Search

Acute myeloid leukemia (AML) was confirmed to be associated with hematopoietic insufficiency, as well as abnormal proliferation, differentiation or survival of myeloid progenitors. Multiple studies reported that microRNA-204 (miR-204) and Hepatocyte growth factor (HGF) played important roles in types of cancers. However, the potential molecular regulatory mechanism between miR-204 and HGF in AML remains to be further defined. Real-time PCR (RT-PCR) was adopted to detect the expression of miR-204 and HG. Relative protein levels were detected by western blot assay. The viability, cell cycle, apoptosis, migration, and invasion were analyzed by MTT, flow cytometry, and transwell assays. Moreover, the target relationship between miR-204 and HGF was predicted by MiRcode website and confirmed by luciferase reporter, RNA pull-down, and western blot assays. Our data suggested that miR-204 was downregulated in AML serum samples and cells. MiR-204 overexpression repressed cell proliferation, migration, invasion, and induced cell apoptosis in AML cells. HGF was upregulated in AML samples and cells, and HGF knockdown inhibited the malignancy of AML cells. In addition, HGF was directly targeted by miR-204. HGF overexpression reversed the effects of miR-204 mimic on AML cell proliferation, apoptosis, migration, and invasion. Besides, miR-204 regulated the c-Met signaling by targeting HGF, thereby regulating the downstream protein levels related to cell proliferation, apoptosis, migration, and invasion in AML cells. In conclusion, miR-204 could regulate AML progression through regulating the HGF/c-Met pathway. [ABSTRACT FROM AUTHOR]

Multivariate output sensitivity analysis has gained much attention when the output of the computational model is a vector. A preferable strategy to deal with the multivariate output issue is the covariance decomposition approach based on the polynomial chaos expansion (PCE) metamodel. However, since the PCE construction depends on the quality of experimental design to some extent, the selection of design points is significant in determining the accuracy of the sensitivity estimator. In this article, a PCE-based sequential experimental design is proposed to estimate the multivariate output sensitivity index. In this method, the optimal design point is sequentially selected to minimize the determinant of covariance matrix of the sensitivity estimator. To validate the performance of the proposed method, several numerical examples are presented, which show that the sequential design approach performs better than other prevalent methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Many scientific studies collect data where the response and predictor variables are both functions of time, location, or some other covariate. Understanding the relationship between these functional variables is a common goal in these studies. Motivated from two real-life examples, we present in this article a function-on-function regression model that can be used to analyze such kind of functional data. Our estimator of the 2D coefficient function is the optimizer of a form of penalized least squares where the penalty enforces a certain level of smoothness on the estimator. Our first result is the representer theorem which states that the exact optimizer of the penalized least squares actually resides in a data-adaptive finite-dimensional subspace although the optimization problem is defined on a function space of infinite dimensions. This theorem then allows us an easy incorporation of the Gaussian quadrature into the optimization of the penalized least squares, which can be carried out through standard numerical procedures. We also show that our estimator achieves the minimax convergence rate in mean prediction under the framework of function-on-function regression. Extensive simulation studies demonstrate the numerical advantages of our method over the existing ones, where a sparse functional data extension is also introduced. The proposed method is then applied to our motivating examples of the benchmark Canadian weather data and a histone regulation study. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the parameter accuracy, a decomposition based multi-innovation recursive generalised least squares algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective. [ABSTRACT FROM AUTHOR]

To investigate the potential oxidative damages of Fe3O4 nanoparticles to organisms, Kunming mice were intraperitoneally administrated with Fe3O4 nanoparticles for seven days at the doses of 5, 10, 20 and 40 mg·kg−1, respectively. The contents of ROS (reactive oxygen species), GSH (glutathione) and MDA (malondialdehyde) in the lung homogenate were measured to indicate the oxidative damage. Results showed that the contents of ROS and MDA increased gradually while GSH content decreased with the increase in exposure dose, and all the studied biomarkers changed in dose-response manners. When exposure dose exceeded 10 mg·kg−1, ROS content was significantly higher than that of the control group (p < 0.05); when exposure dose was over 20 mg·kg−1, MDA content was significantly higher than that of the control group (p < 0.05); when exposure dose was higher than 40 mg·kg−1, GSH content turned out to have significant difference compared with the control group (p < 0.05). It is demonstrated that Fe3O4 nanoparticles can increase the oxidative stress and cause oxidative damage in mouse lung at relatively high dose (≥20 mg·kg−1). [ABSTRACT FROM AUTHOR]

