Double network hydrogels (DN gels) are considered as one of the toughest soft materials. However, conventional chemically linked DN gels often lack high self-recovery and fatigue resistance ...properties due to permanent damage of covalent bonds upon deformation. Current strategies to improve self-recovery and fatigue resistance properties of tough DN gels mainly focus on the manipulation of the first network structure. In this work, we proposed a new design strategy to synthesize a new type of Agar/PAMAAc-Fe3+ DN gels, consisting of an agar gel as the first physical network and a PAMAAc-Fe3+ gel as the second chemical–physical network. By introducing Fe3+ ions into the second network to form strong coordination interactions, at optimal conditions, Agar/PAMAAc-Fe3+ DN gels can achieve extremely high mechanical properties (σ f of ∼8 MPa, E of ∼8.8 MPa, and W of ∼16.7 MJ/m3), fast self-recovery (∼50% toughness recovery after 1 min of resting), and good fatigue resistance compared to properties of cyclic loadings by simply controlling acrylic acid (AAc) content in the second network. The high toughness and fast recovery of Agar/PAMAAc-Fe3+ DN gel is mainly attributed to energy dissipation through reversible noncovalent bonds in both networks (i.e., hydrogen bonds in the agar network and Fe3+ coordination interactions in the PAMAAc network). The time-dependent recovery of Agar/PAMAAc-Fe3+ gels at room temperature and the absence of recovery in Agar/PAMAAc gels also confirm the important role of Fe3+ coordination interactions in mechanical strength, self-recovery, and fatigue resistance of DN gels. Different mechanistic models were proposed to elucidate the mechanical behaviors of different agar-based DN gels. Our results offer a new design strategy to improve strength, self-recovery, and fatigue resistance of DN gels by controlling the structures and interactions in the second network. We hope that this work will provide an alterative view for the design of tough hydrogels with desirable properties.
Transition metal chalcogenides are promising hydrogen evolution reaction (HER) electrocatalysts; however, their performance still needs to be improved to meet the requirements of practical ...applications. Herein, an interface-engineering strategy is exploited to prepare efficient transition metal chalcogenide based HER catalysts in alkaline medium. Specifically, the heterogeneous nanostructure of 1D core-shell Te@NiTe2 and 2D NiS (Te@NiTe2/NiS) has been fabricated by an easy one-step solvothermal method. The HER activity of Te@NiTe2/NiS is optimized by adjusting the ratio of raw materials and reaction time. Under the optimum conditions, the Te@NiTe2/NiS displays higher HER activity than individual Te@NiTe2 and NiS in alkaline medium and the reasons are investigated in detail. To improve the HER activity of Te@NiTe2/NiS, the Te@NiTe2/NiS is further combined with conductive acetylene black (AB) by physically mixing. The resulting Te@NiTe2/NiS/AB has a low overpotential at 10 mA/cm2 of 101 mV, higher current density than 20% Pt/C when the overpotential is larger than 0.37 V and good long-term stability, indicating its practicability as HER electrocatalysts. This work presents the validity of interface-engineering strategy in preparing highly efficient transition metal chalcogenides based HER electrocatalysts.
Display omitted
•Heterogeneous Te@NiTe2/NiS was firstly designed based on an interface engineering strategy.•Te@NiTe2/NiS was prepared by a facile one-step solvothermal method.•Te@NiTe2/NiS displays high HER activity and long-term stability in alkaline medium.•The HER activity of Te@NiTe2/NiS/AB outperforms that of commercial Pt/C at high current density.
The response of AMP-activated protein kinase (AMPK) to oxidative stress has been recently reported but the downstream signals of this response are largely unknown. Meanwhile, the upstream events for ...the activation of nuclear factor erythroid-2-related factor-2 (Nrf2), a critical transcriptional activator for antioxidative responses, remain unclear. In the present study, we investigated the relationship between AMPK and Nrf2 signal pathways in lipopolysaccharide (LPS)-triggered inflammatory system, in which berberine (BBR), a known AMPK activator, was used for inflammation suppression.
In inflammatory macrophages, BBR attenuated LPS-induced expression of inflammatory genes (inducible nitric oxide synthase iNOS, cyclooxygenase-2 COX2, interleukin IL-6), and the generation of nitric oxide and reactive oxygen species, but increased the transcription of Nrf2-targeted antioxidative genes (NADPH quinone oxidoreductase-1 NQO-1, heme oxygenase-1 HO-1), as well as the nuclear localization and phosphorylation of Nrf2 protein. Importantly, we found BBR-induced activation of Nrf2 is AMPK-dependent, as either pharmacologically or genetically inactivating AMPK blocked the activation of Nrf2. Consistent with in vitro experiments, BBR down-regulated the expression of proinflammatory genes but upregulated those of Nrf2-targeted genes in lungs of LPS-injected mice, and these effects were attenuated in Nrf2-deficient mice. Moreover, the effect of BBR on survival time extension and plasma redox regulation in endotoxin-shocked mice was largely weakened when Nrf2-depleted.
Our results demonstrate convergence between AMPK and Nrf2 pathways and this intersection is essential for anti-inflammatory effect of BBR in LPS-stimulated macrophages and endotoxin-shocked mice. Uncovering this intersection is significant for understanding the relationship between energy homeostasis and antioxidative responses and may be beneficial for developing new therapeutic strategies against inflammatory diseases. Antioxid. Redox Signal. 20, 574-588.
Combining both chemical and physical cross-links in a double-network hydrogel (DN gel) has emerged as a promising design strategy to obtain highly mechanically strong hydrogels. Unlike chemically ...cross-linked DN gels, little is known about the fracture process and toughening mechanisms of hybrid chemically physically linked DN gels. In this work, we engineered tough hybrid DN gels of agar/polyacrylamide (Agar/PAAm) by combining two types of cross-linked polymer networks: a physically linked, first agar network and a chemical-linked, second PAAm network. The resulting Agar/PAAm exhibited high stiffness of 313 kPa and high toughness of 1089 J/m2. We then specifically examined the effect of the first agar network on the mechanical properties of hybrid Agar/PAAm gels. We found that by controlling agar concentrations above a critical value, the physically linked agar network can simultaneously enhance both stiffness and toughness of Agar/PAAm DN gels, as evidenced by a linear relationship of elastic modulus and tearing energies of the gels as the increase of agar concentration. This toughening behavior is different from that of chemically linked DN gels. Complement to chemically linked DN gels, this work provides a different view for the design of new stiff and tough hydrogels using hybrid physical and chemical networks.
Venenum Bufonis, a well-known traditional Chinese medicine, has been widely used in Asia and has gained popularity in Western countries over the last decade. Venenum Bufonis has obvious side effects ...that have been observed in clinical settings, but few studies have reported on its cardiotoxicity. In this work, the cardiotoxicity of Venenum Bufonis was investigated using a 11H NMR-based metabolomics approach. The 1H NMR profiles of the serum, myocardial extracts and liver extracts of specific-pathogen-free rats showed that Venenum Bufonis produced significant metabolic perturbations dose-dependently with a distinct time effect, peaking at 2 hr after dosing and attenuating gradually. Clinical chemistry, electrocardiographic recordings, and histopathological evaluation provided additional evidence of Venenum Bufonis-induced cardiac damage that complemented and supported the metabolomics findings. The combined results demonstrated that oxidative stress, mitochondrial dysfunction, and energy metabolism perturbations were associated with the cardiac damage that results from Venenum Bufonis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Normal somatic cells do not divide indefinitely and have their finite replicative lifespan. This property leads to an eventual arrest of cell division termed cell senescence. Human diploid ...fibroblasts offer a typical model for studying cell senescence in vitro. Various approaches to evoke oxidative stresses, such as the exposures of cells to ultraviolet light, ethanol, tert-butyl hydroperoxide (t-BHP), and peroxide hydrogen (H2O2), have been used to study the onset of cellular senescence. The early onset of cellular senescence induced by these stresses is termed stress-induced premature senescence (SIPS). In this manuscript, we will mainly summarize the basic knowledge and experimental approaches important for the induction of SIPS by H2O2, since H2O2 is the most commonly used inducer of SIPS in vitro and an endogenous source of cellular oxidative stress. Several assays methods generally used for testifying cell senescence are introduced.
