Transcriptomic Profiling of Virus-Host Cell Interactions following Chicken Anaemia Virus (CAV) Infection in an In Vivo Model.

Chicken Anaemia Virus (CAV) is an economically important virus that targets lymphoid and erythroblastoid progenitor cells leading to immunosuppression. This study aimed to investigate the interplay between viral infection and the host's immune response to better understand the pathways that lead to CAV-induced immunosuppression. To mimic vertical transmission of CAV in the absence of maternally-derived antibody, day-old chicks were infected and their responses measured at various time-points post-infection by qRT-PCR and gene expression microarrays. The kinetics of mRNA expression levels of signature cytokines of innate and adaptive immune responses were determined by qRT-PCR. The global gene expression profiles of mock-infected (control) and CAV-infected chickens at 14 dpi were also compared using a chicken immune-related 5K microarray. Although in the thymus there was evidence of induction of an innate immune response following CAV infection, this was limited in magnitude. There was little evidence of a Th1 adaptive immune response in any lymphoid tissue, as would normally be expected in response to viral infection. Most cytokines associated with Th1, Th2 or Treg subsets were down-regulated, except IL-2, IL-13, IL-10 and IFNγ, which were all up-regulated in thymus and bone marrow. From the microarray studies, genes that exhibited significant (greater than 1.5-fold, false discovery rate <0.05) changes in expression in thymus and bone marrow on CAV infection were mainly associated with T-cell receptor signalling, immune response, transcriptional regulation, intracellular signalling and regulation of apoptosis. Expression levels of a number of adaptor proteins, such as src-like adaptor protein (SLA), a negative regulator of T-cell receptor signalling and the transcription factor Special AT-rich Binding Protein 1 (SATB1), were significantly down-regulated by CAV infection, suggesting potential roles for these genes as regulators of viral infection or cell defence. These results extend our understanding of CAV-induced immunosuppression and suggest a global immune dysregulation following CAV infection

Preparation of Polyurethane/Clay Nanocomposites: Investigating the Dispersion of Organoclays in PTMEG

Good dispersion of nano-particles in polymer matrices is a crucial parameter in determining the final properties of polymer nanocomposites. In this work, the distributions of Cloisite 30B, Cloisite 10A and Cloisite 93A in PTMEG, popular polyols in the polyurethane manufacturing, were investigated in the nano, micro and macro scales. This investigation was carried out using X-ray diffraction, rheometry and visual observation. The X-ray results indicated that the distance between the layers increased after addition of the polymer but this increase was higher in case of Cloisite 30B than the two other nanoclays. The rheological measurements also showed that Cloisite 30B induces a stronger structure within the suspension. Visual observation of the various suspensions verified the above hypothesis

0444 Impact of CPAP versus Supplemental Oxygen on Cardiac Electophysiological Indices in Obstructive Sleep Apnea: The HeartBEAT study

Abstract Introduction It is unknown whether obstructive sleep apnea (OSA) treatment or reversal of OSA-related hypoxemia improves cardiac electrophysiology. We hypothesize heart rate variability (HRV) measured during sleep would improve more with CPAP compared to nocturnal supplemental oxygen (NSO). Methods Participants were enrolled in a multi-center randomized controlled trial (Heart Biomarker Elevation in Apnea Treatment study, NCT01086800) designed to compare effects of 3 months of NSO versus CPAP versus healthy lifestyle education (HLSE). Overnight ECGs were analyzed from level 3 home sleep studies. HRV was extracted from 5-minute epochs without ectopy or artifact, deriving frequency-based HRV indices [low frequency power (LFP), high frequency power (HFP) and LFP/HFP (LHR)] and time domain indices. Analysis of covariance was used to compare the change in HRV indices among groups adjusted for site, age, sex, race, body mass index (BMI) and cardiac medications. Logarithm transformation was used for baseline and 3-month follow-up HRV measures so that the absolute difference is represented by percentage change. Results The analytic sample (n=211) included participants with age 63 ± 7.4 years, 73% male, and BMI 33 ± 5.9kg/m2. From baseline to follow up, there was a 17% reduction in HFP [least square mean 0.83, 95% confidence interval:0.68–1.00] in the CPAP group versus 5% and 9% increase in NSO [1.05, 0.87–1.27] and HLSE [1.09, 0.89–1.33], p=0.021. Conversely, there was a 16% increase in LHR [1.16, 0.87–1.54] in CPAP compared to 19% reduction in both NSO [0.81, 0.61–1.07] and HLSE [0.81, 0.59–1.09]), p=0.036. No significant changes from baseline to follow up were observed between CPAP, NSO and HLSE for LFP or the secondary time domain HRV indices examined. Conclusion Changes in HRV indices were observed in CPAP compared to either NSO or HLSE. Changes are suggestive of altered sympathovagal balance after CPAP, but also may reflect changes in sino-atrial node electrophysiological function. Support (If Any) Funding: Supported by grants from the National Heart, Lung, and Blood Institute (RC2 HL101417, 1R01HL109493, and R21HL108226) and by a grant from the National Center for Research Resources (UL1 RR024989)

Comparison of the Prevalence of Microbial Contamination in Packed and Unpacked Redmeat and Chicken Meat at Retail Outlets and Department Stores in Southern Tehran

Introduction:Despite advances in disease prevention and food materials technology, food – borne diseases are still a major problem in both developed and developing countries. Morever, meat plays a key role in transfer of bacteria, especially “Zoonotic” to humans. Therefore, we decided to investigate the outbreak of pathogenic bacteria such as Salmonella,Campylobacter, Yersinia and Aeromonas in red meat and chicken offered as packed and unpacked in areas under the authority of Tehran university of medical sciences . Methods: 630 samples including 315 raw chicken meat and 315 raw red meat samples were collected and tested for a period of one year from July, 2004 to August,2005. Samples were collected from shops selling packed meat and chicken as well as shops selling unpacked meat and chicken in different parts of the south of Tehran The methods used for the laboratory investigation were based on Iranian National Standard Procedure No. 2394. Results: Of the 630 samples of chicken and meat, 183 samples (29 %) were contaminated. 49.2 percent of the contaminated samples were chicken meat and 8.9 percent were red meat. From the total, 71 samples were contaminated with salmonella (11.3 %), 68 samples with Campylobacter (10.8 %), 26 samples with Yersinia entrocolitica (4.1 %) and 18 samples with Aeromonas (2.9 %). In red meat samples, microbial contamination was observed in 4.9 % of packed and 10.3 percent of unpacked samples. Contamination rate of chicken samples was higher including 59.3 % of packed and 45.7 % of unpacked chicken samples. The observed difference between the remitting samples of packed and unpacked chicken was statistically significant. (P 0.05

Automated Detection of Postictal Generalized EEG Suppression

Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts