Increased Levels of C1q in the prefrontal cortex of adult offspring after maternal immune activation: Prevention by 7,8-dihydroxyflavone

Objective: Prenatal infection is implicated in the etiology of schizophrenia. The objective of this paper is to study the role of complement protein C1q in the psychosis of adult offspring after maternal immune activation (MIA). In addition, effect of 7,8-dihydroxyflavone (7,8-DHF: A tropomyosin receptor kinase B [TrkB] agonist) was also examined. Methods: Western blot analysis of C1q in the brain regions from adult offspring after prenatal poly(I:C) (5.0 mg/kg/day from E12 to E17) exposure was performed. 7,8-DHF or vehicle was given from 4 to 8-weeks old. Results: Expression of C1q in the prefrontal cortex (PFC) of adult offspring from poly(I:C)-Treated pregnant mice was significantly higher than that of control group. Early treatment with 7,8-DHF during juvenile and adolescent stages could prevent an increase of C1q in the PFC of adult offspring after MIA. Conclusion: Therefore, it is likely that increased C1q expression in the frontal cortex may play a role in the behavioral abnormalities of adult offspring after MIA. Furthermore, supplementation with a TrkB agonist such as 7,8-DHF during the prodromal stage may have prophylactic effects on the behavioral abnormalities after MIA

Resistance imparted by vitamin C, vitamin e and vitamin B12 to the acute hepatic glycogen change in rats caused by noise.

The effects of vitamin C, vitamin E and vitamin B12 on the noise-induced acute change in hepatic glycogen content in rats were investigated. The exposure of rats to 95 dB and 110 dB of noise acutely reduced their hepatic glycogens. Vitamin C (ascorbic acid) and vitamin E (alpha -tocopherol) attenuated the noise-inducedacute reduction in the hepatic glycogen contents. This result suggests that antioxidants could reduce the change via reactive oxygen species. Vitamin B12 (cobalamin) delayed the noiseinduced change, a finding that suggests that vitamin B12 could postpone the acute change via compensating for vitamin B12 deficiency

Development of Oral Fast-Disintegrating Levothyroxine Films for Management of Hypothyroidism in Pediatrics

Purpose: To develop fast disintegrating films of levothyroxine (LVX) using hydrophilic hydroxypropyl methylcellulose (HPMC), croscarmellose sodium (CCS) as superdisintegrant, and propylene glycol (PG) as a plasticizer.Methods: Fast-disintegrating films were formulated by solvent casting evaporation method using 3-factor, 2-level full factorial design. The films were evaluated for disintegration time, in vitro drug release, physical appearance, thickness, weight variation, folding endurance, drug content uniformity, The effect of concentration of dependent variables (HPMC, CCS and PG) on disintegration time and in vitro drug release was studied.Results: Suitable fast-disintegrating films were obtained using HPMC, CCS and PG. The minimum disintegration time (DT) was 15 s and maximum drug release (DR) in 1 h was 97.56 %. All independent variables selected for the study were statistically significant (p < 0.5). Increase in the concentrations of independent variables increased DR and DT.Conclusion: The findings of this study indicate that the developed fast-disintegrating film is suitable for levothyroxine in the management of hypothyroidism in pediatrics.Keywords: Hypothyroidism, Superdisintegrant, Fast-disintegrating, Levothyroxine, Oral films, Hydroxypropyl methylcellulose, Croscarmelose sodium, Propylene glyco

Measurement-device-independent QKD with Modified Coherent State

The measurement-device-independent quantum key distribution (MDI-QKD) protocol has been proposed for the purpose of removing the detector side channel attacks. Due to the multi-photon events of coherent states sources, real-life implementations of MDI-QKD protocol must employ decoy states to beat the photon-number-splitting attack. Decoy states for MDI-QKD based on the weak coherent states have been studied recently. In this paper, we propose to perform MDI-QKD protocol with modified coherent states (MCS) sources. We simulate the performance of MDI-QKD with the decoy states based on MCS sources. And our simulation indicates that both the secure-key rate and transmission distance can be improved evidently with MCS sources.The physics behind this improvement is that the probability of multi-photon events of the MCS is lower than that of weak coherent states while at the same time the probability of single-photon is higher

Column-Spatial Correction Network for Remote Sensing Image Destriping

The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments