Programmable DNA pyrimidine base editing via engineered uracil-DNA glycosylase.

DNA base editing technologies predominantly utilize engineered deaminases, limiting their ability to edit thymine and guanine directly. In this study, we successfully achieve base editing of both cytidine and thymine by leveraging the translesion DNA synthesis pathway through the engineering of uracil-DNA glycosylase (UNG). Employing structure-based rational design, exploration of homologous proteins, and mutation screening, we identify a Deinococcus radiodurans UNG mutant capable of effectively editing thymine. When fused with the nickase Cas9, the engineered DrUNG protein facilitates efficient thymine base editing at endogenous sites, achieving editing efficiencies up to 55% without enrichment and exhibiting minimal cellular toxicity. This thymine base editor (TBE) exhibits high editing specificity and significantly restores IDUA enzyme activity in cells derived from patients with Hurler syndrome. TBEs represent efficient, specific, and low-toxicity approaches to base editing with potential applications in treating relevant diseases.

Predicting fracture risk for elderly osteoporosis patients by hybrid machine learning model.

Background and Objective: Osteoporotic fractures significantly impact individuals's quality of life and exert substantial pressure on the social pension system. This study aims to develop prediction models for osteoporotic fracture and uncover potential risk factors based on Electronic Health Records (EHR). Methods: Data of patients with osteoporosis were extracted from the EHR of Xinhua Hospital (July 2012-October 2017). Demographic and clinical features were used to develop prediction models based on 12 independent machine learning (ML) algorithms and 3 hybrid ML models. To facilitate a nuanced interpretation of the results, a comprehensive importance score was conceived, incorporating various perspectives to effectively discern and mine critical features from the data. Results: A total of 8530 patients with osteoporosis were included for analysis, of which 1090 cases (12.8%) were fracture patients. The hybrid model that synergistically combines the Support Vector Machine (SVM) and XGBoost algorithms demonstrated the best predictive performance in terms of accuracy and precision (above 90%) among all benchmark models. Blood Calcium, Alkaline phosphatase (ALP), C-reactive Protein (CRP), Apolipoprotein A/B ratio and High-density lipoprotein cholesterol (HDL-C) were statistically found to be associated with osteoporotic fracture. Conclusions: The hybrid machine learning model can be a reliable tool for predicting the risk of fracture in patients with osteoporosis. It is expected to assist clinicians in identifying high-risk fracture patients and implementing early interventions.