The current parameterization and algorithm used to fit a smoothing spline analysis of variance (SSANOVA) model are computationally expensive, making a generalized additive model (GAM) the preferred method for multivariate smoothing. In this article, we propose an efficient reparameterization of the smoothing parameters in SSANOVA models, and a scalable algorithm for estimating multiple smoothing parameters in SSANOVAs. To validate our approach, we present two simulation studies comparing our reparameterization and algorithm to implementations of SSANOVAs and GAMs that are currently available in R. Our simulation results demonstrate that (a) our scalable SSANOVA algorithm outperforms the currently used SSANOVA algorithm, and (b) SSANOVAs can be a fast and reliable alternative to GAMs. We also provide an example with oceanographic data that demonstrates the practical advantage of our SSANOVA framework. Supplementary materials that are available online can be used to replicate the analyses in this article. [ABSTRACT FROM PUBLISHER]

In the present study, The PEI/DNA (PD) complexes was first prepared with positive surface charge under a suitable N/P ratio of 10. The redundant positive charge was partially and excessively shielded by a polysaccharide, hyaluronic acid (HA), in aqueous solution. The HA/PEI/DNA ternary complexes were characterized by assessing the zeta potential and size, then transferred to MDA-MB-435, MDA-MB-231, and MCF-7 cell lines with different amounts of HA-specific CD44 receptors on the surface. Consequently, The transfection efficiency of all the prepared complexes show a little increased to MCF-7 (low CD44 level) while a large increased to MDA-MB-231 and MDA-MB-435 cells (high CD44 level) with adding HA. Also, when HA:PEI charge ratio was 7.5%, the ternary complexes show the highest transfection efficiency. The prepared ternary complexes exhibited increased 2–13-fold fluorescence intensity and lower cell toxicity compared to the PD (N/P, 10). These results indicated that the positive HA/PEI/DNA ternary complexes (HA:PEI charge ratio, 7.5%) can target malignant breast cancer cells with high CD44 level and might be a promising candidate vector for gene therapy. [ABSTRACT FROM AUTHOR]

Regression models with a functional response and functional covariate have received significant attention recently. While various nonparametric and semiparametric models have been developed, there is an urgent need for model selection and diagnostic methods. In this article, we develop a unified framework for estimation and model selection in nonparametric function-on-function regression. We propose a general nonparametric functional regression model with the model space constructed through smoothing spline analysis of variance (SS ANOVA). The proposed model reduces to some of the existing models when selected components in the SS ANOVA decomposition are eliminated. We propose new estimation procedures under either L1 or L2 penalty and show that the combination of the SS ANOVA decomposition and L1 penalty provides powerful tools for model selection and diagnostics. We establish consistency and convergence rates for estimates of the regression function and each component in its decomposition under both the L1 and L2 penalties. Simulation studies and real examples show that the proposed methods perform well. Technical details and additional simulation results are available in online . [ABSTRACT FROM AUTHOR]

Generalized linear mixed-effect models are widely used for the analysis of correlated non-Gaussian data such as those found in longitudinal studies. In this article, we consider extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method, and our focus is on the efficient computation and the effective smoothing parameter selection. To assist efficient computation, the joint likelihood of the observations and the latent variables of the random effects is used instead of the marginal likelihood of the observations. For the selection of smoothing parameters and correlation parameters, direct cross-validation techniques are employed; the effectiveness of cross-validation with respect to a few loss functions are evaluated through simulation studies. Real data examples are presented to illustrate potential applications of the methodology. Open-source R code is demonstrated in the Appendix. [ABSTRACT FROM AUTHOR]

Populations of Octoblepharum albidum growing in very moist and shaded habitats were found to have three kinds of asexual reproduction. First, the production of foliar gemmae along the upper half of leaf margins. Second, the production of protonema gemmae on protonemata originated from foliar gemmae. Third, the production of buds and new shoots at leaf tips. The formation of new shoots is further repeated at the leaf tips of the new shoots, giving the plant the character of a 'walking moss'. [ABSTRACT FROM AUTHOR]

AbstractMuscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields. [ABSTRACT FROM AUTHOR]

