Effect of chloroprocaine combined with morphine on analgesia, adverse reactions and dynamic changes in inflammation in patients receiving TURP

Purpose: To investigate the influence of chloroprocaine combined with morphine on the analgesic effects, adverse reactions and inflammation factors in patients receiving transurethral resection of the prostate (TURP).Methods: A total of 80 patients with benign prostatic hyperplasia (BPH) in the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China, were divided into morphine group and combination-therapy group (morphine combined with chloroprocaine). Pain index, changes in inflammatory factors and incidence of adverse reactions in the two groups of patients were assessed.Results: The morphine group and combination-therapy group showed basic profile prior to the treatments. Visual Analogue Scale (VAS) scores before operation and 6 h after operation in the morphine group were similar to those in the combination-therapy group, but the scores at 12, 24 and 48 h after operation in the combination-therapy group were significantly lower than those in the morphine group. Similarly, the combination-therapy group showed lower levels of substance P (SP) and bradykinin (BK) at 12, 24 and 48 h after operation than the morphine group (p < 0.05). Both groups exhibited similar levels of serum inflammatory factors before the operation, but the levels decreased in the combination-therapy group when compared with those in the morphine group after operation (p < 0.05). The combination-therapy group also showed a lower incidence of adverse reactions than the morphine group.Conclusion: Chloroprocaine combined with morphine effectively ameliorates postoperative pain, lowers secretion of tumor necrosis factor-alpha (TNF-α) and interleukin-10 (IL-10), and decreases the incidence of postoperative adverse reactions, thus affording a high level of safety after operation

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional MRI (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain sub-networks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity–based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work

Molybdenum oxide decorated Ru catalyst for enhancement of lignin oil hydrodeoxygenation to hydrocarbons

Herein, the catalysts of ruthenium and molybdenum oxide nanoparticles supported on activated carbon (AC) were synthesized by precursor stepwise impregnation and employed for the hydrodeoxygenation (HDO) of lignin oil. The XRD, TEM, XPS and NH3-TPD-MS result of RuMoOx/AC catalysts confirm that the method of loading molybdenum precursor before ruthenium precursor is advantageous for the MoOx and Ru nanoparticles high dispersion and the reduction of MoOx species enhance the amount of acidity of catalyst. Meanwhile, the reduction temperature of RuMoOx/AC-1-T catalysts could effectively regulate the MoOx species that determine the activity and product distribution of HDO. The conversion of aromatic monomers/dimers were up to 96% with high selectivity of hydrocarbon over RuMoOx/AC-1-350 catalyst at 160 degrees C and 30 bar H-2. Which is attributed to the MoO3 and Ru species possess the excellent activity of benzene ring deep hydrogenation, ether bond breaking and acid dehydration. The RuMoOx/AC-1-350 catalyst effectively converted lignin oil (from the depolymerization of cornstalk hydrolysis residue) into hydrocarbons (56.9 wt%) and cyclohexanol/ethers (18.7 wt%) under 280 degrees C and 3 bar H-2. The durability of the RuMoOx/AC-1-350 catalyst for HDO of lignin oil was also investigated and showed that the RuMoOx/AC-1-350 catalyst had good stability, regenerability and repeatability. (C) 2021 Published by Elsevier Ltd

Influences of Separator Thickness and Surface Coating on Lithium Dendrite Growth: A Phase-Field Study

Li dendrite growth, which causes potential internal short circuit and reduces battery cycle life, is the main hazard to lithium metal batteries. Separators have the potential to suppress dendrite growth by regulating Li+ distribution without increasing battery weight significantly. However, the underlying mechanism is still not fully understood. In this paper, we apply an electrochemical phase-field model to investigate the influences of separator thickness and surface coating on dendrite growth. It is found that dendrite growth under thicker separators is relatively uniform and the average dendrite length is shorter since the ion concentration within thicker separators is more uniform. Moreover, compared to single layer separators, the electrodeposition morphology under particle-coated separators is smoother since the particles can effectively regulate Li ionic flux and homogenize Li deposition. This study provides significant guidance for designing separators that inhibit dendrites effectively

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China's grasslands. The four models were trained with two strategies: training for all of northern China's grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China's grasslands fairly well, while the SAE model performed best (R-2 = 0.858, RMSE = 0.472 gC m(-2) d(-1), MAE = 0.304 gC m(-2) d(-1)). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy