Automated tracking of level of consciousness and delirium in critical illness using deep learning

Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician-nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU

Spatial Dynamics of the Communities and the Role of Major Countries in the International Rare Earths Trade: A Complex Network Analysis.

Rare earths (RE) are critical materials in many high-technology products. Due to the uneven distribution and important functions for industrial development, most countries import RE from a handful of suppliers that are rich in RE, such as China. However, because of the rapid growth of RE exploitation and pollution of the mining and production process, some of the main suppliers have gradually tended to reduce the RE production and exports. Especially in the last decade, international RE trade has been changing in the trade community and trade volume. Based on complex network theory, we built an unweighted and weighted network to explore the evolution of the communities and identify the role of the major countries in the RE trade. The results show that an international RE trade network was dispersed and unstable because of the existence of five to nine trade communities in the unweighted network and four to eight trade communities in the weighted network in the past 13 years. Moreover, trade groups formed due to the great influence of geopolitical relations. China was often associated with the South America and African countries in the same trade group. In addition, Japan, China, the United States, and Germany had the largest impacts on international RE trade from 2002 to 2014. Last, some policy suggestions were highlighted according to the results

Simulation and Experiment of Gas-Solid Flow in a Safflower Sorting Device Based on the CFD-DEM Coupling Method

To study the movement characteristics and separation mechanism of safflower petals and their impurities under the action of airflow and lower the impurity rate in the cleaning operation process, integration of computational fluid dynamics (CFD) and discrete element method (DEM) codes was performed to study the motion and sorting behavior of impurity particles and safflower petals under different airflow inclination angles, dust removal angles and inlet airflow velocities by establishing a true particle model. In this model, the discrete particle phase was applied by the DEM software, and the continuum gas phase was described by the ANSYS Fluent software. The Box-Behnken experimental design with three factors and three levels was performed, and parameters such as inlet airflow velocity, airflow inclined angle, and dust remover angle were selected as independent variables that would influence the cleaning impurity rate and the cleaning loss rate. A mathematical model was established, and then the effects of various parameters and their interactions were analyzed. The test results show that the cleaning effect is best when the inlet airflow velocity is 7 m/s, the airflow inclined angle is 0°, and the dust remover angle is 25°. Confirmatory tests showed that the average cleaning impurity rate and cleaning loss rate were 0.69% and 2.75%, respectively, which dropped significantly compared with those from previous optimization. An experimental device was designed and set up; the experimental results were consistent with the simulation results, indicating that studying the physical behavior of safflower petals-impurity separation in the airflow field by using the DEM-CFD coupling method is reliable. This result provides a basis for follow-up studies of separation and cleaning devices for lightweight materials such as safflower petals

Quantitative Relationships between Pulmonary Function and Residual Neuromuscular Blockade

Background. Neuromuscular blockade is a risk factor for postoperative respiratory weakness during the immediate postoperative period. The quantitative relationships between postoperative pulmonary-function impairment and residual neuromuscular blockade are unknown. Methods. 113 patients who underwent elective laparoscopic cholecystectomy were enrolled in this study. They all had a pulmonary-function test (PFT) during the preoperative evaluation. Predictive values based on demographic data were also recorded. The train-of-four ratio (TOFR) was recorded at the same time as the PFT and at every 5 minutes in the qualified 98 patients in the postanesthesia care unit (PACU). We analyzed the degree of PFT recovery when the TOFR had recovered to different degrees. Results. There was a significant difference (P<0.05) between the preoperative baseline value and the postoperative forced vital capacity at each TOFR point, except at a TOFR value of 1.1. There was also a significant difference (P<0.05) between the preoperative baseline value and the postoperative peak expiratory flow at each TOFR point. Conclusions. Postoperative residual neuromuscular blockade was common (75.51%) after tracheal extubation, and pulmonary function could not recover to an acceptable level (85% of baseline value), even if TOFR had recovered to 0.90. Trial Registration. Chinese Clinical Trial Register is ChiCTR-OOC-15005838

The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Abstract Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans

Toward carbon neutrality: Uncovering constraints on critical minerals in the Chinese power system

China has set up its ambitious carbon neutrality target, which mainly relies on significant energy-related carbon emissions reduction. As the largest important contributing sector, power sector must achieve energy transition, in which critical minerals will play an essential role. However, the potential supply and demand for these minerals are uncertain. This study aims to predict the cumulative demand for critical minerals in the power sector under different scenarios via dynamic material flow analysis (DMFA), including total demands, supplies and production capacities of different minerals. Then, these critical minerals are categorized into superior and scarce resources for further analysis so that more detailed results can be obtained. Results present that the total minerals supply will not meet the total minerals demand (74260 kt) in 2060. Serious resource shortages will occur for several key minerals, such as Cr, Cu, Mn, Ag, Te, Ga, and Co. In addition, the demand for renewable energy will be nearly fifty times higher than that of fossil fuels energy, implying more diversified demands for various minerals. Finally, several policy recommendations are proposed to help improve the overall resource efficiency, such as strategic reserves, material substitutions, and circular economy