When and how do international political arrangements change? International relations scholars have long examined the nature of shifts in international alliance and cooperation networks, often arguing that times of disruption in the international system are the most precarious for peace. In this research note, we rely on innovations in change-point and community detection methods to endogenously examine the timing and nature of shifts in country-to-country relationships through defense cooperative agreements. Using new methodical innovations from network science, we can see how countries move through different communities over time, changing the nature of polarity in the system. This empirical approach can help provide insights into determinants of peace, vulnerabilities in the international system, and potential aggressors in world politics. [ABSTRACT FROM AUTHOR]

Latin hypercube design (LHD) is a multi-stratified sampling method, which has been frequently used in sampling-based analysis. To achieve good space-filling quality of LHD, an efficient method, termed local search-based genetic algorithm (LSGA), is proposed in this article for constructing an optimal LHD. LSGA adopts modified order crossover, probabilistic mutation and adaptive selection operators to enrich population diversity and speed up convergence. A local search strategy is also presented in the approach to enhance the search ability. The performance of the proposed method is compared with several established methods in three perspectives, namely space-filling quality, computational efficiency and predictive accuracy of the metamodel. Several numerical experiments with distinct dimensions and numbers of design points are studied, and the results demonstrate that the proposed method performs better than other methods when dealing with LHD construction issues with high dimension and a large number of sampling points. [ABSTRACT FROM AUTHOR]

The hierarchical validation is recommended for assessing the confidence of the large-scale simulation model, through comparing the model predictions with experimental observations at each level. However, the system-level experimental observations may be scarce in the full-scale testing. A new structural modelling approach is proposed to implement the higher-level model validation using lower-level data. The proposed approach relates the computation model to corresponding system responses and lower-level outputs to higher-level responses. Bayesian network with Markov chain Monte Carlo (MCMC) sampling is used to represent the relationship sets and estimate the structural modelling parameters and input variables. Highest posterior density (HPD) confidence range is employed to quantify the model credibility, the confidence intervals of model errors, and the effect of lower-level data on the higher-level model assessment. The proposed methodology is implemented for hierarchical model validation and confidence extrapolation of a guidance model on a flight vehicle using time series data. [ABSTRACT FROM AUTHOR]

An abstract of the article "Structure of Ba3Y2(BO3)4 Crystal" by Ping Ma et al. is presented.

An abstract of the article "Spectroscopic Properties of Nd3+:Ba3Y2(BO3)4 Crystal" by Ping Ma et al. is presented.

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this article, we study model-free subsampling methods, which aim to identify a subsample, that is, not confined by model assumptions. Existing model-free subsampling methods are usually built upon clustering techniques or kernel tricks. Most of these methods suffer from either a large computational burden or a theoretical weakness. In particular, the theoretical weakness is that the empirical distribution of the selected subsample may not necessarily converge to the population distribution. Such computational and theoretical limitations hinder the broad applicability of model-free subsampling methods in practice. We propose a novel model-free subsampling method by using optimal transport techniques. Moreover, we develop an efficient subsampling algorithm, that is, adaptive to the unknown probability density function. Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator. We also provide the optimal bandwidth for the proposed estimator. Numerical studies on synthetic and real-world datasets demonstrate the performance of the proposed method is superior. [ABSTRACT FROM AUTHOR]

For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least-square estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this article, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression. We first establish consistency and asymptotic normality of the estimator from a general subsampling algorithm, and then derive optimal subsampling probabilities that minimize the asymptotic mean squared error of the resultant estimator. An alternative minimization criterion is also proposed to further reduce the computational cost. The optimal subsampling probabilities depend on the full data estimate, so we develop a two-step algorithm to approximate the optimal subsampling procedure. This algorithm is computationally efficient and has a significant reduction in computing time compared to the full data approach. Consistency and asymptotic normality of the estimator from a two-step algorithm are also established. Synthetic and real datasets are used to evaluate the practical performance of the proposed method. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O (n 3) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O (n q 2) . These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly distributed on a hypercube. These methods may have deteriorating performance when such an assumption is not met. To overcome the obstacle, we develop an efficient algorithm that is adaptive to the unknown probability density function of the predictors. Theoretically, we show the proposed estimator has the same convergence rate as the full-basis estimator when q is roughly at the order of O [ n 2 d / { (p r + 1) (d + 2) } ] , where p ∈ [ 1 , 2 ] and r ≈ 4 are some constants depend on the type of the spline. Numerical studies on various synthetic datasets demonstrate the superior performance of the proposed estimator in comparison with mainstream competitors. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

