Atypical presentation of placenta percreta post-partum-a conservative surgical approach

We report our experience with an atypical presentation of placenta percreta, presenting as a mass-like bulge in the uterine fundus. A hemodynamically stable young lady status-post preterm delivery at 26 weeks was referred to our center on the third post-partum day after multiple failed attempts at removal of a retained placenta. Magnetic resonance imaging (MRI) showed an atypical fibroid with part of an adherent placenta. Uterine artery embolization was done prophylactically. After a failure at removal under USG guidance, a diagnostic laparoscopy revealed an 8x6 cm highly vascular mass in the fundus extending to the right cornua with intact serosa, possibly placenta percreta. The procedure converted to laparotomy and the mass removed. Histopathology confirmed a placenta percreta. However, the neonate admitted at the referring hospital expired on day 14 due to sepsis. Post-partum adherent placenta in the fundal region on MRI can mimic an atypical fibroid

FACTORS ASSOCIATED WITH BODY MASS INDEX IN CHILDREN – A COMMUNITY-BASED STUDY

Objectives: The increasing prevalence of overweight, obesity, and underweight in children has implications for their future health and it is vital to understand the modifiable factors that contribute to it. The study’s main objective is to determine the factors associated with the body mass index in children. Methods: A cross-sectional study was conducted among 346 school children over a period of 6 months. Data were collected using self-administered questionnaire. Physical measurements such as height and weight were obtained from parents. Body mass index was calculated using kg/m2. Children were categorized as overweight, obese, normal, and underweight using their body mass index scores and the factors associated with BMI in children were estimated. Results: The prevalence of overweight, obesity, normal, and underweight among children was 10.40%, 17.92%, 24.28%, and 41.67%, respectively. Intake of fast food, sweetened beverages, junk food, and consumption of food while watching television, media time indicated a significant relationship with body mass index. Conclusion: Health care professionals should educate parents and children regarding healthy nutrition and regular physical activity. The implications of obesity, overweight, and underweight should be well explained to make desirable lifestyle modifications for a better future

Design and User Satisfaction of Interactive Maps for Visually Impaired People

Multimodal interactive maps are a solution for presenting spatial information to visually impaired people. In this paper, we present an interactive multimodal map prototype that is based on a tactile paper map, a multi-touch screen and audio output. We first describe the different steps for designing an interactive map: drawing and printing the tactile paper map, choice of multi-touch technology, interaction technologies and the software architecture. Then we describe the method used to assess user satisfaction. We provide data showing that an interactive map - although based on a unique, elementary, double tap interaction - has been met with a high level of user satisfaction. Interestingly, satisfaction is independent of a user's age, previous visual experience or Braille experience. This prototype will be used as a platform to design advanced interactions for spatial learning

Evolution of associative learning in chemical networks

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells

Short-wavelength infrared photodetector on Si employing strain-induced growth of very tall InAs nanowire arrays

One-dimensional crystal growth enables the epitaxial integration of III-V compound semiconductors onto a silicon (Si) substrate despite significant lattice mismatch. Here, we report a short-wavelength infrared (SWIR, 1.4-3 mu m) photodetector that employs InAs nanowires (NWs) grown on Si. The wafer-scale epitaxial InAs NWs form on the Si substrate without a metal catalyst or pattern assistance; thus, the growth is free of metal-atom-induced contaminations, and is also cost-effective. InAs NW arrays with an average height of 50 mu m provide excellent anti-reflective and light trapping properties over a wide wavelength range. The photodetector exhibits a peak detectivity of 1.9 x 10(8) cm.Hz(1/2)/W for the SWIR band at 77 K and operates at temperatures as high as 220 K. The SWIR photodetector on the Si platform demonstrated in this study is promising for future low-cost optical sensors and Si photonicsopen0

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

The 100 most cited articles investigating the radiological staging of oesophageal and junctional cancer: a bibliometric analysis

Objectives Accurate staging of oesophageal cancer (OC) is vital. Bibliometric analysis highlights key topics and publications that have shaped understanding of a subject. The 100 most cited articles investigating radiological staging of OC are identified. Methods The Thomas Reuters Web of Science database with search terms including “CT, PET, EUS, oesophageal and gastro-oesophageal junction cancer” was used to identify all English language, full-script articles. The 100 most cited articles were further analysed by topic, journal, author, year and institution. Results A total of 5,500 eligible papers were returned. The most cited paper was Flamen et al. (n = 306), investigating the utility of positron emission tomography (PET) for the staging of patients with potentially operable OC. The most common research topic was accuracy of staging investigations (n = 63). The article with the highest citation rate (38.00), defined as the number of citations divided by the number of complete years published, was Tixier et al. investigating PET texture analysis to predict treatment response to neo-adjuvant chemo-radiotherapy, cited 114 times since publication in 2011. Conclusion This bibliometric analysis has identified key publications regarded as important in radiological OC staging. Articles with the highest citation rates all investigated PET imaging, suggesting this modality could be the focus of future research

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system