumor diagnosis, prognosis assessment, and therapeutic monitoring. Peptide self-assembly technology has emerged as a promising biosensing platform, leveraging its unique molecular recognition capabilities and intrinsic signal amplification properties. Compared to conventional nanomaterials, peptide-engineered structures demonstrate superior biocompatibility, precise controllability, and spontaneous self-assembly into functional nanostructures under mild conditions. By designing dual-functional peptides that merge target recognition with signal amplification, researchers developed an electrochemical biosensor based on peptide self-assembly engineering signal amplification (PSA-e-SA). This innovation achieves ultrasensitive simultaneous detection of CEA and miR-21, addressing the critical need for early cancer diagnosis when biomarker concentrations are extremely low.

With the growing demand for more efficient and sustainable chemical processes, single-atom catalysts (SACs) have become a research hotspot due to their high atomic utilization and unique catalytic performance. As a core characterization tool, XAFS (X-ray absorption fine structure) technology can deeply study the microscopic chemical environment of SACs, providing key data for catalyst design. This article is based on a review published in Nano Research, exploring the progress, challenges, and future prospects of XAFS in SACs research, aiming to provide readers with comprehensive scientific insights.