We aimed to investigate the expression levels of GABA and GABAA receptor α1 and α2 subunits in patients with major depressive disorder (MDD) during onset and remission. 48 patients with MDD during onset and 45 patients with MDD during remission who were treated in our university were selected. Moreover, the control group included 46 healthy volunteers recruited in the community. The depression and anxiety symptoms were assessed by Hamilton Depression (HAMD) Scale and Hamilton Anxiety (HAMA) Scale. ELISA was used to determine the serum GABA levels. The mRNA expression of GABAA receptor α1 and α2 subunits in peripheral blood were detected by RT-PCR. The expression levels of serum GABA and of GABAA receptor α1 and α2 subunits in MDD depression attack group were notably decreased in comparison with those in MDD remission group and control group ((4.10 ± 0.73) v.s. (5.91 ± 1.25) and (5.83 ± 1.17) umol/L, F = 5.61, p < 0.001; (0.53 ± 0.32) v.s. (0.91 ± 0.18) and (0.93 ± 0.21), F = 8.37, p < 0.001; (1.45 ± 0.86) v.s. (2.33 ± 1.49) and (2.28 ± 1.50), F = 8.23, p < 0.001). However, there were no marked difference in the levels of these three indices between the MDD remission group and the control group (p > 0.05). Serum GABA levels were negatively correlated with HAMA total score (r = −0.34, p = 0.02), HAMD total score (r = −0.46, p = 0.01) and depression core symptom score (r = −0.32, p = 0.03). During the onset of MDD, there may be GABA neuronal dysfunction and abnormal expression of GABAA receptor subunits, and those changes showed a state change, which gradually returned to normal during remission. [ABSTRACT FROM AUTHOR]

We consider a measurement constrained supervised learning problem, that is, (i) full sample of the predictors are given; (ii) the response observations are unavailable and expensive to measure. Thus, it is ideal to select a subsample of predictor observations, measure the corresponding responses, and then fit the supervised learning model on the subsample of the predictors and responses. However, model fitting is a trial and error process, and a postulated model for the data could be misspecified. Our empirical studies demonstrate that most of the existing subsampling methods have unsatisfactory performances when the models are misspecified. In this paper, we develop a novel subsampling method, called "LowCon," which outperforms the competing methods when the working linear model is misspecified. Our method uses orthogonal Latin hypercube designs to achieve a robust estimation. We show that the proposed design-based estimator approximately minimizes the so-called worst-case bias with respect to many possible misspecification terms. Both the simulated and real-data analyses demonstrate the proposed estimator is more robust than several subsample least-squares estimators obtained by state-of-the-art subsampling methods. for this article are available online. [ABSTRACT FROM AUTHOR]

Polysaccharide of Taxus chinensis var. mairei Cheng et L.K.Fu (Taxaceae) (PTM) functions in anti-apoptosis and antioxidation, but its function on Alzheimer's disease (AD) remains unclear. To investigate the effect of PTM on AD. C57BL/6J mice were randomly divided into three groups: control, d-galactose (d-gal), and d-gal + PTM. AD-like symptom was induced by d-gal for 6 weeks, followed with PTM (0.4 g/kg/d) for 14 days. PTM was added to BV2 cells stimulated with d-gal (1, 10, 20, 50, 100 and 500 μg/mL). Cell viability was evaluated by MTT assay. The expression of NRF2, SOD, cleaved caspase-3, Bax and Bcl-2 were detected with Western blot analysis. Cognitive function was evaluated by Morris water maze test. Decreased cleaved caspase-3 (1.30 ± 0.09) and Bax/Bcl2 ratio (1.32 ± 0.11) were observed in BV2 cells induced by d-gal + PTM (50 μg/mL). Increased MDA and ROS and decreased SOD were observed in d-gal group. However, decreased MDA (175 ± 9 ng/mL) and ROS level (188 ± 38 ng/mL) were observed after treated with PTM group (p < 0.05). In addition, the expression of NRF2 decreased in d-gal group (0.75 ± 0.09) but increased after treated with PTM (p < 0.05). Furthermore, decreased Aβ1-42 was observed and the cognitive function was improved after PTM intervention (p < 0.05). This is the first report that PTM inhibited oxidative stress and apoptosis in AD. The result will further accelerate the applications of Taxus chinensis var. mairei and the treatment for AD. [ABSTRACT FROM AUTHOR]

