The correlation between fruit intake and all-cause mortality in hypertensive patients: a 10-year follow-up study.

Objective: Extensive research has consistently shown the beneficial impact of fruit consumption on overall health. While some studies have proposed a potential association between fruit consumption and hypertension management, the influence of fruit consumption on mortality rates among hypertensive individuals remains uncertain. Consequently, aim of this study is to evaluate whether fruit consumption is associated with all-cause mortality among hypertensive patients. Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES), conducted between 2003 and 2006. Ten-year follow-up data from the National Death Index (NDI) were used to assess all-cause mortality. Cox proportional hazard model was utilized to explore the impact of fruit intake on all-cause mortality among hypertensive individuals. Results: The study included a cohort of 2,480 patients diagnosed with hypertension, and during the follow-up period, a total of 658 deaths from various causes were recorded. The COX regression analysis demonstrated that hypertensive patients who consumed apples three to six times per week exhibited a significantly reduced risk of all-cause mortality (HR = 0.60, 95%CI: 0.45-0.78, p < 0.001) in comparison to those who consumed apples less than once per month. Likewise, consuming bananas three to six times per week also led to a comparable outcome (HR = 0.76, 95%CI: 0.59-0.97, p = 0.027). Moreover, Combined consumption of bananas and apples three to six times per week exhibited a noteworthy decrease in all-cause mortality (HR = 0.57, 95%CI: 0.39-0.84, p = 0.005) when compared to individuals who consumed these fruits less frequently. Conversely, no significant association was found between the consumption of other fruits, including pears, pineapples, and grapes, and all-cause mortality. Conclusion: The study discovered that moderate consumption of apples and bananas was associated with a reduced risk of all-cause mortality in patients with hypertension.

MiR-4454 in Combined Allergic Rhinitis and Asthma Syndrome (CARAS): Clinical significance and mechanistic insights into airway inflammation.

Abnormal expression of non-coding microRNA is associated with the development of combined allergic rhinitis and asthma syndrome (CARAS). However, the function of miR-4454 in CARAS is unknown. Our study aimed to reveal the clinical significance and related mechanism of miR-4454 in CARAS. Blood samples from 38 cases of CARAS and 43 cases of healthy subjects were collected to detect the expression of miR-4454. House dust mite (HDM) sensitization and challenge-induced bronchial epithelial cells to simulate the asthma state model in vitro, miR-4454 mimics and inhibitor transfection to detect the expression level of pro-inflammatory cytokines, cell survival rate and migration ability, flow cytometry and western blot (WB) Detection of cell cycle, apoptosis and inflammation-related protein levels. Compared with healthy controls, the expression of miR-4454 in the blood of CARAS patients was significantly up-regulated, and IL-6 and IL-8 were significantly up-regulated in the HDM treatment group, indicating that the model induction was successful. After overexpression of miR-4454, cell proliferation and migration in the HDM-treated group were significantly inhibited, and the levels of early apoptosis and inflammation-related proteins (IL-17, IL-17RD, TNF-α, GCSF and NF-κB) were increased High; after inhibiting miR-4454, cell proliferation and migration were significantly enhanced, and the levels of apoptosis and inflammation-related proteins were decreased. This study found that inhibiting the expression of miR-4454 can improve HDM-induced cell injury, which may be related to miR-4454 regulating the activation of IL-17/NF-кB inflammatory axis.

Circ_0070934 promotes MGAT3 expression and inhibits epithelial-mesenchymal transition in bronchial epithelial cells by sponging miR-199a-5p.

