The diagnosis of biliary atresia (BA) remains a clinical challenge because affected infants have signs, symptoms, and serum liver biochemistry that are also seen in those with other causes of neonatal cholestasis (non-BA). However, an early diagnosis and prompt surgical treatment are required to improve clinical outcome. Recently, the relative abundance of serum matrix metalloproteinase-7 (MMP-7) was suggested to have discriminatory features for infants with BA. To test the hypothesis that elevated serum concentration of MMP-7 is highly diagnostic for BA, we determined the normal serum concentration of MMP-7 in healthy control infants, and then in 135 consecutive infants being evaluated for cholestasis. The median concentration for MMP-7 was 2.86 ng/mL (interquartile range, IQR: 1.32-5.32) in normal controls, 11.47 ng/mL (IQR: 8.54-24.55) for non-BA, and 121.1 ng/mL (IQR: 85.42-224.4) for BA (P < 0.0001). The area under the curve of MMP-7 for the diagnosis of BA was 0.9900 with a cutoff value of 52.85 ng/mL; the diagnostic sensitivity and specificity were 98.67% and 95.00%, respectively, with a negative predictive value of 98.28%. Conclusion: Serum MMP-7 assay has high sensitivity and specificity to differentiate BA from other neonatal cholestasis, and may be a reliable biomarker for BA.

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Brain–computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect.

Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world.