Systemic sclerosis (SSc) is characterized by excessive collagen deposition in the skin and internal organs. Activated fibroblasts are the key effector cells for the overproduction of type I collagen, which comprises the α1(I) and α2(I) chains encoded by COL1A1 and COL1A2, respectively. In this study, we examined the expression patterns of α1(I) and α2(I) collagen in SSc fibroblasts, as well as their co-regulation with each other. The relative expression ratio of COL1A1 to COL1A2 in SSc fibroblasts was significantly higher than that in control fibroblasts. The same result was observed for type I collagen protein levels, indicating that α2(I) collagen is more elevated than α2(I) collagen. Inhibition or overexpression of α1(I) collagen in control fibroblasts affected the α2(I) collagen levels, suggesting that α1(I) collagen might act as an upstream regulator of α2(I) collagen. The local injection of COL1A1 small interfering RNA in a bleomycin-induced SSc mouse model was found to attenuate skin fibrosis. Overall, our data indicate that α2(I) collagen is a potent regulator of type I collagen in SSc; further investigations of the overall regulatory mechanisms of type I collagen may help understand the aberrant collagen metabolism in SSc. 相似文献
Diurnal variations of fossil secondary organic carbon (SOC) and nonfossil SOC were determined for the first time using a combination of several carbonaceous aerosol measurement techniques, including radiocarbon (1?C) determinations by accelerator mass spectrometry, and a receptor model (chemical mass balance, CMB) at a site downwind of Tokyo during the summer of 2007. Fossil SOC showed distinct diurnal variation with a maximum during daytime, whereas diurnal variation of nonfossil SOC was relatively small. This behavior was reproduced by a chemical transport model (CTM). However, the CTM underestimated the concentration of anthropogenic secondary organic aerosol (ASOA) by a factor of 4-7, suggesting that ASOA enhancement during daytime is not explained by production from volatile organic compounds that are traditionally considered major ASOA precursors. This result suggests that unidentified semivolatile organic compounds or multiphase chemistry may contribute largely to ASOA production. As our knowledge of production pathways of secondary organic aerosol (SOA) is still limited, diurnal variations of fossil and nonfossil SOC in our estimate give an important experimental constraint for future development of SOA models. 相似文献
Surface structures of iron–phosphate glasses were examined using X‐ray photoelectron spectroscopy (XPS). Cr2O3, CoO, and Al2O3 were introduced to the glass by the replacement of a part of Fe2O3, and the simulated fission products are also added. The obtained glasses showed high chemical durabilities by MCC‐1 test. In situ high‐temperature and room‐temperature XPS measurements were conducted on the polished sample surfaces and also those after 1‐week chemical durability test. Unique trends were observed in XPS spectra on heating and after the chemical durability test, respectively. Nature of the glass surface of iron–phosphate glasses was explained from the point of view of surface energy, and the origin of high chemical durability and the effect of chromium ions were discussed based on the changes on surface composition and valence states of transition‐metal ions. 相似文献
This study aimed to examine the feasibility of evaluating the stress level at the surface of lumber during drying using near-infrared (NIR) spectroscopy combined with artificial neural networks (ANNs). Sugi (Cryptomeria japonica D. Don) lumber with an initial moisture content ranging from 41.1 to 85.8% was dried using a commercial drying schedule. An ANN model for predicting surface-released strain (SRS) was developed based on NIR spectra collected from the lumber during drying. The predictive ability of the ANN model was compared with a partial least squares (PLS) regression model.
The ANN model showed good correlation between laboratory-measured SRS and predicted SRS with an R2 of 0.79, a root mean square error of prediction (RMSEP) of 0.0009, and a ratio of performance to deviation (RPD) of 1.81. The PLS regression model gave a lower R2 of 0.69, a higher RMSEP of 0.0010, and a lower RPD of 1.38 than the ANN model, suggesting that the predictive performance of the ANN model was superior to the PLS regression model. The SRS evolution during drying as predicted by the models showed a similar trend to the laboratory-measured one. The predicted elapsed times to reach maximum tensile SRS and stress reversal roughly coincided with the laboratory-measured times. These results suggest that NIR spectroscopy combined with multivariate analysis has the potential to predict the drying stress level on the lumber surface and the critical periods during drying, such as the points of maximum tensile stress and stress reversal. 相似文献