BACKGROUND: Circular RNA (circRNA) has the potential to serve as a crucial regulator in the progression of bronchial asthma. The objective of this investigation was to elucidate the functional dynamics of the circ_0070934/miR-199a-5p/Mannoside acetylglucosaminyltransferase 3 (MGAT3) axis in the development of asthma. METHODS: Circ_0070934, miR-199a-5p and MGAT3 in peripheral venous blood of 38 asthmatic patients and 43 healthy controls were detected by qRT-PCR, and the expression of MGAT3 protein was examined by ELISA. The GSE148000 dataset was analyzed for differences in MGAT3. The BEAS-2B cells were transfected with circ_0070934 plasmid and small interfering RNA, miR-199a-5p mimics and inhibitors. The apoptosis level was detected by flow cytometry and MGAT3 was detected by qRT-PCR and Western blot. The expression of E-cadherin, N-cadherin, Vimentin was examined by Western blot. Interleukin-4 (IL-4) and IL-13 were used to co-stimulate BEAS-2B cells as an asthmatic airway epithelial cell model. BEAS-2B cells exposed to type 2 cytokines (IL-4 and IL-13) were treated with circ_0070934 plasmid, and the expression of E-cadherin, N-cadherin, and Vimentin was detected by Western blot. The binding relationships were verified using dual-luciferase reporter assay and miRNA pull-down assay. RESULTS: The expression of circ_0070934 and MGAT3 in peripheral venous blood of asthmatic patients was down-regulated, and the expression of miR-199a-5p was up-regulated. And the expression of MGAT3 was reduced in sputum of asthma patients. Down-regulating the expression of circ_0070934 could promote apoptosis of BEAS-2B cells and increase epithelial-mesenchymal transition (EMT), and this effect can be partially reversed by down-regulating miR-199a-5p. Circ_0070934 could inhibit the process of epithelial mesenchymal transition induced by IL-4 and IL-13 in BEAS-2B cells. In addition, miR-199a-5p could respectively bind to circ_0070934 and MGAT3. CONCLUSION: The findings of this study indicate that circ_0070934 may function as a competitive endogenous RNA (ceRNA) of miR-199a-5p, thereby modulating the expression of MGAT3 and impacting the process of EMT in bronchial epithelial cells. These results contribute to the establishment of a theoretical framework for advancing the prevention and treatment strategies for asthma.

Liquid Metal Loaded Molecular Sieve: Specialized Lithium Dendrite Blocking Filler for Polymeric Solid-State Electrolyte.

All-solid-state lithium metal batteries (LMBs) are currently one of the best candidates for realizing the yearning high-energy-density batteries with high safety. However, even polyethylene oxide (PEO), the most popular polymeric solid-state electrolyte (SSE) with the largest ionic conductivity in the category so far, has significant challenges due to the safety issues of lithium dendrites, and the insufficient ionic conductivity. Herein, molecular sieve (MS) is integrated into the PEO as an inert filler with the liquid metal (LM) as a functional module, forming an "LM-MS-PEO" composite as both SSE with enhanced ionic conductivity, and protection layer against lithium dendrites. As demonstrated by theoretical and experimental investigations, LM released from MS can be uniformly and efficiently distributed in PEO, which could avoid agglomeration, enable the effective blocking of lithium dendrites, and regulate the mass transport of Li ions, thus achieving even deposition of lithium during charge/discharge. Moreover, MS could reduce the crystallinity of PEO, improve lithium-ion conductivity, and reduce operating temperature. Benefiting from the introduction of the functional MS/LM, the LM-MS-PEO electrolyte exhibits fourfold higher lithium ionic conductivity than the pristine PEO at 40 °C, while the as-assembled all-solid-state LMBs have four to five times longer stable cycle life.

Thioacetamide Additive Homogenizing Zn Deposition Revealed by In Situ Digital Holography for Advanced Zn Ion Batteries.

Zinc ion batteries are considered as potential energy storage devices due to their advantages of low-cost, high-safety, and high theoretical capacity. However, dendrite growth and chemical corrosion occurring on Zn anode limit their commercialization. These problems can be tackled through the optimization of the electrolyte. However, the screening of electrolyte additives using normal electrochemical methods is time-consuming and labor-intensive. Herein, a fast and simple method based on the digital holography is developed. It can realize the in situ monitoring of electrode/electrolyte interface and provide direct information concerning ion concentration evolution of the diffusion layer. It is effective and time-saving in estimating the homogeneity of the deposition layer and predicting the tendency of dendrite growth, thus able to value the applicability of electrolyte additives. The feasibility of this method is further validated by the forecast and evaluation of thioacetamide additive. Based on systematic characterization, it is proved that the introduction of thioacetamide can not only regulate the interficial ion flux to induce dendrite-free Zn deposition, but also construct adsorption molecule layers to inhibit side reactions of Zn anode. Being easy to operate, capable of in situ observation, and able to endure harsh conditions, digital holography method will be a promising approach for the interfacial investigation of other battery systems.

