fURS-rawdata.xlsx

A total of 156 patients' characteristics in stenting and non-stenting grou

Cdc42-Interacting Protein 4 Represses E-Cadherin Expression by Promoting β-Catenin Translocation to the Nucleus in Murine Renal Tubular Epithelial Cells

Renal fibrosis is an inevitable outcome of end-stage chronic kidney disease. During this process, epithelial cells lose E-cadherin expression. β-Catenin may act as a mediator by accumulation and translocation to the nucleus. Studies have suggested that CIP4, a Cdc42 effector protein, is associated with β-catenin. However, whether CIP4 contributes to E-cadherin loss in epithelial cells by regulating β-catenin translocation is unclear. In this study, we investigated the involvement of CIP4 in β-catenin translocation. Expression of CIP4 was upregulated in renal tissues of 5/6 nephrectomized rats and mainly distributed in renal tubular epithelia. In TGF-β1-treated NRK-52E cells, upregulation of CIP4 expression was accompanied by reduced expression of E-cadherin. CIP4 overexpression promoted the translocation of β-catenin to the nucleus, which was accompanied by reduced expression of E-cadherin even without TGF-β1 stimulation. In contrast, CIP4 depletion by using siRNA inhibited the translocation of β-catenin to the nucleus and reversed the decrease in expression of E-cadherin. The interaction between CIP4 and β-catenin was detected. We also show that β-catenin depletion could restore the expression of E-cadherin that was suppressed by CIP4 overexpression. In conclusion, these results suggest that CIP4 overexpression represses E-cadherin expression by promoting β-catenin translocation to the nucleus

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health