Background: Adequate diagnosis of acute anterior ischaemic optic neuropathy (AION) is challenging to achieve with traditional medical imaging approaches. The aim of this study was to evaluate the detection capacity of diffusion tensor imaging for abnormalities of the optic nerve in acute AION patients.Methods: Diffusion tensor imaging data were collected from 31 patients with acute AION and 20 healthy subjects. The mean fractional anisotropy and apparent diffusion co-efficient subsequently were evaluated across all subjects.Results: In affected nerves, the mean fractional anisotropy was reduced and the mean apparent diffusion co-efficient was increased in acute AION patients compared with control nerves (p < 0.001), as well as clinically unaffected contralateral nerves (p < 0.001). The mean fractional anisotropy and apparent diffusion co-efficient demonstrated no difference between normal control nerves and clinically unaffected contralateral nerves (p = 0.73 and 0.92, respectively).Conclusion: Diffusion tensor imaging of the optic nerves demonstrated diagnostic potential for acute AION and could serve as a novel tool for the detection and evaluation of therapies. [ABSTRACT FROM AUTHOR]

The human glioma cell line U251 was incubated for 12 h and 24 h in medium containing dibutyl phthalate at concentrations of 25 µmol/L and 100 µmol/L. Decreased cell viability and increased oxidative stress show the cytotoxicity of dibutyl phthalate. Nuclear factor-κB and nuclear factor-κB pathway-related proteins were altered, which could be attenuated by vitamin E at 20 µmol/L. Thus, dibutyl phthalate induces cytotoxicity through activation of nuclear factor-κB and vitamin E exerts neuroprotection against dibutyl phthalate-mediated cytotoxicity in vitro. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a serious, common, global disease, yet its etiology and pathogenesis are incompletely understood. Although an association between AD and exposure to air pollutants has been discussed, the effects of pollutants on the functioning of the brain remain unclear. The indoor environment is where exposure to formaldehyde (FA) can occur. Whether exposure to FA contributes to the development of AD needs to be investigated. To determine the objective, C57BL/6 mice were exposed daily to FA (0, 0.155, 1.55 and 15.5 mg/kg/day) for 1 week. After acute FA exposure, some early AD-like changes [cognitive deficits, pathological alterations in the mouse brain, accumulation of total β-amyloid plaques 1-42 (Aβ1-42) and hyper-phosphorylated tau (Tau-P) in the cerebral cortex] were detected after exposure to high concentrations of FA (1.55 or 15.5 mg/kg/day). The permeability of the blood–brain barrier (BBB), activation of astrocyte and microglia, oxidative stress (OS) and inflammation were analyzed to explore the toxicity mechanisms behind the development of early AD-like changes. While exposed to a low concentration of FA (0.155 mg/kg/day) had little or no adverse effects on the mouse brain. The results indicated that acute FA exposure induced early AD-like changes in mouse brain, increased the susceptibility of AD in mouse. [ABSTRACT FROM PUBLISHER]

Background:Approximately 2 million doses of vaccine against haemorrhagic fever with renal syndrome (HFRS) have been used annually in China. However, there were limited studies focused on persistence of immune responses to HFRS vaccine in healthy adults. A phase 4, multicentre, open trial has been undertaken to assess antibody persistence after HFRS vaccination of healthy adolescents and adults aged 16–60 years. Methods:The vaccine was administered as a three-dose series at 0, 2 weeks and 6 months, including two primary doses and one booster dose. Anti-hantavirus IgG antibodies were measured by enzyme-linked immunosorbent test (ELISA) pre-vaccination and 1.5, 7 and 24 months after the initial vaccination. Results:A total of 143 individuals aged 16–60 years were included. The median OD (range) values of IgG antibody were 0.005 (0.004–0.016), 0.116 (0.036–0.620), 0.320 (0.065–0.848) and 0.128 (0.011–0.649) pre-vaccination and at 1 month after the two primary doses, 1 month after the booster dose and 18 months after the booster dose. The positivity rate was 7.7%, 40.6%, 62.2% and 48.2%, respectively. Conclusions:The two primary doses could help healthy individuals to generate an immune response, and this three-dose series may be better than a two-dose regimen. [ABSTRACT FROM AUTHOR]