Association of PFDeA exposure with hypertension (NHANES, 2013-2018).

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) is a series of artificial compounds which is associated with human health. However, there are few studies on the relationship between PFASs and hypertension. In this study, we examined the association between different kinds of PFASs and hypertension. Multivariable logistic regression and subgroup analysis were adopted to assess the associations between PFASs and hypertension. Spline smoothing plots and linear regression were used to assess the relationship between PFASs and blood pressure. We found a positive association between serum PFDeA concentrations and the prevalence of hypertension after fully adjusting confounders (OR = 1.2, P = 0.01), but other types of PFASs showed no positive results. Subgroup analysis stratified by ethnicity showed there was a stronger relationship among non-Hispanics than Hispanics. Serum PFDeA concentrations were positively associated with systolic pressure (ß = 0.7, P< 0.01) and diastolic blood pressure (ß = 0.8, P< 0.01) among non-Hispanics who did not take antihypertensive drugs. This study showed that PFDeA exposure was associated with hypertension in Americans who identify as non-Hispanic. There was a positive association between PFDeA and blood pressure in non-Hispanic Americans who did not take antihypertensive drugs.

Filter-Informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis.

Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also been introduced in the field of fault diagnosis with the goal to make better use of the inductive bias of the interdependencies between the different sensor measurements. However, there are some limitations with these GNN-based fault diagnosis methods. First, they lack the ability to realize multiscale feature extraction due to the fixed receptive field of GNNs. Second, they eventually encounter the over-smoothing problem with increase of model depth. Finally, the extracted features of these GNNs are hard to understand due to the black-box nature of GNNs. To address these issues, a filter-informed spectral graph wavelet network (SGWN) is proposed in this article. In SGWN, the spectral graph wavelet convolutional (SGWConv) layer is established upon the spectral graph wavelet transform, which can decompose a graph signal into scaling function coefficients and spectral graph wavelet coefficients. With the help of SGWConv, SGWN is able to prevent the over-smoothing problem caused by long-range low-pass filtering, by simultaneously extracting low-pass and band-pass features. Furthermore, to speed up the computation of SGWN, the scaling kernel function and graph wavelet kernel function in SGWConv are approximated by the Chebyshev polynomials. The effectiveness of the proposed SGWN is evaluated on the collected solenoid valve dataset and aero-engine intershaft bearing dataset. The experimental results show that SGWN can outperform the comparative methods in both diagnostic accuracy and the ability to prevent over-smoothing. Moreover, its extracted features are also interpretable with domain knowledge.

Application of metagenomic next-generation sequencing (mNGS) in diagnosing pneumonia of adults.

INTRODUCTION: Accurate identification of pathogens that cause pulmonary infections is essential for effective treatment and hastening recovery in adults diagnosed with pneumonia. At present, despite metagenomic next-generation sequencing (mNGS) technology has been widely used in clinical practice for pathogen identification, the clinical significance and necessity of detecting pathogen in bronchoalveolar lavage fluid (BALF) for pneumonia-stricken adults remain ambiguous. METHODOLOGY: In this study, 80 patients suffering from pulmonary infection were enrolled, who were admitted to the Affiliated Changzhou Second People's Hospital of Nanjing Medical University between January 2020 and September 2022. The diagnostic performances of mNGS and conventional methods (CM) were systematically analyzed based on BALF samples, and we further investigated the influence of mNGS and CM in diagnosis modification and treatment. RESULTS: We found a significantly higher positive rate for the mNGS method in contrast to CM. Bacteria were the most common pathogens, and Streptococcus pneumoniae was the most commonly identified pathogen. Candida albicans and Epstein-Barr virus were the most frequently identified fungus and virus. Atypical pathogens such as Mycobacterium tuberculosis, virus Nontuberculous mycobacteria, and Chlamydia psittaci were also identified. A total of 77 patients were identified with mixed infections by mNGS. As the disease progressed and recurrent antibiotic treatment persisted, significant dynamic changes in the clinical manifestation from the BALF samples could be found by mNGS. CONCLUSIONS: This study underscores the efficacy of mNGS in detecting pathogens in BALF samples from patients suffering pulmonary infections. Compared with the CM, mNGS significantly enhanced the positive diagnosis ratio, particularly in diagnosing Mycobacterium tuberculosis, atypical pathogens, and viral or fungal infections.

