Phenothiazine-Based Cu(II)-Selective Fluorescent Sensor: GHK-Cu Sensing Applications.

Sensing important metals in different environments is an important area and involves the development of a wide variety of metal-sensing materials. The employment of fluorescent sensors in metal sensing has been one of the most widely applied methodologies, and the identification of selective metal sensors is important. We herein report a phenothiazine-based Cu(II) fluorescent sensor that is highly selective to Cu(II) ions compared with other transition metal salts. The Lewis acidity of the Cu(II) salt certainly was found to be a factor for obtaining an enhanced sensing response in MeOH as the solvent, while a ratio of 1:1 was calculated to be the most optimum for getting the desired response.

Development of Electronic Health Record-Based Machine Learning Models to Predict Barrett's Esophagus and Esophageal Adenocarcinoma Risk.

INTRODUCTION: Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. METHODS: The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models. RESULTS: We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes. DISCUSSION: ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

DNA-encoded libraries via late-stage functionalization strategies: a review.

The hit finding strategy in drug discovery has undergone a tremendous change in the past decade with the advent of DNA-encoded libraries with diverse chemical libraries. The miniaturization of the assays has enabled high-throughput screening on diverse targets to identify binders as a starting point for medicinal chemistry campaign. The diverse chemical space that can be accessed through DEL provides a unique opportunity to explore new chemistries on DNA. This review highlights the metal-mediated synthetic pathways that allow late-stage functionalisation of DNA strands to access such DEL libraries. Critical analysis of the literature and the methods employed has been done to allow readers to understand the usefulness, as well as the limitations of these protocols.