44% (n = 3).Exotic species especially Asteraceae plants severely invade wetlands in Shenzhen Bay, an important part of the coast wetland in Guangdong-Hong Kong-Macau Bay Area, China. However, the reasons causing their expansion are unclear. The leaf traits and expansion indices of six invasive Asteraceae plants from the Overseas Chinese Town (OCT) wetland were studied and the results showed that nearly 45% of the total plant species (31 out of 69 species) in the OCT wetland, belonging to 15 families and 27 genera, were exotic invasive species. The expansion indices of six Asteraceae species negatively correlated with their leaf construction cost based on mass (CCM), caloric values and carbon concentration, but their relations with ash content were positive. Multiple linear regression analysis revealed that CCM was the most important factor affecting the expansion of an exotic species, indicating CCM may be an important reason causing the expansion of exotic species in coastal wetlands.The concentrations of 37 polycyclic aromatic hydrocarbons (PAHs) and their potential risk to human health were determined in fifty sardine muscle (Sardinella brasiliensis) samples collected along the southern Brazilian shelf. Parental and alkylated PAHs were identified and quantified using a pressurized liquid extraction with in-cell purification method and gas chromatography-mass spectrometry identification and quantification. The concentrations of Σ37 PAHs in muscle ranged between 6.02 and 4074 μg kg-1 wet weight, which are comparable to levels reported for commercially important fish worldwide. The most abundant compounds were pyrene and fluoranthene, which originate from both petrogenic and pyrolytic hydrocarbon inputs. In only 4% of the samples the benzo[a] pyrene equivalent concentration was above the threshold of 6 μg kg-1 suggested for safe fish consumption in Brazil. These findings will serve as baseline data for monitoring the quality of sardines consumed in the country and for studying fish populations.A big challenge of the 21st century is to cope with the huge amounts of plastic waste on Earth. Especially the oceans are heavily polluted with plastics. To counteract this issue, biological (enzymatic) plastic decomposition is increasingly gaining attention. Recently it was shown that polyethylene terephthalate (PET) can be degraded in a saltwater-based environment using bacterial PETase produced by a marine diatom. At moderate temperatures, plastic biodegradation is slow and requires sensitive methods for detection, at least at initial stages. However, conventional methods for verifying the plastic degradation are either complex, expensive, time-consuming or they interfere with the degradation process. Here, we adapt lensless digital holographic microscopy (LDHM) as a new application for efficiently monitoring enzymatic degradation of a PET glycol copolymer (PETG). LDHM is a cost-effective, compact and sensitive optical method. We demonstrate enzymatic PETG degradation over a time course of 43 days employing numerical analysis of LDHM images.This study aimed to compare different methods to assess body fat (BF). We hypothesized that bioelectrical impedance analysis (BIA) or anthropometry may be used to estimate BF in prefrail older women, equivalently to dual-energy X-ray absorptiometry (DXA). The cross-sectional study included 72 prefrail community-dwelling older women (71.13 ± 4.65 years old; body mass index [BMI] 28.89 ± 4.23 kg/m2). The BF percentage (%BF) was estimated using anthropometry with the Durnin & Womersley (D&W) and Petroski's predictive equations, BIA with 2 Baumgartner predictive equations (BIA 1 and BIA 2), and DXA. All methods differed significantly from DXA according to assessments using repeated measures ANOVA and pairwise comparisons. The mean %BF varied between 39.99 ± 3.42% (D&W) and 43.93 ± 5.06% (DXA). Multiple regression analysis with age and BMI as covariates showed positive correlations (R2 = 0.91) in models with D&W equation and BMI, and with BIA 2 and BMI; however, BMI explained more of the model (71%) than the equations. Furthermore, Bland-Altman test revealed a proportional bias for D&W and for BIA 2, with underestimation of BF varying across different %BF values. Petroski's skinfold equation showed a positive correlation on linear regression (R2 = 0.74) and no proportional bias; however, Bland-Altman analysis revealed high limits of agreement (-13.6 to -0.05), thus compromising clinical application. To conclude, compared with DXA, all the equations tested showed a high disagreement and wide limits of agreement, restricting their use in clinical practice to estimate the BF in prefrail older women.
It remains unclear whether a specific chest CT characteristic is associated with the clinical severity of COVID-19. This meta-analysis was performed to assess the relationship between different chest CT features and severity of clinical presentation in COVID-19.
PubMed, Embase, Scopus, web of science databases (WOS), Cochrane library, and Google scholar were searched up to May 19, 2020 for observational studies that assessed the relationship of different chest CT manifestations and the severity of clinical presentation in COVID-19 infection. Risk of bias assessment was evaluated applying the Newcastle-Ottawa Scale. https://www.selleckchem.com/products/cepharanthine.html A random-effects model or fixed-effects model, as appropriately, were used to pool results. Heterogeneity was assessed using Forest plot, Cochran's Q test, and I2. Publication bias was assessed applying Egger's test.
A total of 18 studies involving 3323 patients were included. Bronchial wall thickening (OR 11.64, 95% CI 1.81-74.66) was more likely to be associated with severe cases of COVID-19 infection, followed by crazy paving (OR 7.60, 95% CI 3.82-15.14), linear opacity (OR 3.27, 95% CI 1.10-9.70), and GGO (OR 1.37, 95% CI 1.08-1.73). However, there was no significant association between the presence of consolidation and severity of clinical presentation (OR 2.33, 95% CI 0.85-6.36). Considering the lesion distribution bilateral lung involvement was more frequently associated with severe clinical presentation (OR 3.44, 95% CI 1.74-6.79).
Our meta-analysis of observational studies indicates some specific chest CT features are associated with clinical severity of COVID-19.
Our meta-analysis of observational studies indicates some specific chest CT features are associated with clinical severity of COVID-19.https://www.selleckchem.com/products/cepharanthine.html
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