Epoxides, as a prominent small ring O-heterocyclic and the privileged pharmacophores for medicinal chemistry, have recently represented an ideal substrate for the development of single-atom ...replacements. The previous O-to-C replacement strategy for epoxides to date typically requires high temperatures to achieve low yields and lacks substrate range and functional group tolerance, so achieving this oxygen-carbon exchange remains a formidable challenge. Here, we report a silver-catalyzed direct conversion of epoxides into trifluoromethylcyclopropanes in a single step using trifluoromethyl N-triftosylhydrazones as carbene precursors, thereby achieving oxygen-carbon exchange via a tandem deoxygenation/2 + 1 cycloaddition. The reaction shows broad tolerance of functional groups, allowing routine cheletropic olefin synthesis in a strategy for the net oxygen-carbon exchange reaction. The utility of this method is further showcased with the late-stage diversification of epoxides derived from bioactive natural products and drugs. Mechanistic experiments and DFT calculations elucidate the reaction mechanism and the origin of the chemo- and stereoselectivity.
•A goldfish model was established to investigate the toxicity of lambda-cyhalothrin (LCT) exposure on multiple organs.•NMR based metabolomics approach were firstly used to provide a global view of ...the toxicity of LCT.•LCT induced neurotransmitters and osmoregulatory imbalances, oxidative stress, energy and amino acid metabolic disorders.•Glutamate–glutamine–GABA axis as a potential target for LCT toxicity was first found.
In this study, a 1H nuclear magnetic resonance (NMR) based metabolomics approach was applied to investigate the toxicity of lambda-cyhalothrin (LCT) in goldfish (Carassius auratus). LCT showed tissue-specific damage to gill, heart, liver and kidney tissues of goldfish. NMR profiling combined with statistical methods such as orthogonal partial least squares discriminant analysis (OPLS-DA) and two-dimensional statistical total correlation spectroscopy (2D-STOCSY) was developed to discern metabolite changes occurring after one week LCT exposure in brain, heart and kidney tissues of goldfish. LCT exposure influenced levels of many metabolites (e.g., leucine, isoleucine and valine in brain and kidney; lactate in brain, heart and kidney; alanine in brain and kidney; choline in brain, heart and kidney; taurine in brain, heart and kidney; N-acetylaspartate in brain; myo-inositol in brain; phosphocreatine in brain and heart; 2-oxoglutarate in brain; cis-aconitate in brain, and etc.), and broke the balance of neurotransmitters and osmoregulators, evoked oxidative stress, disturbed metabolisms of energy and amino acids. The implication of glutamate–glutamine–gamma-aminobutyric axis in LCT induced toxicity was demonstrated for the first time. Our findings demonstrated the applicability and potential of metabolomics approach for the elucidation of toxicological effects of pesticides and the underlying mechanisms, and the discovery of biomarkers for pesticide pollution in aquatic environment.
Cities are beginning to monitor atmospheric carbon dioxide (CO2) to assess the efficacy of their climate policies. However, changes in anthropogenic CO2 emissions must be separated from biospheric ...CO2 fluxes which have a large seasonal cycle. Urban vegetation (e.g. lawns, trees along street and in parks, etc) in developed land covers is often omitted in regional biogenic CO2 flux models. We set up a biosphere model to estimate the regional biogenic CO2 fluxes in New York City (NYC) and assess the importance of vegetation within developed land covers. The model incorporates a high-resolution (30 m) land cover map which identifies the mixture of impervious surfaces and vegetation that is ubiquitous across developed land covers. We designed three model scenarios to evaluate the role of developed land covers in regional biogenic CO2 fluxes by assuming (a) there is no vegetation versus scenarios where all remotely sensed vegetation in developed land covers is either (b) grassland or (c) deciduous forest. Despite relatively low tree canopy cover in NYC, the regional biogenic CO2 fluxes are surprisingly large when vegetation within the developed land covers is included. Furthermore, the types of vegetation within the developed land covers are crucially important for estimating regional biogenic CO2 fluxes, demonstrated by a doubling in estimates of total biogenic CO2 flux when this vegetation is assumed to be grassland compared to forest. Using a Lagrangian atmospheric transport model, we find that the regional biogenic CO2 uptake offsets up to 40% of atmospheric CO2 enhancements attributed to anthropogenic emissions in summer afternoons and completely balances on-road traffic in one of the most congested cities in the United States. Accurate characterization of the vegetation and biogenic carbon fluxes in cities are essential to the development of effective atmospheric monitoring tools. Future measurements should focus on constraining CO2 fluxes in urban grasslands (i.e. lawns).
Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by ...China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697).
PDF
