Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning.

Background/objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare systems. Predicting injections needs at each monitoring session could optimize treatment outcomes and reduce unnecessary interventions.

Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration.

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis.

Synthesis and screening of a library of Lewis deoxyfluoro-analogues reveals differential recognition by glycan-binding partners.

Glycan-mediated interactions play a crucial role in biology and medicine, influencing signalling, immune responses, and disease pathogenesis. However, the use of glycans in biosensing and diagnostics is limited by cross-reactivity, as certain glycan motifs can be recognised by multiple biologically distinct protein receptors. To address this specificity challenge, we report the enzymatic synthesis of a 150-member library of site-specifically fluorinated Lewis analogues ('glycofluoroforms') using naturally occurring enzymes and fluorinated monosaccharides.