Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites.

Antibodies Expand the Scope of Angiotensin Receptor Pharmacology.

G protein-coupled receptors (GPCRs) are key regulators of human physiology and are the targets of many small molecule research compounds and therapeutic drugs. While most of these ligands bind to their target GPCR with high affinity, selectivity is often limited at the receptor, tissue, and cellular level. Antibodies have the potential to address these limitations but their properties as GPCR ligands remain poorly characterized.

An in silico method to assess antibody fragment polyreactivity.

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.

Rational Ligand Design Enables pH Control over Aqueous Iron Magnetostructural Dynamics and Relaxometric Properties.

Complexes of Fe engage in rich aqueous solution speciation chemistry in which discrete molecules can react with solvent water to form multinuclear μ-oxo and μ-hydroxide bridged species. Here we demonstrate how pH- and concentration-dependent equilibration between monomeric and μ-oxo-bridged dimeric Fe complexes can be controlled through judicious ligand design. We purposed this chemistry to develop a first-in-class Fe-based MR imaging probe, Fe-PyCy2AI, that undergoes relaxivity change via pH-mediated control of monomer vs dimer speciation.