Identifying unreliable predictions in clinical risk models.

The ability to identify patients who are likely to have an adverse outcome is an essential component of good clinical care. Therefore, predictive risk stratification models play an important role in clinical decision making. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model's performance on large patient datasets using standard statistical measures of success (e.

2.5-Dimensional Parylene C micropore array with a large area and a high porosity for high-throughput particle and cell separation.

Large-area micropore arrays with a high porosity are in high demand because of their promising potential in liquid biopsy with a large volume of clinical sample. However, a micropore array with a large area and a high porosity faces a serious mechanical strength challenge. The filtration membrane may undergo large deformation at a high filtration throughput, which will decrease its size separation accuracy.

Enhanced parylene-C fluorescence as a visual marker for neuronal electrophysiology applications.

Parylene-C is a popular polymer material in biomedical applications, with excellent physicochemical properties and microfabrication capability. Like many aromatic polymers, parylene-C also has autofluorescence, which was usually taken as a negative background noise in biomedical detection studies. However, the fluorescence intensity of thin-film (<1 μm) parylene-C was relatively weak, which may be a big limitation in visualization.