The structure of human CD23 and its interactions with IgE and CD21

The low-affinity immunoglobulin E (IgE) receptor, CD23 (FcɛRII), binds both IgE and CD21 and, through these interactions, regulates the synthesis of IgE, the antibody isotype that mediates the allergic response. We have determined the three-dimensional structure of the C-type lectin domain of CD23 in solution by nuclear magnetic resonance spectroscopy. An analysis of concentration-dependent chemical shift perturbations have allowed us to identify the residues engaged in self-association to the trimeric state, whereas ligand-induced changes have defined the binding sites for IgE and CD21. The results further reveal that CD23 can bind both ligands simultaneously. Despite the C-type lectin domain structure, none of the interactions require calcium. We also find that IgE and CD23 can interact to form high molecular mass multimeric complexes. The interactions that we have described provide a solution to the paradox that CD23 is involved in both up- and down-regulation of IgE and provide a structural basis for the development of inhibitors of allergic disease

Inhibition of activin/nodal signalling is necessary for pancreatic differentiation of human pluripotent stem cells

Peer reviewedPublisher PD

Circulating mRNAs are differentially expressed in pregnancies with severe placental insufficiency and at high risk of stillbirth

Background Fetuses affected by placental insufficiency do not receive adequate nutrients and oxygenation, become growth restricted and acidemic, and can demise. Preterm fetal growth restriction is a severe form of placental insufficiency with a high risk of stillbirth. We set out to identify maternal circulating mRNA transcripts that are differentially expressed in preterm pregnancies complicated by very severe placental insufficiency, in utero fetal acidemia, and are at very high risk of stillbirth. Methods We performed a cohort study across six hospitals in Australia and New Zealand, prospectively collecting blood from 128 pregnancies complicated by preterm fetal growth restriction (delivery < 34 weeks’ gestation) and 42 controls. RNA-sequencing was done on all samples to discover circulating mRNAs associated with preterm fetal growth restriction and fetal acidemia in utero. We used RT-PCR to validate the associations between five lead candidate biomarkers of placental insufficiency in an independent cohort from Europe (46 with preterm fetal growth restriction) and in a third cohort of pregnancies ending in stillbirth. Results In the Australia and New Zealand cohort, we identified five mRNAs that were highly differentially expressed among pregnancies with preterm fetal growth restriction: NR4A2, EMP1, PGM5, SKIL, and UGT2B1. Combining three yielded an area under the receiver operative curve (AUC) of 0.95. Circulating NR4A2 and RCBTB2 in the maternal blood were dysregulated in the presence of fetal acidemia in utero. We validated the association between preterm fetal growth restriction and circulating EMP1, NR4A2, and PGM5 mRNA in a cohort from Europe. Combining EMP1 and PGM5 identified fetal growth restriction with an AUC of 0.92. Several of these genes were differentially expressed in the presence of ultrasound parameters that reflect placental insufficiency. Circulating NR4A2, EMP1, and RCBTB2 mRNA were differentially regulated in another cohort destined for stillbirth, compared to ongoing pregnancies. EMP1 mRNA appeared to have the most consistent association with placental insufficiency in all cohorts. Conclusions Measuring circulating mRNA offers potential as a test to identify pregnancies with severe placental insufficiency and at very high risk of stillbirth. Circulating mRNA EMP1 may be promising as a biomarker of severe placental insufficiency

A Polymorphism in a Gene Encoding Perilipin 4 Is Associated with Height but not with Bone Measures in Individuals from the Framingham Osteoporosis Study

There is increasing interest in identifying new pathways and candidate genes that confer susceptibility to osteoporosis. There is evidence that adipogenesis and osteogenesis may be related, including a common bone marrow progenitor cell for both adipocytes and osteoblasts. Perilipin 1 (PLIN1) and Perilipin 4 (PLIN4) are members of the PATS family of genes and are involved in lipolysis of intracellular lipid deposits. A previous study reported gender-specific associations between one polymorphism of PLIN1 and bone mineral density (BMD) in a Japanese population. We hypothesized that polymorphisms in PLIN1 and PLIN4 would be associated with bone measures in adult Caucasian participants of the Framingham Osteoporosis Study (FOS). We genotyped 1,206 male and 1,445 female participants of the FOS for four single-nucleotide polymorphism (SNPs) in PLIN1 and seven SNPs in PLIN4 and tested for associations with measures of BMD, bone ultrasound, hip geometry, and height. We found several gender-specific significant associations with the measured traits. The association of PLIN4 SNP rs8887, G>A with height in females trended toward significance after simulation testing (adjusted P = 0.07) and remained significant after simulation testing in the combined-sex model (adjusted P = 0.033). In a large study sample of men and women, we found a significant association between one SNP in PLIN4 and height but not with bone traits, suggesting that PATS family genes are not important in the regulation of bone. Identification of genes that influence human height may lead to a better understanding of the processes involved in growth and development

Physical activity to improve cognition in older adults: can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis

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An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?

Risk prediction model for knee pain in the Nottingham Community: a Bayesian modeling approach

Background: 25% of the British population over the age of 50 experience knee pain. It can limit physical ability, cause distress and bears significant socioeconomic costs. Knee pain, not knee osteoarthritis (KOA) is the all to common malady. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiaitve (OAI) Cohort. Methods: 1822 participants at risk for knee pain from the Nottingham community were followed up for 12 years. Of this cohort, 2/3 (n=1203) were used to develop the risk prediction model and 1/3 (n=619) were used to validate the model. Incident knee pain was defined as pain on most days for at least one month in the past 12 months. Predictors were age, gender, body mass index (BMI), pain elsewhere, prior knee injury and knee alignment. Bayesian logistic regression model was used to determine the probability of an odds ratio >1. The Hosmer-Lemeshow x2 statistic (HLS) was used for calibration and receiver operator characteristics (ROC) was used for discrimination. The OAI cohort was used to examine the performance of the model in a secondary care population. Results: A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration with HLS of 7.17 (p=0.52) and moderate discriminative abilities (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p<0.01) and poor discriminative ability (ROC 0.54) in the OAI secondary care dataset. Conclusion: This is the first risk prediction model for knee pain, irrespective of underlying structural changes of KOA, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in a hospital derived cohort and may provide a convenient tool for primary care to predict the risk of knee pain in the general population

The response of leptin, interleukin-6 and fat oxidation to feeding in weight-losing patients with pancreatic cancer

At baseline, weight-losing pancreatic cancer patients (n=7) had lower leptin (P&#60;0.05) but higher cortisol, interleukin-6, resting energy expenditure and fat oxidation than healthy subjects (n=6, P&lt;0.05). Over a 4 h feeding period, the areas under the curve for glucose, cortisol and interleukin-6 were greater (P&#60;0.05), but less for leptin in the cancer group (P&#60;0.05). Therefore, it would appear that low leptin concentrations, increased fat oxidation and insulin resistance are associated with increased concentrations of cortisol and interleukin-6 in weight-losing patients with pancreatic cancer