No abstract

Pulmonary fibrosis is characterized by lung fibroblast proliferation and collagen secretion. In lipopolysaccharide (LPS)-induced acute lung injury (ALI), aberrant proliferation of lung fibroblasts is initiated in early disease stages, but the underlying mechanism remains unknown. In this study, we knocked down Toll-like receptor 4 (TLR4) expression in cultured mouse lung fibroblasts using TLR4-siRNA-lentivirus in order to investigate the effects of LPS challenge on lung fibroblast proliferation, phosphoinositide3-kinase (PI3K)-Akt pathway activation, and phosphatase and tensin homolog (PTEN) expression. Lung fibroblast proliferation, detected by BrdU assay, was unaffected by 1 mug/mL LPS challenge up to 24 hours, but at 72 hours, cell proliferation increased significantly. This proliferation was inhibited by siRNA-mediated TLR4 knockdown or treatment with the PI3K inhibitor, Ly294002. In addition, siRNA-mediated knockdown of TLR4 inhibited the LPS-induced up-regulation of TLR4, down-regulation of PTEN, and activation of the PI3K-Akt pathway (overexpression of phospho-Akt) at 72 hours, as detected by real-time PCR and Western blot analysis. Treatment with the PTEN inhibitor, bpV(phen), led to activation of the PI3K-Akt pathway. Neither the baseline expression nor LPS-induced down-regulation of PTEN in lung fibroblasts was influenced by PI3K activation state. PTEN inhibition was sufficient to exert the LPS effect on lung fibroblast proliferation, and PI3K-Akt pathway inhibition could reverse this process. Collectively, these results indicate that LPS can promote lung fibroblast proliferation via a TLR4 signaling mechanism that involves PTEN expression down-regulation and PI3K-Akt pathway activation. Moreover, PI3K-Akt pathway activation is a downstream effect of PTEN inhibition and plays a critical role in lung fibroblast proliferation. This mechanism could contribute to, and possibly accelerate, pulmonary fibrosis in the early stages of ALI/ARDS.

We report a novel strategy of simultaneous in situ extraction and fabrication of surface-enhanced Raman scattering substrate (IE-SERS) to perform selective and reliable on-site determination of thiram residue in soil, fruits, and vegetables. In this protocol, the thiram residue on complex surfaces can facilely diffuse into the solvent (dichloromethane (DCM)) and specifically bind to gold nanoparticles (AuNPs), affording the SERS substrate through the embedding of the thiram-trapped AuNPs into the cellulose p-toluenesulfonates (CTSAs) film through the evaporation of DCM. SERS signals of the specifically prepared CTSAs could be used as an internal standard to calibrate the absolute signal of thiram, which can avoid the fluctuation of SERS intensities caused by uneven and irregular morphology of SERS substrate. Thus, reliable quantitation of thiram through SERS detection and superior reproducibility in the SERS measurement (RSD = 4.21%) were achieved. As for directly sensing the thiram residue in soil, the established method shows strong anti-interference ability and a good linear response from 0.1 to 12 μg/g with a low limit of detection (LOD) of 50 ng/g, which is lower than that of all the previously reported methods. The recoveries range from 91.76 to 112.3% for thiram in paddy soils, indicating that the established IE-SERS method is reliable and applicable to the detection of thiram residue in real soil samples. In addition, the measurement of the residual thiram on strawberry and cucumber surface was also successfully accomplished by this strategy, indicating that the established method also has great potential in the in situ ultrasensitive detection of thiram on irregular fruits and vegetables.

No abstract

Surface-enhanced Raman-scattering-based (SERS-based) biosensing in biological fluids is constrained by nonspecific macromolecule adsorptions and disposable property of the SERS substrate. Here, novel multi-Au-nanoparticle-embedded mesoporous silica microspheres (AuNPs/mSiO) were prepared using a one-pot method, which served as reliable substrates for SERS enhancement associated with salient features of self-filtering ability and reusability. The fabrication and physical characterization of AuNPs/mSiO microspheres were discussed, and SERS activity of this novel substrate was investigated by using 4-mercaptobenzoic acid (4-MBA) as Raman probe. The responses of our substrates to Raman intensities exhibited a SERS enhancement factor of 2.01 × 10 and high reproducibility (relative standard deviation of 6.13%). Proof-of-concept experiments were designed to evaluate the self-filtering ability of the substrates in bovine serum albumin (BSA) and human serum solution, separately. The results clearly demonstrate that mesoporous SiO can serve as a molecular sieve via size exclusion and avoid Raman signal interference of biomacromolecules in biological fluids. Subsequently, feasibility of practical application of AuNPs/mSiO microspheres was assessed by quantitative detection of methotrexate (MTA) in serum. The method exhibited good linearity between 1 and 110 nM with the correlation coefficients of 0.996, which proved that the obtained AuNPs/mSiO microspheres were good SERS substrates for determination of small biomolecules directly in biological fluids without need of manipulating samples. In addition, the substrate maintained its SERS response during multiple cycles, which was evaluated by recording Raman signals for 4-MBA before and after thermal annealing, thereby demonstrating the high thermostability and satisfactory reusability. These results offered the AuNPs/mSiO microspheres attractive advantages in their SERS biosensing.

Cancer clinical trials are routinely designed to assess the effect of treatment on disease progression and death, often in terms of a composite endpoint called progression‐free survival. When progression status is known only at periodic assessment times, the progression time is interval censored, and complications arise in the analysis of progression‐free survival. Despite the advances in methods for dealing with interval‐censored data, naive methods such as right‐endpoint imputation are widely adopted in this setting. We examine the asymptotic and empirical properties of estimators of the marginal progression‐free survival functions and associated treatment effects under this scheme. Specifically, we explore the determinants of the asymptotic bias and point out that there is typically a loss in power of tests for treatment effects. Copyright © 2015 John Wiley & Sons, Ltd.

SummaryCohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods that do not distinguish between these reasons for missingness typically provide inference about a hypothetical cohort where no one can die (immortal cohort). It has been suggested that inference about the cohort composed of those who are still alive at any time point (partly conditional inference) may be more meaningful. MI, LI, and IPW can all be adapted to provide partly conditional inference. In this article, we clarify and compare the assumptions required by these MI, LI, and IPW methods for partly conditional inference on continuous outcomes. We also propose augmented IPW estimators for making partly conditional inference. These are more efficient than IPW estimators and more robust to model misspecification. Our simulation studies show that the methods give approximately unbiased estimates of partly conditional estimands when their assumptions are met, but may be biased otherwise. We illustrate the application of the missing data methods using data from the ‘Origins of Variance in the Old–old’ Twin study.

Not applicable.