Design Strategies for Hydroxyapatite-Based Materials to Enhance Their Catalytic Performance and Applicability.

Hydroxyapatite (HAP) is a green catalyst that has a wide range of applications in catalysis due to its high flexibility and multifunctionality. These properties allow HAP to accommodate a large number of catalyst modifications that can selectively improve the catalytic performance in target reactions. To date, many studies have been conducted to elucidate the effect of HAP modification on the catalytic activities for various reactions. However, systematic design strategies for HAP catalysts are not established yet due to an incomplete understanding of underlying structure-activity relationships. In this review, tuning methods of HAP for improving the catalytic performance are discussed: 1) ionic composition change, 2) morphology control, 3) incorporation of other metal species, and 4) catalytic support engineering. Detailed mechanisms and effects of structural modulations on the catalytic performances for attaining the design insights of HAP catalysts are investigated. In addition, computational studies to understand catalytic reactions on HAP materials are also introduced. Finally, important areas for future research are highlighted.

Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability.

Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural network was applied to estimate SV from arterial blood pressure waveform data recorded from liver transplantation (LT) surgeries. The model was trained using a gold standard referential SV measured via pulmonary artery thermodilution method. Merging a gold standard SV and corresponding 10.24 seconds of arterial blood pressure waveform as an input/output data set with 2-senconds of sliding overlap, 484,384 data sets from 34 LT surgeries were used for training and validation of DL model. The performance of DL model was evaluated by correlation and concordance analyses in another 491,353 data sets from 31 LT surgeries. We also evaluated the performance of pre-existing commercialized model (EV1000), and the performance results of DL model and EV1000 were compared. The DL model provided an acceptable performance throughout the surgery (r = 0.813, concordance rate = 74.15%). During the reperfusion phase, where the most severe hemodynamic instability occurred, DL model showed superior correlation (0.861; 95% Confidence Interval, (CI), 0.855-0.866 vs. 0.570; 95% CI, 0.556-0.584, P < 0.001) and higher concordance rate (90.6% vs. 75.8%) over EV1000. In conclusion, the DL-based model was superior for estimating intraoperative SV and thus might guide physicians to precise intraoperative hemodynamic management. Moreover, the DL model seems to be particularly promising because it outperformed EV1000 in circumstance of rapid hemodynamic changes where physicians need most help.