The forms of mercury in the wet flue gas desulfurization outlet flue gases from two generating units of coal-fired power plants in Xinjiang, using the standard Ontario Hydro Method Calculations of the mercuryEfand the estimation of mercury emissions were performed based on the monitoring results, flue gas flow rates, coal consumption, and lower heating values (LHVs). The concentrations of HgP, Hg2+, Hg°, total gaseous mercury, and total mercury in the flue gases were 0.07–0.20, 0.13–0.65, 1.16–1.19, 1.29–1.84, and 1.49–1.91 μg/Nm3, respectively, all of which were far below the emission limit of 30 μg/Nm3specified in the national standard. The emission factors for HgP, Hg2+, Hg°, the total gaseous mercury, and the total mercury were 0.03–0.12, 0.08–0.23, 0.42–0.69, 0.65–0.76, and 0.68–0.89 mg/GJ, respectively. Mercury emissions were estimated by using the aforesaid data, and the results showed that the total mercury emissions from flue gases of power plants No. 1 and 2 were 14.5 and 19.4 kg/a, respectively. The total mercury concentrations in the flue gases from the coal-fired power plants in Xinjiang were much lower than those of coal-fired power plants in other regions of China, the United States, and Europe. [ABSTRACT FROM PUBLISHER]

BackgroundChina has the highest prevalence of hemorrhagic fever with renal syndrome (HFRS) and accounts for 90% of the total cases worldwide. However, the long-term persistence of anti-hantavirus antibodies in sera of patients with HFRS and subjects vaccinated against the disease remains unclear. The aim of the present study was to investigate the prevalence of anti-hantavirus IgG antibodies in sera of patients with prior HFRS, versus subjects vaccinated against the disease and controls in Shaanxi, China.MethodsSix hundred individuals were included in this study. We quantified anti-hantavirus IgG antibodies in HFRS patients (n = 100), vaccinees (n = 200), controls from endemic regions (n = 200), and controls from non-endemic regions (n = 100) in China.ResultsThe median optical density (OD) values (range) were 0.803 (0.008–1.813), 0.075 (0.004–1.565), 0.026 (0–1.179), and 0.015 (0.009–0.118) for HFRS patients, vaccinated subjects, endemic controls, and non-endemic controls, respectively. There was a strikingly significant difference between the HFRS group and each non-HFRS group (p < 0.001). The vaccinated subjects were also significantly different from the endemic controls. The time since the acute phase was correlated with the OD values of the HFRS patients.ConclusionsOur study suggests that HFRS patients gain long-lasting protection and that vaccination may be an effective way to stimulate antibody production. [ABSTRACT FROM PUBLISHER]

Formaldehyde (FA) is an environmental pollutant and an endogenous product believed to be involved in tumorigenesis. However, the underlying mechanism of observed FA effects has not been clearly defined. Paxillin is a focal adhesion protein that may play an important role in several signaling pathways. Many paxillin-interacting proteins are involved in the regulation of actin cytoskeleton organization, which is necessary for cell motility events associated with diverse biological responses, such as embryonic development, wound repair and tumor metastasis. P53 is important in multicellular organisms, where it regulates the cell cycle and thus functions as a tumor suppressor that is involved in preventing cancer. In this study, we investigated the effects of FA on paxillin–tyrosine phosphorylation and P53 expression in Hela cells by Western blot and immunofluorescence. Western blot analysis revealed that nonlethal concentrations of FA (0.5, 1.0 and 2.0 mM, with the exposure time for 0.5, 1.0 and 2.0 h, respectively) had downregulated paxillin and wild-type p53 genes expression while upregulated paxillin–tyrosine phosphorylation significantly. At the same time, phosphotyrosine at the focal adhesion sites detected by immunofluorescence assay obviously increased in Hela cells incubated with 2.0 mM FA for 2 h. The results suggested that paxillin and p53 genes expression may be involved in FA-related adverse effects and the mechanism may be involved in paxillin–tyrosine phosphorylation. [ABSTRACT FROM AUTHOR]