Automated Screening for Three Inborn Metabolic Disorders: A Pilot Study

Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling us to take decisions on the screening and clinical diagnosis of a patient. Settings and Design: A non-incremental concept learning classification algorithm was applied to a set of patient data and the procedure followed to obtain a decision on a patient’s disorder. Materials and Methods: Initially a training set containing 13 cases was investigated for three inborn errors of metabolism. Results: A total of thirty test cases were investigated for the three inborn errors of metabolism. The program identified 10 cases with galactosemia, another 10 cases with fructosemia and the remaining 10 with propionic acidemia. The program successfully identified all the 30 cases. Conclusions: This kind of decision support systems can help the healthcare delivery personnel immensely for early screening of IMDs

Non Inflammatory Boronate Based Glucose-Responsive Insulin Delivery Systems

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L–40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger

A System Dynamics Approach for Hospital Waste Management in a City in a Developing Country: The Case of Nablus, Palestine

Hospitals and health centers provide a variety of healthcare services and normally generate hazardous waste as well as general waste. General waste has a similar nature to that of municipal solid waste and therefore could be disposed of in municipal landfills. However, hazardous waste poses risks to public health, unless it is properly managed. The hospital waste management system encompasses many factors, i.e., number of beds, number of employees, level of service, population, birth rate, fertility rate, and not in my back yard (NIMBY) syndrome. Therefore, this management system requires a comprehensive analysis to determine the role of each factor and its influence on the whole system. In this research, a hospital waste management simulation model is presented based on the system dynamics technique to determine the interaction among these factors in the system using a software package, ithink. This model is used to estimate waste segregation as this is important in the hospital waste management system to minimize risk to public health. Real data has been obtained from a case study of the city of Nablus, Palestine to validate the model. The model exhibits wastes generated from three types of hospitals (private, charitable, and government) by considering the number of both inpatients and outpatients depending on the population of the city under study. The model also offers the facility to compare the total waste generated among these different types of hospitals and anticipate and predict the future generated waste both infectious and non-infectious and the treatment cost incurred

Recurrent stupor due to lysinuric protein intolerance

Recurrent stupor in children is an uncommon clinical problem with a wide differential diagnosis; inherited metabolic disorders account for a vast majority. We report a 9-year-old girl with recurrent episodes of stupor. Initial episode was treated as viral encephalitis and the second episode was managed as non-convulsive status epilepticus. Hyperammonemia was detected in the last episode. Metabolic work-up after dietary protein challenge revealed classical biochemical features of lysinuric protein intolerance. She was managed with protein-restricted diet, which resulted in marked neurological improvement. LPI is a rare inherited metabolic disorder due to membrane transport defect of cationic amino acids

Shallow water wave characteristics off Cochin during monsoon 1986

256-262Shallow water wave measurements were carried out off Cochin (depth 12.5 m) during May to October 1986. Wind speed (U), significant wave height (Hs) and zero-upcrossing period(Tz)varied between 2 and 9 m. sec(-1), 0.4 and 2 m, and 3.5 and 8 sec respectively. The value of U measured at the coast was less compared with the open sea and did not show significant correlation with Hs and Tz. However, both wind and waves show diurnal variability although a major portion of the wave energy was propagated from deep waters as swells. The present analysis revealed very good correlation between Hs and Tz. Onshore and offshore components of the wind normal to the coastline also showed better correlation with Hs and Tz

Assessment of Metabolic Parameters For Autism Spectrum Disorders

Autism is a brain development disorder that first appears during infancy or childhood, and generally follows a steady course without remission. Impairments result from maturation-related changes in various systems of the brain. Autism is one of the five pervasive developmental disorders (PDD), which are characterized by widespread abnormalities of social interactions and communication, and severely restricted interests and highly repetitive behavior. The reported incidence of autism spectrum disorders (ASDs) has increased markedly over the past decade. The Centre for Disease Control and Prevention has recently estimated the prevalence of ASDs in the United States at approximately 5.6 per 1000 (1 of 155 to 1 of 160) children. Several metabolic defects, such as phenylketonuria, are associated with autistic symptoms. In deciding upon the appropriate evaluation scheme a clinician must consider a host of different factors. The guidelines in this article have been developed to assist the clinician in the consideration of these factors

Eddy Resolving Simulations of Shear Layer Instabilities in Open Cavity Flows

No abstract available

Evaluation of wound healing potential of bauhinia purpurea

The present study was carried out to evaluate the effect of methanol and chloroform extracts of Bauhinia purpurea on experimentally induced excision, incision, burn and dead space wound models in Sprague Dawley rats. Formulations of methanol and chloroform extracts of Bauhinia purpurea were prepared in carbopol and simple ointment base at concentrations of 2.5% and 5% and applied to the wounds. In the excision and burn wound models, animals treated with high doses of methanol and chloroform showed significant reduction in time taken for epithelization and wound contraction (50%) compared to control. A significant increase in breaking strength was found in incision wound model with methanol and chloroform extracts compared to their respective bases. In the dead space wound model, methanol and chloroform extract treatment (100 and 500 mg/kg) orally produced a significant increase in the breaking strength, dry tissue weight and hydroxyproline content of the granulation tissue when compared to control. Among the extracts, methanol extract exhibited more activity followed by the chloroform extract. In conclusion, the present study indicated that Bauhinia purpurea leaves exhibited wound healing activity

Estimation of Link Interference in Static Multi-hop Wireless Networks

We present a measurement-based study of interference among links in a static, IEEE 802.11, multi-hop wireless network. Interference is a key cause of performance degradation in such networks. To improve, or to even estimate the performance of these networks, one must have some knowledge of which links in the network interfere with one another, and to what extent. However, the problem of estimating the interference among links of a multihop wireless network is a challenging one. Accurate modeling of radio signal propagation is difficult since many environment and hardware-specific factors must be considered. Empirically testing every group of links is not practical: a network with n nodes can have O(n 2) links, and even if we consider only pairwise interference, we may have to potentially test O(n 4) pairs. Given these difficulties, much of the previous work on wireless networks has assumed that information about interference in the network is either known, or that it can be approximated using simple heuristics. We test these heuristics in our testbed and find them to be inaccurate. We then propose a simple, empirical estimation methodology that can predict pairwise interference using only O(n 2) measurements. Our methodology is applicable to any wireless network that uses omni-directional antennas. The predictions made by our methodology match well with the observed pairwise interference among links in our 22 node, 802.11-based testbed.