Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
6,108
result(s) for
"Yue, Hua"
Sort by:
DPDDI: a deep predictor for drug-drug interactions
Background
The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases.
Results
In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs.
Conclusion
We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.
Journal Article
Rationally designed transition metal hydroxide nanosheet arrays on graphene for artificial CO2 reduction
The performance of transition metal hydroxides, as cocatalysts for CO
2
photoreduction, is significantly limited by their inherent weaknesses of poor conductivity and stacked structure. Herein, we report the rational assembly of a series of transition metal hydroxides on graphene to act as a cocatalyst ensemble for efficient CO
2
photoreduction. In particular, with the Ru-dye as visible light photosensitizer, hierarchical Ni(OH)
2
nanosheet arrays-graphene (Ni(OH)
2
-GR) composites exhibit superior photoactivity and selectivity, which remarkably surpass other counterparts and most of analogous hybrid photocatalyst system. The origin of such superior performance of Ni(OH)
2
-GR is attributed to its appropriate synergy on the enhanced adsorption of CO
2
, increased active sites for CO
2
reduction and improved charge carriers separation/transfer. This work is anticipated to spur rationally designing efficient earth-abundant transition metal hydroxides-based cocatalysts on graphene and other two-dimension platforms for artificial reduction of CO
2
to solar chemicals and fuels.
The development of effective, earth-abundant cocatalysts is critical for photocatalytic CO
2
reduction. Here, authors report the assembly of transition metal hydroxides on graphene to act as cocatalyst ensembles for efficient CO
2
photoreduction.
Journal Article
Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between drugs within the DDI network and new drugs from outside the DDI network. In addition, they are not designed to explore the underlying mechanisms of DDIs and lack interpretative capacity. Thus, here we propose a novel method of GNN-DDI to predict potential DDIs by constructing a five-layer graph attention network to identify k-hops low-dimensional feature representations for each drug from its chemical molecular graph, concatenating all identified features of each drug pair, and inputting them into a MLP predictor to obtain the final DDI prediction score. The experimental results demonstrate that our GNN-DDI is suitable for each of two DDI predicting scenarios, namely the potential DDIs among known drugs in the DDI network and those between drugs within the DDI network and new drugs from outside DDI network. The case study indicates that our method can explore the specific drug substructures that lead to the potential DDIs, which helps to improve interpretability and discover the underlying interaction mechanisms of drug pairs.
Journal Article
Problem-solving males become more attractive to female budgerigars
Darwin proposed that mate choice might contribute to the evolution of cognitive abilities. An open question is whether observing the cognitive skills of an individual makes it more attractive as a mate. In this study, we demonstrated that initially less-preferred budgerigar males became preferred after females observed that these males, but not the initially preferred ones, were able to solve extractive foraging problems. This preference shift did not occur in control experiments in which females observed males with free access to food or in which females observed female demonstrators solving these extractive foraging problems. Our results suggest that direct observation of problem-solving skills increases male attractiveness and that this could contribute to the evolution of the cognitive abilities underlying such skills.
Journal Article
Kaempferol Attenuates LPS-Induced Striatum Injury in Mice Involving Anti-Neuroinflammation, Maintaining BBB Integrity, and Down-Regulating the HMGB1/TLR4 Pathway
Neuroinflammation has been demonstrated to be linked with Parkinson’s disease (PD), Alzheimer’s disease, and cerebral ischemia. Our previous investigation had identified that kaempferol (KAE) exerted protective effects on cortex neuron injured by LPS. In this study, the effects and possible mechanism of KAE on striatal dopaminergic neurons induced by LPS in mice were further investigated. The results showed that KAE improved striatal neuron injury, and increased the levels of tyrosine hydroxylase (TH) and postsynaptic density protein 95 (PSD95) in the striatum of mice. In addition, KAE inhibited the production of pro-inflammatory cytokines, including interleukin 1β (IL-1β), interleukin 6 (IL-6), tumor necrosis factor α (TNF-α), reduced the level of monocyte chemotactic protein-1 (MCP-1), intercellular cell adhesion molecule-1 (ICAM-1), and cyclooxygenase-2 (COX-2) in the striatum tissues. Furthermore, KAE protected blood-brain barrier (BBB) integrity and suppressed the activation of the HMGB1/TLR4 inflammatory pathway induced by LPS in striatum tissues of mice. In conclusion, these results suggest that KAE may have neuroprotective effects against striatum injury that is induced by LPS and the possible mechanisms are involved in anti-neuroinflammation, maintaining BBB integrity, and down-regulating the HMGB1/TLR4 pathway.
