Predictors and occurrence of postoperative cognitive dysfunction in children undergoing noncardiac surgery: A prospective cohort study.

In children after cardiac surgery, alterations in cognitive ability and behavior are increasingly common, but whether postoperative cognitive dysfunction (POCD) occurs in children undergoing noncardiac surgery is not known. The present study was performed to investigate the incidence rate and potential risk factors of early neurocognitive dysfunction in children after noncardiac surgery. Two hundred patients aged between 4 and 14 years old underwent elective noncardiac surgery and 100 healthy age-matched controls were enrolled in this prospective observational study. Wechsler Preschool and Primary Scale of Intelligence or Wechsler Intelligence Scale for Children-Revised were conducted 1 day before and 3 days after surgery. POCD was calculated and diagnosed as a combined Z score. Any factors that differed between POCD and non-POCD group (p < 0.10) were tested together by multivariate logistic regression analysis against the cognitive outcome of patients, to find out the independent risk factors of POCD. The general incidence of POCD was 15.6%. The univariate analysis revealed that POCD was associated with general anesthesia, surgical and anesthesia duration, early postoperative fever (EPF), and surgical history. However, only the history of surgery (p = 0.029), anesthesia duration (p = 0.010), and EPF (p < 0.001) were demonstrated to be independent risk factors for POCD. The occurrence rate of early POCD after noncardiac surgery in children is 15.6%. Children who had surgical history, longer anesthesia duration, or EPF are more prone to develop POCD.

Tumor-targeted glycogen nanoparticles loaded with hemin and glucose oxidase to promote tumor synergistic therapy.

Strategies which are used to address the low levels of intracellular hydrogen peroxide and the development of biocompatible catalysts still need to be fulfilled in tumor chemodynamic therapy. Therefore, a novel tumor-targeted glycogen-based nanoparticle system (GN/He/GOx/HA) was developed to co-deliver hemin (He) and GOx, which can self-supply glucose formed upon degradation of glycogen by α-glycosidase in the lysosome environment, in order to achieve synergistic antitumor therapy. Hyaluronic acid (HA) was selected as the outer shell to protect the activity of GOx, and to increase the uptake by tumor cells via CD44 receptor-mediated endocytosis. GN/He/GOx/HA NPs had a good stability in the blood circulation, but fast release of the therapeutic cargos upon intracellular uptake. Hemin had a cascade catalytic reaction with GOx. Furthermore, GN/He/GOx/HA NPs had the strongest cytotoxicity in Hela cells in a glucose concentration dependent manner. The NPs could efficiently produce reactive oxygen species in tumor cells, resulting in a decrease in the mitochondrial membrane potential and apoptosis of tumor cells. The in vivo results showed that the drug-loaded nanoparticles had good safety, biocompatibility, and efficacious antitumor effect. Therefore, the glycogen-based nanoparticle delivery system provides potential application for self-enhancing CDT, which can be used for effective antitumor therapy.

Enzyme-loaded glycogen nanoparticles with tumor-targeting Activatable host-guest supramolecule for augmented chemodynamic therapy.

Chemodynamic therapy (CDT) has advantages in site-specific killing tumor and avoiding systemically side effect. Although numerous nano-systems have been developed to enhance the intracellular hydrogen peroxide (H2O2) for improving CDT effect, the biocompatibility of the materials limits their further biomedical applications. Herein glycogen, as a natural biological macromolecule, was used to construct a new targeted separable GOx@GF/HC nanoparticle system to deliver glucose oxidase (GOx) for CDT/starvation tumor therapy. Amination glycogen-ferrocene (GF) as a guest core and hyaluronic acid-ß-cyclodextrin (HC) as a host shell were synthesized and self-assembled through host-guest interactions to deliver GOx. After being entered into tumor cells, GOx were released to catalyze glucose to produce gluconic acid and H2O2, which in turn cut off the nutrition of tumor cells for starvation therapy and enhanced the generation of OH with ferrous ion through Fenton reaction. Furthermore, GOx@GF/HC also exhibited remarkable tumor-targeting and tumor-suppression in vivo. Therefore, the GOx@GF/HC system can exert excellent synergistic effect of CDT and starvation therapy on cancer treatment through a cascade reaction, which have some potential application for the development of CDT tumor-treatment.

Feature Selection Using a Neural Network With Group Lasso Regularization and Controlled Redundancy.

We propose a neural network-based feature selection (FS) scheme that can control the level of redundancy in the selected features by integrating two penalties into a single objective function. The Group Lasso penalty aims to produce sparsity in features in a grouped manner. The redundancy-control penalty, which is defined based on a measure of dependence among features, is utilized to control the level of redundancy among the selected features. Both the penalty terms involve the L2,1 -norm of weight matrix between the input and hidden layers. These penalty terms are nonsmooth at the origin, and hence, one simple but efficient smoothing technique is employed to overcome this issue. The monotonicity and convergence of the proposed algorithm are specified and proved under suitable assumptions. Then, extensive experiments are conducted on both artificial and real data sets. Empirical results explicitly demonstrate the ability of the proposed FS scheme and its effectiveness in controlling redundancy. The empirical simulations are observed to be consistent with the theoretical results.

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.

BACKGROUND: The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. OBJECTIVE: The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. METHODS: We used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. RESULTS: Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients' outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. CONCLUSIONS: Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Learning Optimized Structure of Neural Networks by Hidden Node Pruning With L1 Regularization.

We propose three different methods to determine the optimal number of hidden nodes based on L1 regularization for a multilayer perceptron network. The first two methods, respectively, use a set of multiplier functions and multipliers for the hidden-layer nodes and implement the L1 regularization on those, while the third method equipped with the same multipliers uses a smoothing approximation of the L1 regularization. Each of these methods begins with a given number of hidden nodes, then the network is trained to obtain an optimal architecture discarding redundant hidden nodes using the multiplier functions or multipliers. A simple and generic method, namely, the matrix-based convergence proving method (MCPM), is introduced to prove the weak and strong convergence of the presented smoothing algorithms. The performance of the three pruning methods has been tested on 11 different classification datasets. The results demonstrate the efficient pruning abilities and competitive generalization by the proposed methods. The theoretical results are also validated by the results.