Search for: All records

Water-soluble deep cavitands with cationic functions at the lower rim can selectively bind iodide anions in purely aqueous solution. By pairing this lower rim recognition with an indicator dye that is bound in the host cavity, optical sensing of anions is possible. The selectivity for iodide is high enough that micromolar concentrations of iodide can be detected in the presence of molar chloride. Iodide binding at the “remote” lower rim causes a conformational change in the host, displacing the bound dye from the cavity and effecting a fluorescence response. The sensing is sensitive, selective, and works in complex environments, so will be important for optical anion detection in biorelevant media. 

Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.

 

An arrayed combination of water-soluble deep cavitands and cationic dyes has been shown to optically sense insect pheromones at micromolar concentration in water. Machine learning approaches were used to optimize the most effective array components, which allows differentiation between small structural differences in targets, including between different diastereomers, even though the pheromones have no innate chromophore. When combined with chiral additives, enantiodiscrimination is possible, dependent on the size and shape of the pheromone.