Journal Article
The effects of temperature on mental health: evidence from China
We examine the effects of ambient temperatures on mental health using a nationally representative longitudinal survey of Chinese individuals. We find that temperatures over 30∘C significantly increase the likelihood of depression. High temperatures have larger detrimental effects on the mental health of the middle-aged and elderly, females, the less-educated, and agricultural workers. We discuss two likely mechanisms for the mental health impact of high temperatures: raising the incidence of physical illness and reducing sleeping time. We find suggestive evidence of air conditioners moderating the adverse impacts of high temperatures and of adaptation to high temperatures in the long term. We reveal that without any government interventions or private adaptation, mental health will deteriorate by 3.1% in the medium term and 5.3% in the long term based on the Hadley GEM2-ES climate-change projection.
Journal Article
Quercetin Attenuates Atherosclerosis via Modulating Oxidized LDL-Induced Endothelial Cellular Senescence
Endothelial senescence is an important risk factor leading to atherosclerosis. The mechanism of quercetin against endothelial senescence is worth exploring.
Quercetin (20 mg/kg/d) was administered to ApoE
mice intragastrically to evaluate the effectiveness of quercetin on atherosclerotic lesion
.
, human aortic endothelial cells (HAECs) were used to assess the effect of quercetin on cellular senescence induced by oxidized low-density lipoprotein (ox-LDL). Transcriptome microarray and quantitative RT-PCR was conducted to study the pharmacological targets of quercetin.
ApoE
mice demonstrated obvious lipid deposition in arterial lumina, high level of serum sIcam-1 and IL-6, and high density of Vcam-1 and lower density of Sirt1 in aorta. Quercetin administration decreased lipid deposition in arterial lumina, serum sIcam-1, and IL-6 and Vcam-1 in aorta, while increased the density of Sirt1 in aorta of ApoE
mice.
, quercetin (0.3, 1, or 3 μmol/L) decreased the expression of senescence-associated β-galactosidase and improved cell morphology of HAECs. And quercetin decreased the cellular apoptosis and increased mitochondrial membrane potential (ΔΨm) in dose-dependent manner, and decreased ROS generation simultaneously. Transcriptome microarray suggested 254 differentially expressed (DE) mRNAs (110 mRNAs were upregulated and 144 mRNAs were downregulated) in HAECs after quercetin treatment (fold change > 1.5,
< 0 .05, Que
Ox-LDL). GO and KEGG analysis indicated nitrogen metabolism, ECM-receptor interaction, complement, and coagulation cascades, p53 and mTOR signaling pathway were involved in the pharmacological mechanisms of quercetin against ox-LDL.
Quercetin alleviated atherosclerotic lesion both
and
.
Journal Article
Polymeric micro/nanoparticles: Particle design and potential vaccine delivery applications
•The effects of physiochemical property on the immunological response were clarified by using uniform micro/nanoparticles.•Particle adjuvants with optimized attributes were designed for preventive or therapeutic vaccine.•The underlying mechanisms of the particles-based vaccine were uncovered.
Particle based adjuvant showed promising signs on delivering antigen to immune cells and acting as stimulators to elicit preventive or therapeutic response. Nevertheless, the wide size distribution of available polymeric particles has so far obscured the immunostimulative effects of particle adjuvant, and compromised the progress in pharmacological researches. To conquer this hurdle, our research group has carried out a series of researches regarding the particulate vaccine, by taking advantage of the successful fabrication of polymeric particles with uniform size. In this review, we highlight the insight and practical progress focused on the effects of physiochemical property (e.g. particle size, charge, hydrophobicity, surface chemical group, and particle shape) and antigen loading mode on the resultant biological/immunological outcome. The underlying mechanisms of how the particles-based vaccine functioned in the immune system are also discussed. Based on the knowledge, particles with high antigen payload and optimized attributes could be designed for expected adjuvant purpose, leading to the development of high efficient vaccine candidates.
Journal Article
Temporal and spatial correlation patterns of air pollutants in Chinese cities
As a huge threat to the public health, China's air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants' hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O3, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O3 is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM10 and O3, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants' evolutionary process, but also shed lights on the application of complex network methods into geographic issues.
Journal Article