Robersonmclean4550

No evidence exists concerning the effect of airborne particulate matter of 1 μm or less (PM1) on blood pressure of young adults planning for pregnancy. We collected health examination information of about 1.2 million couples (aged 18-45 years) from a national birth cohort in China from Jan 1, 2013 to Oct 1, 2014 and matched their home address to daily PM1 and PM2.5 concentrations, which were predicted by remote sensing information. Generalized additive mixed models were used to analyze associations between long-term exposure to PM and blood pressure, after controlling for individual factors. A 10 μg/m3 increase in PM1 was associated with increased systolic blood pressure (SBP) for 0.26 (95%CI 0.24, 0.29) mmHg in females and 0.29 (95%CI 0.26, 0.31) mmHg in males, respectively. PM1 was also associated with increased DBP for 0.22 (95%CI 0.20, 0.23) mmHg in females and 0.17 (95%CI 0.15, 0.19) mmHg in males, respectively. Similar effects on blood pressure were found for PM2.5, meanwhile, the effect of PM2.5 on SBP increased with the scale of PM1 included in PM2.5 (p for interaction term less then 0.01). In summary, long-term exposure to PM1 as well as PM2.5 was associated with increased SBP and DBP of Chinese young adults planning for pregnancy. BACKGROUND Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering. CONCLUSION In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering. Phthalocyanines have interesting optoelectronic properties but typically suffer from aggregation in aqueous solution, which can limit their applicability, especially in photodynamic therapy. In this study, indium(III) phthalocyanine peripherally substituted with eight triazolyl-containing phenoxy groups (InOAc) and its water-soluble analogue (Q-InOAc) were synthesised and structurally characterised. Bcl-2 inhibitor Heavy metal effects, exerted by the central indium ion, on the photosensitising and photophysical properties (singlet oxygen quantum yield, singlet state lifetime and quantum yield, and triplet state lifetime) were investigated in both DMF and D2O. Highly efficient generation of the triplet excited state (T1), induced by the incorporation of a large atom, enhanced singlet oxygen formation, as revealed by both chemical and physical methods. Correspondingly, the singlet oxygen quantum yield (ΦΔ) of Q-InOAc was 0.603 in DMF and 0.433 in D2O. These values are higher than those previously reported for the corresponding metal-free, Mg-based, and Zn-based water-soluble phthalocyanines (HH, Mg, and Zn). Consequently, Q-InOAc is expected to be an excellent photosensitiser for photodynamic therapy. In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitagliptin (STG) based on the aggregation of gold nanoparticles (AuNPs). The Morphology and size distribution of synthesized AuNPs before and after adding drug (Zipmet) were monitored using transmission electron microscopy (TEM) and dynamic light scattering (DLS), respectively. By adding a drug, the absorption peak was shifted from 520 to 650 nm. The colorimetric method along with partial least squares (PLS) as a multivariate calibration method, as well as neural network time series were applied to estimate MET and STG simultaneously. The percentage of the mean recovery and root mean square error (RMSE) of the test set of mixtures related to the MET and STG were obtained 99.96, 1.1301 and 99.77, 1.0106, respectively. On the other hand, the regression coefficient (R2) of the training, validation, and test sets corresponding to the artificial neural network (ANN) were close to one for both components. Eventually, the proposed method was compared with a reference technique named high-performance liquid chromatography (HPLC) by analysis of variance (ANOVA) test and there was no significant difference between them. BACKGROUND An early symptom of multiple sclerosis is unilateral weakness, particularly in the lower limbs, which is associated with strength asymmetries. The purpose of this exploratory study was to examine strength asymmetries at the hip, knee, and ankle joints, and to investigate the associations between lower limb strength asymmetries and self-reported fatigue severity and disability in people with multiple sclerosis. METHODS Sixteen mildly-disabled people with multiple sclerosis (females = 9) completed isokinetic maximal voluntary contractions of the hip extensors and flexors, knee extensors and flexors, and ankle plantar flexors and dorsiflexors. Asymmetry indices between the strength of the more- and less-affected lower limbs at each muscle group and the percent agreement between self-reported and objectively-determined more-affected lower limb were calculated. Patient Determined Diseases Steps and Fatigue Severity Scale were also completed. FINDINGS All joints showed asymmetry (asymmetry indices ≥10%).