Prognostic value of lymphocyte to monocyte ratio in patients with esophageal cancer: a systematic review and meta-analysis.

Objectives: To report the largest systematic review and meta-analysis to evaluate prognostic value of lymphocyte to monocyte ratio (LMR) in patients with esophageal cancer.

Methods: We conducted a systematic literature retrieval via PubMed, Embase, Web of Science, and Cochrane until December, 2023 for studies which evaluated the prognostic value of LMR in patients with esophageal cancer. Outcomes measured were overall survival (OS), disease-free survival (DFS), relapse-free survival (RFS), and progression-free survival (PFS).

Molecular Programming Design of Glyconucleic Acid Aptamer with High Stability.

Functional nucleic acids (FNAs), possessing specific biological functions beyond their informational roles, have gained widespread attention in disease therapeutics. However, their clinical application is severely limited by their low serum stability in complex physiological environments. In this work, a precise molecular programming strategy is explored to prepare glyconucleic acid aptamers (GNAAs) with high serum stability.

Recent Progress on Second Near-Infrared Emitting Carbon Dots in Biomedicine.

Second near-infrared (NIR-II) carbon dots, with absorption or emission between 1000 and 1700 nm, are gaining increasing attention in the biomaterial field due to their distinctive properties, which include straightforward preparation processes, stable photophysical characteristics, excellent biocompatibility, and low cost. As a result, there is a growing focus on the controlled synthesis and modulation of the photochemical and photophysical properties of NIR-II carbon dots, with the aim to further expand their biomedical applications, a current research hotspot. This account aims to provide a comprehensive overview of the recent advancements in NIR-II carbon dots within the biomedical field.

Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning.

. Histopathology image segmentation can assist medical professionals in identifying and diagnosing diseased tissue more efficiently. Although fully supervised segmentation models have excellent performance, the annotation cost is extremely expensive.