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.g., accuracy, discriminatory ability). However, as these metrics correspond to averages over patients who have a range of different characteristics, it is difficult to discern whether an individual prediction on a given patient should be trusted using these measures alone. In this paper, we introduce a new method for identifying patient subgroups where a predictive model is expected to be poor, thereby highlighting when a given prediction is misleading and should not be trusted. The resulting "unreliability score" can be computed for any clinical risk model and is suitable in the setting of large class imbalance, a situation often encountered in healthcare settings. Using data from more than 40,000 patients in the Global Registry of Acute Coronary Events (GRACE), we demonstrate that patients with high unreliability scores form a subgroup in which the predictive model has both decreased accuracy and decreased discriminatory ability.

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. In this work, we reported a simple annealing method to significantly enhance the fluorescence and achieve sufficient intensity as a visual marker. We studied the behaviors and mechanisms of the enhanced parylene-C fluorescence, then verified the feasibility and reliability of parylene-C for preparing fluorescent pipettes in targeted neuronal electrophysiology, where fluorescent guidance was strongly needed. The powerful parylene-C fabrication technique enables a precisely-controlled conformal coating along with a mass production capability, which further resulted in high-quality electrophysiological recordings of both cultured hippocampal neurons and acute hippocampal brain slices. Moreover, the enhanced parylene-C fluorescence can also be applied in more general biological operations, such as designable fluorescent micro-patterns for visualization in broader biomedical fields.

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. In this work, a keyhole-free Parylene molding process has been developed to prepare a large (>20 mm × 20 mm) filtration membrane containing a 2.5-dimensional (2.5D) micropore array with an ultra-high porosity (up to 91.37% with designed pore diameter/space of 100 µm/4 µm). The notation 2.5D indicates that the large area and the relatively small thickness (approximately 10 µm) of the fabricated membranes represent 2D properties, while the large thickness-to-width ratio (10 µm/ < 4 µm) of the spaces between the adjacent pores corresponds to a local 3D feature. The large area and high porosity of the micropore array achieved filtration with a throughput up to 180 mL/min (PBS solution) simply driven by gravity. Meanwhile, the high mechanical strength, benefiting from the 2.5D structure of the micropore array, ensured a negligible pore size variation during the high-throughput filtration, thereby enabling high size resolution separation, which was proven by single-layer and multi-layer filtrations for particle separation. Furthermore, as a preliminary demonstration, the prepared 2.5-dimensional Parylene C micropore array was implemented as an efficient filter for rare cancer cell separation from a large volume, approximately 10 cells in 10 mL PBS and undiluted urine, with high recovery rates of 87 ± 13% and 56 ± 13%, respectively.