Diagnostic potential of myocardial early systolic lengthening for patients with suspected non-ST-segment elevation acute coronary syndrome.

BACKGROUND: During early systole, ischemic myocardium with reduced active force experiences early systolic lengthening (ESL). This study aimed to explore the diagnostic potential of myocardial ESL in suspected non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients with normal wall motion and left ventricular ejection fraction (LVEF). METHODS: Overall, 195 suspected NSTE-ACS patients with normal wall motion and LVEF, who underwent speckle tracking echocardiography (STE) before coronary angiography, were included in this study. Patients were stratified into the coronary artery disease (CAD) group when there was ≥ 50% stenosis in at least one major coronary artery. The CAD patients were further stratified into the significant (≥ 70% reduction of vessel diameter) stenosis group or the nonsignificant stenosis group. Myocardial strain parameters, including global longitudinal strain (GLS), duration of early systolic lengthening (DESL), early systolic index (ESI), and post-systolic index (PSI), were analyzed using STE and compared between groups. Receiver operating characteristic curve (ROC) analysis was performed to determine the diagnostic accuracy. Logistic regression analysis was conducted to establish the independent and incremental determinants for the presence of significant coronary stenosis. RESULTS: The DESL and ESI values were higher in patients with CAD than those without CAD. In addition, CAD patients with significant coronary stenosis had higher DESL and ESI than those without significant coronary stenosis. The ROC analysis revealed that ESI was superior to PSI for identifying patients with CAD, and further superior to GLS and PSI for predicting significant coronary stenosis. Moreover, ESI could independently and incrementally predict significant coronary stenosis in patients with CAD. CONCLUSIONS: The myocardial ESI is of great value for the diagnosis and risk stratification of clinically suspected NSTE-ACS patients with normal LVEF and wall motion.

Amplitude-integrated electroencephalography improves the predictive ability of acute bilirubin encephalopathy.

BACKGROUND: To establish a clinical prediction model of acute bilirubin encephalopathy (ABE) using amplitude-integrated electroencephalography (aEEG). METHODS: A total of 114 neonatal hyperbilirubinemia patients in the Beijing Chaoyang Hospital from August 2015 to October 2018 were enrolled in this study. There were 62 (54.38%) males, and the age of patients undergoing aEEG examination was 2-23 days, with an average of 7.61±4.08 days. Participant clinical information, peak bilirubin value, albumin value, hyperbilirubinemia, and the graphic indicators of aEEG were extracted from medical records, and ABE was diagnosed according to a bilirubin-induced neurological dysfunction (BIND) score >0. Multivariable logistic regression was used to establish a clinical prediction model of ABE. Furthermore, decision curve analysis (DCA) was performed to evaluate the model's predictive value. RESULTS: According to the BIND score, there were a total of 23 (20.18%) ABE cases. The multivariable logistic regression analysis showed that the value of bilirubin/albumin (B/A), presence of hyperbilirubinemia risk factors, number of sleep-wake cycling (SWC) within 3 hours, widest bandwidth, duration of SWC, and type of SWC were significantly associated with ABE. A clinical prediction model was developed as: p=ex/ (1+ex), X=0.278+0.713*B/A+2.602*with risk factors (with risk factors equals 1) - 1.500*SWC number within 3 hours + 0.219*the widest bandwidth-0.065*the duration of one SWC + 1.491* SWC (mature SWC equals 0, immature SWC equals 1). The area under the curve (AUC) was 0.85 [95% confidence interval (CI): 0.75-0.94], which was significantly higher than the AUC only based on conventional clinical information of B/A (AUC: 0.58, 95% CI: 0.45-0.72). The DCA also showed good predictive ability compared to B/A. CONCLUSIONS: A clinical prediction model can be established based on the patients' B/A, presence of risk factors for hyperbilirubinemia, number of SWC within 3 hours, widest bandwidth, duration of 1 SWC, and the type of SWC. It has good predictive ability and may improve the diagnostic accuracy of ABE.

CFD based parameter tuning for motion control of robotic fish.

After millions of years of evolution, fishes have been endowed with agile swimming ability to accomplish various behaviourally relevant tasks. In comparison, robotic fish are still quite poor swimmers. One of the unique challenges facing robotic fish is the difficulty in tuning the motion control parameters on the robot directly. This is mainly due to the complex fluid environment robotic fish need to contend with and endurance limitations (i.e. battery capacity limitations). To overcome these limitations, we propose a computational fluid dynamics (CFD) simulation platform to first tune the motion control parameters for the computational robotic fish and then refine the parameters by experiments on robotic fish. Within the simulation platform, the body morphology and gait control of the computational robotic fish are designed according to a robotic fish. The gait control is implemented by a central pattern generator (CPG); The CFD model is solved by using a hydrodynamic-kinematics strong-coupling method. We tested our simulation platform with three basic tasks under active disturbance rejection control (ADRC) and try-and-error-based parameter tuning. Trajectory comparisons between the computational robotic fish and robotic fish verify the effectiveness of our simulation platform. Moreover, power costs and swimming efficiency under the motion control are also analyzed based on the outputs from the simulation platform. Our results indicate that the CFD based simulation platform is powerful and robust, and shed new light on the efficient design and parameter optimization of the motion control of robotic fish.