Relationship between Azithromycin Susceptibility and Administration Efficacy for Nontypeable Haemophilus influenzae Respiratory Infection

Nontypeable Haemophilus influenzae (NTHI) is an opportunistic pathogen that is an important cause of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). COPD is an inflammatory disease of the airways, and exacerbations are acute inflammatory events superimposed on this background of chronic inflammation. Azithromycin (AZM) is a macrolide antibiotic with antibacterial and anti-inflammatory properties and a clinically proven potential for AECOPD prevention and management. Relationships between AZM efficacy and resistance by NTHI and between bactericidal and immunomodulatory effects on NTHI respiratory infection have not been addressed. In this study, we employed two pathogenic NTHI strains with different AZM susceptibilities (NTHI 375 [AZM susceptible] and NTHI 353 [AZM resistant]) to evaluate the prophylactic and therapeutic effects of AZM on the NTHI-host interplay. At the cellular level, AZM was bactericidal toward intracellular NTHI inside alveolar and bronchial epithelia and alveolar macrophages, and it enhanced NTHI phagocytosis by the latter cell type. These effects correlated with the strain MIC of AZM and the antibiotic dose. Additionally, the effect of AZM on NTHI infection was assessed in a mouse model of pulmonary infection. AZM showed both preventive and therapeutic efficacies by lowering NTHI 375 bacterial counts in lungs and bronchoalveolar lavage fluid (BALF) and by reducing histopathological inflammatory lesions in the upper and lower airways of mice. Conversely, AZM did not reduce bacterial loads in animals infected with NTHI 353, in which case a milder anti-inflammatory effect was also observed. Together, the results of this work link the bactericidal and anti-inflammatory effects of AZM and frame the efficacy of this antibiotic against NTHI respiratory infection.

The influence of microbial factors on the susceptibility of bacteria to photocatalytic destruction

The role that bacterial factors play in determining how bacteria respond to photocatalytic degradation is becoming increasingly recognised. Fimbriae which are thin, proteinaceous cell surface structures produced by many enterobacteria are generally considered to be important bacterial virulence determinants in the host. Recent studies, however, suggest that their expression may be increased during times of environmental stress to protect them against factors such as nutrient depletion and oxidation. In this study bacteria were grown under defined culture conditions to promote the expression of type 1 fimbriae and subjected to photocatalytic treatment. Results showed that Escherichia coli grown under conditions to express type 1 fimbriae were more resistant to photocatalytic destruction than control cultures, taking 75 min longer to be destroyed. Curli fimbriae are also known to play a role in environmental protection of bacteria and they are associated with biofilm production. The ability of the E. coli strain to produce curli fimbriae was confirmed and biofilms were grown and subjected to photocatalytic treatment. Biofilm destruction by photocatalysis was assessed using a resazurin viability assay and a loss of cell viability was demonstrated within 30 min treatment time. This study suggests that intrinsic bacterial factors may play a role in determining an organism’s response to photocatalytic treatment and highlights their importance in this disinfection process.

Digital Dermatitis in Dairy Cows: A Review of Risk Factors and Potential Sources of Between-Animal Variation in Susceptibility

Digital dermatitis (DD) is a bacterial disease that primarily affects the skin on the heels of cattle. It is a major cause of lameness in dairy cows and a significant problem for the dairy industry in many countries, causing reduced animal welfare and economic loss. A wide range of infection levels has been found on infected farms, prompting investigations into both farm level and animal level risk factors for DD occurrence. There also appears to be individual variation between animals in susceptibility to the disease. The identification of factors affecting individual variation in susceptibility to DD might allow changes in breeding policies or herd management which could be used to reduce DD prevalence. Factors mentioned in the literature as possibly influencing individual variation in susceptibility to DD include physical factors such as hoof conformation and properties of the skin, physiological factors such as the efficacy of the immune response, and behavioural factors such as standing half in cubicles. Further work is required to determine the influence of these factors, identify the genetic basis of variation, clarify the level of heritability of DD susceptibility and to determine how this is correlated with production and health traits currently used in breeding programmes.

City Profile: Erbil

Erbil (Hawler in Kurdish), is the capital and the largest city of Iraqi Kurdistan. Having been continuously inhabited for about 6000 years, the city has recently been regarded by UNESCO World Heritage as one of the world’s oldest urban settlements. The city is witnessing remarkable urban growth and rapid spatial expansion compounded by a dramatic increase in population due to emigration from the countryside and rural areas over the last three decades. Following the changing geopolitical landscape of post-war Iraq, urban changes and socio-political transformation are largely driven by Erbil’s growing autonomous status as the capital of northern region of Kurdistan since 2003. This paper explores the layers of historical, spatial and social developments of the contemporary urban context of Kurdistan in general and of Erbil in particular as a reflection of the changing status of the city, as well as the polarization of Iraq and the emergence of neoliberal urbanism. The tension between the global and modern from one side and traditional and authentic from another is ever present and evident in everyday challenges in the planning of the city. In large part, Erbil’s built fabric embodies the dichotomy of identity and contests between its past and future, in which the present remains a transition between two disconnected realities.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Dynamic modelling of die melt temperature profile in polymer extrusion: Effects of process settings, screw geometry and material

Extrusion is one of the major methods for processing polymeric materials and the thermal homogeneity of the process output is a major concern for manufacture of high quality extruded products. Therefore, accurate process thermal monitoring and control are important for product quality control. However, most industrial extruders use single point thermocouples for the temperature monitoring/control although their measurements are highly affected by the barrel metal wall temperature. Currently, no industrially established thermal profile measurement technique is available. Furthermore, it has been shown that the melt temperature changes considerably with the die radial position and hence point/bulk measurements are not sufficient for monitoring and control of the temperature across the melt flow. The majority of process thermal control methods are based on linear models which are not capable of dealing with process nonlinearities. In this work, the die melt temperature profile of a single screw extruder was monitored by a thermocouple mesh technique. The data obtained was used to develop a novel approach of modelling the extruder die melt temperature profile under dynamic conditions (i.e. for predicting the die melt temperature profile in real-time). These newly proposed models were in good agreement with the measured unseen data. They were then used to explore the effects of process settings, material and screw geometry on the die melt temperature profile. The results showed that the process thermal homogeneity was affected in a complex manner by changing the process settings, screw geometry and material.