D-D and PLR, NLR combined forecasting first-line treatment: The short-term curative effect and prognosis of patients with SCLC.

The aim of this study was to evaluate the difference in D-dimer (D-D) combined with the platelet lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) before treatment in small cell lung cancer (SCLC) patients receiving first-line treatment and to analyze the efficacy and prognosis. We retrospectively collected the records of SCLC patients treated in our hospital from February 2019 to January 2023 and finally included 100 patients. A binary logistic regression analysis method was applied to analyze the relationship between D-D, PLR, and NLR and short-term efficacy. Univariate and multivariate Cox regression analyses were utilized to estimate the individual effect of plasma parameters on progression-free survival (PFS). The optimal cutoff values of D-D, PLR, and NLR for predicting survival outcome were determined by receiver operating characteristic curve analysis. Kaplan-Meier survival analysis was utilized to examine the correlation between D-D, PLR, and NLR the prognosis of SCLC patients. PLR was associated with a short-term curative effect in patients with SCLC (odds ratio: 0.326, 95% confidence interval [CI]: 0.135 0.790). Univariate Cox regression showed that D-D (hazard ratio [HR]: 0.495, 95% CI: 0.323-0.758), PLR (HR:0.420, 95% CI: 0.269-0.655) and NLR (HR: 0.407, 95% CI: 0.263-0.630) were associated with PFS in SCLC patients (P < .05). Multivariate Cox regression analysis showed that PLR (HR: 2.395, 95% CI: 1.468-3.906) and NLR (HR: 2.148, 95% CI: 1.319-3.499) correlated significantly with PFS (P < .05). The optimal cutoff values of D-D, PLR and NLR for predicting PFS were 0.88 mg/L (65.4% and 68.7%), 195.44 (61.5% and 81.2%) and 3.63 (63.5% and 81.2%), respectively, and the corresponding area under receiver (AUC) operating characteristic curve 0.691 (95% CI: 0.587-0.795), 0.721 (95% CI: 0.620-0.822) and 0.714 (95% CI: 0.614-0.815). When D-D was used in combination with PLR or NLR, the corresponding AUCs were 0.737 (95% CI: 0.640-0.835) and 0.761 (95% CI: 0.667-0.855). Pretreatment PLR is an independent predictor of short-term efficacy in SCLC patients. Pretreatment D-D, PLR and NLR are potential biochemical markers for predicting the prognosis of SCLC patients treated with first-line treatment. When D-D is combined with PLR and NLR, it shows stronger predictive ability.

Practicable Zn metal batteries enabled by ultrastable ferromagnetic interface.

Rechargeable zinc (Zn) metal batteries (RZMBs) are demonstrated as sustainable and low-cost alternative in the energy storage industry of the future. However, the elusive Zn deposition behavior and water-originated parasitic reactions bring significant challenges to the fabrication and commercialization of Zn anodes, especially under high plating/stripping capacity. In this work, the ferromagnetic interface in conjunction with the magnetic field (MF) to effectively address these fabrication hurdles is proposed. The introduction of ferromagnetic layer with high chemical durability not only maintains the long-term regulating deposition steadily by magnetic field, but also plays a significant role in preventing side reactions, hence reducing gas production. These merits allow Zn-anode to achieve over 350 h steady Zn-deposition with a depth of discharge (DODZn) up to 82% and translates well to ZnFe-MF||V2O5 full cells, supporting stable cycling at high mass loading of 13.1 mg/cm2, which makes RZMBs configurations promising for commercial applications.

WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis.

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the poor interpretability and noise robustness of DL-based methods are still the main factors limiting their wide application in industry. To address these issues, an interpretable wavelet packet kernel-constrained convolutional network (WPConvNet) is proposed for noise-robust fault diagnosis, which combines the feature extraction ability of wavelet bases and the learning ability of convolutional kernels together. First, the wavelet packet convolutional (WPConv) layer is proposed, and constraints are imposed to convolutional kernels, so that each convolution layer is a learnable discrete wavelet transform. Second, a soft threshold activation is proposed to reduce the noise component in feature maps, whose threshold is adaptively learned by estimating the standard deviation of noise. Third, we link the cascaded convolutional structure of convolutional neutral network (CNN) with wavelet packet decomposition and reconstruction using Mallat algorithm, which is interpretable in model architecture. Extensive experiments are carried out on two bearing fault datasets, and the results show that the proposed architecture outperforms other diagnosis models in terms of interpretability and noise robustness.

DUSP10 alleviates ischemic stroke-induced neuronal damage by restricting p38/JNK pathway.

Neuronal apoptosis is considered one of the hallmarks of ischemic stroke. Dual specificity phosphatase 10 (DUSP10), a member of the dual-specificity phosphatase family, which is involved in the regulation of apoptosis process. This study aimed to investigate the effect of on apoptosis in primary cortical neurons exposed to oxygen-glucose deprivation and reoxygenation (OGD/R) and mice suffered from transient middle cerebral artery occlusion and reperfusion (MCAO/R). The results showed that DUSP10 overexpression improved survival and reduced apoptosis in neurons subjected to OGD/R, which was manifested by decreased apoptotic proteins (cleaved caspase 3 and bax) and TUNEL+ cells, as well as increased the anti-apoptotic protein (bcl-2). DUSP10 overexpression inhibited the p38/JNK signaling pathway after OGD/R treatment, whilst DUSP10 knockdown had opposite effects. In addition, the p38 inhibitor SB203580 or JNK inhibitor SP600125 attenuated the increased apoptosis of OGD/R-stimulated neurons treated with DUSP10 silencing. Consistently, DUSP10 knockdown exacerbated infarct volume in MCAO/R injury. The data of Nissl staining and TUNEL-NeuN double staining revealed that DUSP10 interference aggravated neuronal damage in the ischemic penumbra of mice. Furthermore, DUSP10 inhibition activated the p38/JNK axis accompanied by enhanced phosphorylation of p38 and JNK in vivo. In summary, DUSP10 is a neuroprotective agent against ischemic stroke-induced neuronal damage via suppressing the p38/JNK signaling pathway.

Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump.

Most current data-driven prognosis approaches suffer from their uncontrollable and unexplainable properties. To address this issue, this article proposes a physics-constraint variational neural network (PCVNN) for wear state assessment of the external gear pump. First, a response model of the pressure pulsation of the gear pump is constructed via a spectral method, and a compound neural network is utilized to extract features from the pressure pulsation signal. Then, the response model is formulated into an objective function to softly constrain the learning process of the neural network, forcing the learned features to have explicit physics meaning. Meanwhile, to characterize the system uncertainty, the variational inference is utilized to extend a Kullback-Leibler ( KL) divergence into the objective function. Finally, the wear state is evaluated based on the distance of learned physics features. Experimental results on an external gear pump validate the merits of the proposed method in explainable representation learning and system uncertainty estimation. It also offers a controllable and explainable perspective to understand the dynamic behavior of the system.

Ultrastable Zinc Anode Enabled by CO2-Induced Interface Layer.

Aqueous Zn-ion batteries (AZIBs), being safe, inexpensive, and pollution-free, are a promising candidate for future large-scale sustainable energy storage. However, in a conventional AZIBs setup, the Zn metal anode suffers oxidative corrosion, side reactions with electrolytes, disordered dendrite growth during operation, and consequently low efficiency and short lifespan. In this work, we discover that purging CO2 gas into the electrolyte could address these issues by eliminating dissolved O2, inhibiting side reactions by buffering the local pH change, and preventing dendrite growth by inducing the in situ formation of a ZnCO3 solid electrolyte interphase layer. Moreover, the CO2-purged electrolyte could enable a highly reversible plating/stripping behavior with a high Coulombic efficiency of 99.97% and an ultralong lifespan of 32,000 cycles (1600 h) even under an ultrahigh current density of 40 mA cm-2. Consequently, the CO2-purged symmetrical cells deliver long cycling stability at a high depth of discharge of 57%, while the CO2-purged Zn/V2O5 full cells exhibit outstanding capacity retention of 66% after 1000 cycles at a high current density of 5 A g-1. Our strategy, the simple introduction of CO2 gas into the electrolyte, could effectively mediate the zinc anode's critical issues and provide a scalable and cost-effective pathway for the commercialization of AZIBs.

Variational Attention-Based Interpretable Transformer Network for Rotary Machine Fault Diagnosis.

Deep learning technology provides a promising approach for rotary machine fault diagnosis (RMFD), where vibration signals are commonly utilized as input of a deep network model to reveal the internal state of machinery. However, most existing methods fail to mine association relationships within signals. Unlike deep neural networks, transformer networks are capable of capturing association relationships through the global self-attention mechanism to enhance feature representations from vibration signals. Despite this, transformer networks cannot explicitly establish the causal association between signal patterns and fault types, resulting in poor interpretability. To tackle these problems, an interpretable deep learning model named the variational attention-based transformer network (VATN) is proposed for RMFD. VATN is improved from transformer encoder to mine the association relationships within signals. To embed the prior knowledge of the fault type, which can be recognized based on several key features of vibration signals, a sparse constraint is designed for attention weights. Variational inference is employed to force attention weights to samples from Dirichlet distributions, and Laplace approximation is applied to realize reparameterization. Finally, two experimental studies conducted on bevel gear and bearing datasets demonstrate the effectiveness of VATN to other comparison methods, and the heat map of attention weights illustrates the causal association between fault types and signal patterns.

Diagnostic Value of Multislice Spiral Computed Tomography Combined with Serum AFP, TSGF, and GP73 Assay in the Diagnosis of Primary Liver Cancer.

Objective: To explore the diagnostic value of multislice spiral computed tomography (MSCT) scan combined with serum alpha-fetoprotein (AFP), tumor-specific growth factor (TSGF), and Golgi protein73 (GP73) assays in the diagnosis of primary liver cancer (PLC). Methods: Totally, 60 patients with PLC admitted to The Second Hospital of Dalian Medical University from January 2019 to January 2020 were included in group A, 60 patients with liver cirrhosis were included in group B, and 60 healthy subjects were included in group C. The serum AFP, TSGF, and GP73 levels were determined, and all participants received MSCT scanning. The diagnostic efficacy of MSCT, assays of serum AFP, TSGF, and GP73, and their combined detection was analyzed. Results: Group A had the highest levels of AFP, TSGF, and GP73, followed by group B, and then group C. The sensitivity, specificity, positive predictive value, and negative predictive value of MSCT for PLC were 80.0%,91.7%, 82.8%, and 90.2%, respectively, while those of combined detection of MSCT plus serum AFP, TSGF, and GP73 for PLC were 100.0%, 93.3%, 88.2%, and 100.0%. The combined detection was associated with significantly a higher detection rate of PLC versus stand-alone detection. Conclusion: MSCT plus serum AFP, TSGF, and GP73 has a higher detection rate versus stand-alone detection, which shows great potential in the diagnosis of PLC.

The Protective Effect of Kaempferol Against Ischemia/Reperfusion Injury Through Activating SIRT3 to Inhibit Oxidative Stress

Abstract Introduction: The objective of this study is to investigate the protective effect of kaempferol against ischemia/reperfusion (IR) injury and the underlying molecular mechanisms. Methods: H9C2 cells were pretreated with kaempferol for 24 hours and further insulted with IR injury. Cell vitality, reactive oxygen species (ROS) level, glutathione (GSH) level, nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity, and sirtuin-3 (SIRT3), B-cell lymphoma 2 (Bcl2), and Bcl2-associated X protein (Bax) expressions were evaluated. Moreover, short interfering ribonucleic acid targeting SIRT3 was used to investigate the role of SIRT3 against IR mediated by kaempferol in vitro. IR mice models were also established to confirm the protective effects of kaempferol on IR in vivo. Results: After IR injury, H9C2 cells vitality was reduced, ROS levels, NADPH oxidase activity, and Bax expressions were increased, and GSH levels and Bcl2 expressions were decreased. After kaempferol pretreatment, the vitality of H9C2 cells was increased. The levels of ROS, NADPH oxidase activity, and Bax expression were decreased. In addition, levels of GSH and Bcl2 expression were enhanced. Furthermore, silencing SIRT3 attenuated the protective effect mediated by kaempferol, with increased ROS levels, NADPH oxidase activity, and Bax expression, along with reduced GSH level and Bcl2 expression. In vivo IR model showed that kaempferol could preserve IR-damaged cardiac function. Conclusion: Kaempferol has the capability of attenuating H9C2 cells IR injury through activating SIRT3 to inhibit oxidative stress.

Cyclohexanedodecol-Assisted Interfacial Engineering for Robust and High-Performance Zinc Metal Anode.

Aqueous zinc-ion batteries (AZIBs) can be one of the most promising electrochemical energy storage devices for being non-flammable, low-cost, and sustainable. However, the challenges of AZIBs, including dendrite growth, hydrogen evolution, corrosion, and passivation of zinc anode during charging and discharging processes, must be overcome to achieve high cycling performance and stability in practical applications. In this work, we utilize a dual-functional organic additive cyclohexanedodecol (CHD) to firstly establish [Zn(H2O)5(CHD)]2+ complex ion in an aqueous Zn electrolyte and secondly build a robust protection layer on the Zn surface to overcome these dilemmas. Systematic experiments and theoretical calculations are carried out to interpret the working mechanism of CHD. At a very low concentration of 0.1 mg mL-1 CHD, long-term reversible Zn plating/stripping could be achieved up to 2200 h at 2 mA cm-2, 1000 h at 5 mA cm-2, and 650 h at 10 mA cm-2 at the fixed capacity of 1 mAh cm-2. When matched with V2O5 cathode, the resultant AZIBs full cell with the CHD-modified electrolyte presents a high capacity of 175 mAh g-1 with the capacity retention of 92% after 2000 cycles under 2 A g-1. Such a performance could enable the commercialization of AZIBs for applications in grid energy storage and industrial energy storage.

Investigation of the support constraint effect and failure instability law of tunnels constructed using the New Austrian tunneling method.

The application of a reasonable numerical calculation method is the key to accurately analyzing tunnel rock-support interactions. In this paper, we address the support constraint effect of tunnels and analyze the influence of related factors based on the confinement convergence method. Rupturable support models are developed using FLAC3D to intuitively show the numerical calculation results of tunnels. The results imply that the virtual supporting force generated by the support constraint effect should be considered in two-dimensional rock tunnel model calculations, and that the supporting force of the support should be increased by 2-3% of the maximum supporting force. Boundary effects should be considered in the three-dimensional tunnel model calculations, in which the influence range of the model boundary effect is nearly 1.5 times the tunnel span. A comparison of the field monitoring data and numerical calculations of the Baoshan tunnel project shows that the numerical results that consider the support constraint effect are in better agreement with the actual project situation. The rupturable support models can also reflect the stress and failure evolution law of supports, and provide support for the accurate evaluation of tunnel engineering stability.