Anaesthesia administered as S(+)-ketamine for cardiac intervention in children with common congenital heart disease.

BACKGROUND: Safety and efficacy were assessed of different S(+)-ketamine doses combined with propofol administered as anesthesia to common pediatric congenital heart disease (CHD) patients undergoing cardiac interventional surgery to provide reference data as guidance for use in clinical settings. METHODS: Sixty CHD children admitted to Beijing Anzhen Hospital, Capital Medical University from December 2020 to December 2021 who underwent elective cardiac intervention were assigned to 3 groups (H, L, M, 20 patients/group) using a random number table-based method. Patients received 1% propofol (2 mg/kg) and intravenous injections of S(+)-ketamine (Group L, 0.4 mg/kg; Group M, 0.5 mg/kg; Group H, 0.6 mg/kg) followed by intravenous pumping of 1% propofol (4-6 mg/kg/h). Heart rate (HR), mean arterial pressure, and pulse oxygen saturation were recorded preoperatively (T0), at the time of anesthesia maintenance (T1), at the time of arteriovenous puncture (T2), and when they awakened (T3). Additionally, propofol dose and incidence rates of intraoperative body movement, postoperative agitation, and postoperative nausea/vomiting were recorded. RESULTS: For the 3 groups, Group H awakening time was significantly longer than that of Group L (P = .039). Notably, intergroup intraoperative propofol times differed significantly (P = .009). Meanwhile, T0 to T3 intragroup HR values differences were significant (P = .017; P = .001; P = .005, respectively). Group L HR was significantly elevated at T2 relative to T0 (P = .003), Group M HR was significantly elevated at T1 and T2 relative to T0 (P = .019; P = .003, respectively), and Group H HR values were significantly elevated at T1 and T2 relative to T0 (P = .012; P = .005, respectively). At all 4 time points no statistically significant intergroup differences in mean arterial pressure values were observed (P = .587). T1 to T3 pulse oxygen saturation values for all 3 groups were significantly greater than corresponding T0 values. Although intergroup intraoperative body movement incidence differed significantly (P = .044), intergroup differences in awakening time agitation and postoperative nausea/vomiting incidence rates were insignificant (P = .732, P = .887, respectively). CONCLUSION: Use of 0.6 mg/kg S(+)-ketamine with propofol was most effective as anesthesia for common pediatric CHD patients undergoing cardiac interventional surgery.

Reducing noisy annotations for depression estimation from facial images.

Depression has been considered the most dominant mental disorder over the past few years. To help clinicians effectively and efficiently estimate the severity scale of depression, various automated systems based on deep learning have been proposed. To estimate the severity of depression, i.e., the depression severity score (Beck Depression Inventory-II), various deep architectures have been designed to perform regression using the Euclidean loss. However, they do not consider the label distribution, and they do not learn the relationships between the facial images and BDI-II scores, which can be resulting in the noisy labeling for automatic depression estimation (ADE). To mitigate this problem, we propose an automated deep architecture, namely the self-adaptation network (SAN), to improve this uncertain labeling for ADE. Specifically, the architecture consists of four modules: (1) ResNet-18 and ResNet-50 are adopted in the deep feature extraction module (DFEM) to extract informative deep features; (2) a self-attention module (SAM) is adopted to learn the weights from the mini-batch; (3) a square ranking regularization module (SRRM) to create high partitions and low partitions is proposed; and (4) a re-label module (RM) is used to re-label the uncertain annotations for ADE in the low partitions. We conduct extensive experiments on depression databases (i.e., AVEC2013 and AVEC2014) and obtain a performance comparable to the performances of other ADE methods in assessing the severity of depression. More importantly, the proposed method can learn valuable depression patterns from facial videos and obtain a performance comparable to the performances of other methods for depression recognition.

A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy.

In protein-ligand docking, an optimization algorithm is used to find the best binding pose of a ligand against a protein target. This algorithm plays a vital role in determining the docking accuracy. To evaluate the relative performance of different optimization algorithms and provide guidance for real applications, we performed a comparative study on six efficient optimization algorithms, containing two evolutionary algorithm (EA)-based optimizers (LGA, DockDE) and four particle swarm optimization (PSO)-based optimizers (SODock, varCPSO, varCPSO-ls, FIPSDock), which were implemented into the protein-ligand docking program AutoDock. We unified the objective functions by applying the same scoring function, and built a new fitness accuracy as the evaluation criterion that incorporates optimization accuracy, robustness, and efficiency. The varCPSO and varCPSO-ls algorithms show high efficiency with fast convergence speed. However, their accuracy is not optimal, as they cannot reach very low energies. SODock has the highest accuracy and robustness. In addition, SODock shows good performance in efficiency when optimizing drug-like ligands with less than ten rotatable bonds. FIPSDock shows excellent robustness and is close to SODock in accuracy and efficiency. In general, the four PSO-based algorithms show superior performance than the two EA-based algorithms, especially for highly flexible ligands. Our method can be regarded as a reference for the validation of new optimization algorithms in protein-ligand docking.

Specificity and affinity quantification of protein-protein interactions.

MOTIVATION: Most biological processes are mediated by the protein-protein interactions. Determination of the protein-protein structures and insight into their interactions are vital to understand the mechanisms of protein functions. Currently, compared with the isolated protein structures, only a small fraction of protein-protein structures are experimentally solved. Therefore, the computational docking methods play an increasing role in predicting the structures and interactions of protein-protein complexes. The scoring function of protein-protein interactions is the key responsible for the accuracy of the computational docking. Previous scoring functions were mostly developed by optimizing the binding affinity which determines the stability of the protein-protein complex, but they are often lack of the consideration of specificity which determines the discrimination of native protein-protein complex against competitive ones. RESULTS: We developed a scoring function (named as SPA-PP, specificity and affinity of the protein-protein interactions) by incorporating both the specificity and affinity into the optimization strategy. The testing results and comparisons with other scoring functions show that SPA-PP performs remarkably on both predictions of binding pose and binding affinity. Thus, SPA-PP is a promising quantification of protein-protein interactions, which can be implemented into the protein docking tools and applied for the predictions of protein-protein structure and affinity. AVAILABILITY: The algorithm is implemented in C language, and the code can be downloaded from http://dl.dropbox.com/u/1865642/Optimization.cpp.