The HIF-1alpha C1772T polymorphism may be associated with susceptibility to clinically localised prostate cancer but not with elevated expression of hypoxic biomarkers

We investigated the role of the C1772T polymorphisms in exon 12 of the Hypoxia-inducible factor-1 alpha (HIF-1alpha) gene C1772T genotype in prostate cancer (PCa) and amplification of the hypoxic response. We identified the heterozygous germline CT genotype as an increased risk factor for clinically localised prostate cancer (Odds ratio = 6.2; p < 0.0001). While immunostaining intensity for HIF-1alpha and VEGF was significantly enhanced in 75% of PCa specimens when compared to matched benign specimens (p < 0.0001), the CT genotype did not modulate the kinetics of HIF-1alpha protein expression in hypoxia in vitro, and was not associated with enhanced expression of hypoxic biomarkers. This study provides the first evidence of an increased risk for clinically localised prostate cancer in men carrying the C1772T HIF-1alpha gene polymorphism. Although our results did not suggest an association between expression of hypoxic biomarkers and genotype status, the correlation may merit further investigation.

Variation in DNA repair genes XRCC3, XRCC4, XRCC5 and susceptibility to myeloma

Cytogenetic analysis in myeloma reveals marked chromosomal instability. Both widespread genomic alterations and evidence of aberrant class switch recombination, the physiological process that regulates maturation of the antibody response, implicate the DNA repair pathway in disease pathogenesis. We therefore assessed 27 SNPs in three genes (XRCC3, XRCC4 and XRCC5) central to DNA repair in patients with myeloma and controls from the EpiLymph study and from an Irish hospital registry (n = 306 cases, 263 controls). For the haplotype-tagging SNP (htSNP) rs963248 in XRCC4, Allele A was significantly more frequent in cases than in controls (86.4 versus 80.8%; odds ratio 1.51; 95% confidence interval 1.10-2.08; P = 0.0133), as was the AA genotype (74 versus 65%) (P = 0.026). Haplotype analysis was performed using Unphased for rs963248 in combination with additional SNPs in XRCC4. The strongest evidence of association came from the A-T haplotype from rs963248-rs2891980 (P = 0.008). For XRCC5, the genotype GG from rs1051685 was detected in 10 cases from different national populations but in only one control (P = 0.015). This SNP is located in the 3'-UTR of XRCC5. Overall, these data provide support for the hypothesis that common variation in the genes encoding DNA repair proteins contributes to susceptibility to myeloma.

Mycosis fungoides shows concurrent deregulation of multiple genes involved in the TNF signaling pathway:an expression profile study

Mycosis fungoides (MF) is the most frequent type of cutaneous T-cell lymphoma, whose diagnosis and study is hampered by its morphologic similarity to inflammatory dermatoses (ID) and the low proportion of tumoral cells, which often account for only 5% to 10% of the total tissue cells. cDNA microarray studies using the CNIO OncoChip of 29 MF and 11 ID cases revealed a signature of 27 genes implicated in the tumorigenesis of MF, including tumor necrosis factor receptor (TNFR)-dependent apoptosis regulators, STAT4, CD40L, and other oncogenes and apoptosis inhibitors. Subsequently a 6-gene prediction model was constructed that is capable of distinguishing MF and ID cases with unprecedented accuracy. This model correctly predicted the class of 97% of cases in a blind test validation using 24 MF patients with low clinical stages. Unsupervised hierarchic clustering has revealed 2 major subclasses of MF, one of which tends to include more aggressive-type MF cases including tumoral MF forms. Furthermore, signatures associated with abnormal immunophenotype (11 genes) and tumor stage disease (5 genes) were identified.

Multigenerational Undernutrition Increases Susceptibility to Obesity and Diabetes that Is Not Reversed after Dietary Recuperation

People in developing countries have faced multigenerational undernutrition and are currently undergoing major lifestyle changes, contributing to an epidemic of metabolic diseases, though the underlying mechanisms remain unclear. Using a Wistar rat model of undernutrition over 50 generations, we show that Undernourished rats exhibit low birth-weight, high visceral adiposity (DXA/MRI), and insulin resistance (hyperinsulinemic-euglycemic clamps), compared to age-/gender-matched control rats. Undernourished rats also have higher circulating insulin, homocysteine, endotoxin and leptin levels, lower adiponectin, vitamin B12 and folate levels, and an 8-fold increased susceptibility to Streptozotocin-induced diabetes compared to control rats. Importantly, these metabolic abnormalities are not reversed after two generations of unrestricted access to commercial chow (nutrient recuperation). Altered epigenetic signatures in insulin-2 gene promoter region of Undernourished rats are not reversed by nutrient recuperation, and may contribute to the persistent detrimental metabolic profiles in similar multigenerational undernourished human populations.

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.