Characterization of mixed lymphocyte reaction blocking antibodies (MLR-Bf) in human pregnancy

BACKGROUND: It is known that during normal pregnancy and after immunotherapy blocking antibodies are developed, these antibodies inhibit mixed lymphocyte reaction and are also anti-mitogenic in nature. Mixed lymphocyte reaction blocking antibodies are specific to the husband's lymphocytes. In the present study an attempt has been made to characterize the mixed lymphocyte reaction blocking antibodies in normal pregnancy and in women with recurrent spontaneous abortion after immunotherapy. METHODS: Serum was obtained from women of different gestational windows of pregnancy (Ist, IInd, IIIrd trimesters and post delivery period of normal pregnancy), recurrent spontaneous aborters from pre and post immunization. Healthy (male and females) controls were screened for the presence of mixed lymphocyte reaction blocking antibodies. The standard mixed lymphocyte reaction technique was used to evaluate the inhibitory effect of serum in the mixed lymphocyte reaction. Each serum was tested for cytotoxic antibodies. Immunoglobulin G and its isotypes were isolated according to the standard protocol. RESULTS: In the present study we have observed that there was significant inhibition of proliferation response when immunoglobulin G from different trimesters of pregnancy were added to one way mixed lymphocyte reaction or to phytohemagglutinin activated lymphocyte proliferation assay. Similar pattern was seen when immunoglobulin G isolated from adequately immunized women with recurrent spontaneous abortion was used. It was further confirmed that amongst all the isotypes of immunoglobulin G, only immunoglobulin G-3 was found to be positive for the inhibitory effect. CONCLUSIONS: Present study indicates that mixed lymphocyte reaction blocking antibodies are immunoglobulin G-3 in nature. It is developed during pregnancy and also after immunotherapy in women with recurrent spontaneous abortion who subsequently have the successful pregnancy

How Can I Drink Safely?; Perception Versus the Reality of Alcohol Consumption

This article investigates differences between perception and actual consumption of alcohol in young adults within the UK, suggesting that inaccurate information in the public domain may hamper those seeking to drink safely plus the development of moderate drinking cultures. Results confirm that inaccurate information may be preventing the development of safe drinking behaviours among certain groups. In addition, they indicate that some groups choose to ignore safe consumption limits in particular circumstances. Results indicate that many government strategies aimed at reducing unsafe drinking behaviour are inaccurately targeted; changing male public consumption behaviour may trigger changes in female behaviour

Schmallenberg virus pathogenesis, tropism and interaction with the innate immune system of the host

Schmallenberg virus (SBV) is an emerging orthobunyavirus of ruminants associated with outbreaks of congenital malformations in aborted and stillborn animals. Since its discovery in November 2011, SBV has spread very rapidly to many European countries. Here, we developed molecular and serological tools, and an experimental in vivo model as a platform to study SBV pathogenesis, tropism and virus-host cell interactions. Using a synthetic biology approach, we developed a reverse genetics system for the rapid rescue and genetic manipulation of SBV. We showed that SBV has a wide tropism in cell culture and “synthetic” SBV replicates in vitro as efficiently as wild type virus. We developed an experimental mouse model to study SBV infection and showed that this virus replicates abundantly in neurons where it causes cerebral malacia and vacuolation of the cerebral cortex. These virus-induced acute lesions are useful in understanding the progression from vacuolation to porencephaly and extensive tissue destruction, often observed in aborted lambs and calves in naturally occurring Schmallenberg cases. Indeed, we detected high levels of SBV antigens in the neurons of the gray matter of brain and spinal cord of naturally affected lambs and calves, suggesting that muscular hypoplasia observed in SBV-infected lambs is mostly secondary to central nervous system damage. Finally, we investigated the molecular determinants of SBV virulence. Interestingly, we found a biological SBV clone that after passage in cell culture displays increased virulence in mice. We also found that a SBV deletion mutant of the non-structural NSs protein (SBVΔNSs) is less virulent in mice than wild type SBV. Attenuation of SBV virulence depends on the inability of SBVΔNSs to block IFN synthesis in virus infected cells. In conclusion, this work provides a useful experimental framework to study the biology and pathogenesis of SBV

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Shotgun Proteomics Identifies Serum Fibronectin as a Candidate Diagnostic Biomarker for Inclusion in Future Multiplex Tests for Ectopic Pregnancy

Ectopic pregnancy (EP) is difficult to diagnose early and accurately. Women often present at emergency departments in early pregnancy with a 'pregnancy of unknown location' (PUL), and diagnosis and exclusion of EP is challenging due to a lack of reliable biomarkers. The objective of this study was to identify novel diagnostic biomarkers for EP. Shotgun proteomics, incorporating combinatorial-ligand library pre-fractionation, was used to interrogate pooled sera (n = 40) from women undergoing surgery for EP, termination of viable intrauterine pregnancy and management of non-viable intrauterine pregnancy. Western blot was used to validate results in individual sera. ELISAs were developed to interrogate sera from women with PUL (n = 120). Sera were collected at time of first symptomatic presentation and categorized according to pregnancy outcome. The main outcome measures were differences between groups and area under the receiver operating curve (ROC). Proteomics identified six biomarker candidates. Western blot detected significant differences in levels of two of these candidates. ELISA of sera from second cohort revealed that these differences were only significant for one of these candidates, fibronectin. ROC analysis of ability of fibronectin to discriminate EP from other pregnancy outcomes suggested that fibronectin has diagnostic potential (ROC 0.6439; 95% CI 0.5090 to 0.7788; P>0.05), becoming significant when 'ambiguous' medically managed PUL excluded from analysis (ROC 0.6538; 95% CI 0.5158 to 0.7918; P<0.05). Fibronectin may make a useful adjunct to future multiplex EP diagnostic tests

Home Range and Ranging Behaviour of Bornean Elephant (Elephas maximus borneensis) Females

BACKGROUND: Home range is defined as the extent and location of the area covered annually by a wild animal in its natural habitat. Studies of African and Indian elephants in landscapes of largely open habitats have indicated that the sizes of the home range are determined not only by the food supplies and seasonal changes, but also by numerous other factors including availability of water sources, habitat loss and the existence of man-made barriers. The home range size for the Bornean elephant had never been investigated before. METHODOLOGY/PRINCIPAL FINDINGS: The first satellite tracking program to investigate the movement of wild Bornean elephants in Sabah was initiated in 2005. Five adult female elephants were immobilized and neck collars were fitted with tracking devices. The sizes of their home range and movement patterns were determined using location data gathered from a satellite tracking system and analyzed by using the Minimum Convex Polygon and Harmonic Mean methods. Home range size was estimated to be 250 to 400 km(2) in a non-fragmented forest and 600 km(2) in a fragmented forest. The ranging behavior was influenced by the size of the natural forest habitat and the availability of permanent water sources. The movement pattern was influenced by human disturbance and the need to move from one feeding site to another. CONCLUSIONS/SIGNIFICANCE: Home range and movement rate were influenced by the degree of habitat fragmentation. Once habitat was cleared or converted, the availability of food plants and water sources were reduced, forcing the elephants to travel to adjacent forest areas. Therefore movement rate in fragmented forest was higher than in the non-fragmented forest. Finally, in fragmented habitat human and elephant conflict occurrences were likely to be higher, due to increased movement bringing elephants into contact more often with humans

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction

&lt;b&gt;Background&lt;/b&gt; The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt; The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis

Gene-Expression Signature Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer Patients

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients