Background: Diabetic ketoacidosis (DKA) is a medical emergency. It may be the presenting feature of diabetes mellitus, but more commonly DKA complicates diabetic patients during inter-current acute ...illness or if they become non-compliant to anti-diabetic medications. Early detection and treatment of DKA including underlying cause is important in determining outcome.Objectives: To describe the demographic characteristics, presentations, precipitating factors and outcome of DKA.Methods: This cross-sectional study was conducted at BIRDEM General Hospital from January 2008 to December 2011.Results: Total patients were 200 with female predominance (56%). Mean age of the study population was 37.6 years. Incidence of DKA was more in known diabetic patients (71%) than in new cases (29%), more among rural population (53%) and low income group (76.5%). Common presentations included nausea (63%), vomiting (61%), polyuria (43%), polydypsia (42.5%), fever (29%), abdominal pain (28%), shortness of breath (28%), drowsiness (20%), blurred vision (13%), leg cramps (6.5%) and coma (7%). Infection (45.5%) was the commonest precipitating cause of DKA followed by non-adherence to insulin therapy (31%). Acute pancreatitis (5%), myocardial infarction (2%), stroke (1%) and surgery (1.5%) were less common precipitating factors. Aetiology of DKA could not be identified in 14% cases. Mean random blood glucose during admission was 27.1mmol/L and mean HbA1c was 11.3%. Severe acidosis (pH<7) was less common (8.5%). Neutrophil leukocytosis was present in 87% cases, irrespective of infection. In-hospital mortality was 6.5%.Conclusion: DKA occurred in diabetic patients in over two-third of the cases. In over two-third of the study population, DKA was precipitated by infection and non-adherence to insulin treatment. So, patient education regarding treatment compliance and sick days management are important and may prevent many cases of DKABangladesh Crit Care J September 2015; 3 (2): 53-56
Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of ...multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
Using of nano-inclusion to reinforce polymeric materials has emerged as a potential technique to achieve an upper extreme of specific strength. Despite the significant improvement of mechanical ...properties via nano-reinforcements, the commercial application of such nano-composites is still restricted, due to high cost and unwanted aggregation of nanoparticles in the polymer matrix. To address these issues, here we proposed a scalable and economical synthesis of TiO2 at low temperatures, resulting in self-dispersed nanoparticles, without any surfactant. As lower energy is consumed in the synthesis and processing of such nanoparticles, so their facile gram-scale synthesis is possible. The defect-rich surface of such nanoparticles accommodates excessive dangling bonds, serving as a center for the functional groups on the surface. Functional surface enables high dispersion stability of room temperature synthesized TiO2 particles. With this motivation, we optimized the processing conditions and concentration of as-synthesized nano-particles for better mechanical properties of unsaturated polyester (UP) resin. The composite structure (UP-TiO2) showed nearly two folds higher tensile, flexural, and impact strength, with 4% content of nanoparticles. Characterization tools show that these better mechanical properties are attributed to a strong interface and superior dispersion of nanoparticles, which facilitate better stress distribution in the composite structure. In addition, the crack generation and propagation are restricted at a much smaller scale in nanocomposites, therefore significant improvement in mechanical properties was observed.
Biologically active secondary metabolites, essential oils, and volatile compounds derived from medicinal and aromatic plants play a crucial role in promoting human health. Within the large family ...Asteraceae, the genus
consists of approximately 500 species.
species have a rich history in traditional medicine worldwide, offering remedies for a wide range of ailments, such as malaria, jaundice, toothache, gastrointestinal problems, wounds, inflammatory diseases, diarrhoea, menstrual pains, skin disorders, headache, and intestinal parasites. The therapeutic potential of
species is derived from a multitude of phytoconstituents, including terpenoids, phenols, flavonoids, coumarins, sesquiterpene lactones, lignans, and alkaloids that serve as active pharmaceutical ingredients (API). The remarkable antimalarial, antimicrobial, anthelmintic, antidiabetic, anti-inflammatory, anticancer, antispasmodic, antioxidative and insecticidal properties possessed by the species are attributed to these APIs. Interestingly, several commercially utilized pharmaceutical drugs, including arglabin, artemisinin, artemether, artesunate, santonin, and tarralin have also been derived from different
species. However, despite the vast medicinal potential, only a limited number of
species have been exploited commercially. Further, the available literature on traditional and pharmacological uses of
lacks comprehensive reviews. Therefore, there is an urgent need to bridge the existing knowledge gaps and provide a scientific foundation for future
research endeavours. It is in this context, the present review aims to provide a comprehensive account of the traditional uses, phytochemistry, documented biological properties and toxicity of all the species of
and offers useful insights for practitioners and researchers into underutilized species and their potential applications. This review aims to stimulate further exploration, experimentation and collaboration to fully realize the therapeutic potential of
in augmenting human health and well-being.
We present reliability-based optimization (RBO) of the Mechanically Stability Earth (MSE) walls, using constrained optimization, considering the external stability, under ultimate limit state ...conditions of sliding, eccentricity, and bearing capacity. The design is optimized for a target reliability index of 3 that corresponds to an approximate failure probability of 1 in 1000. Reliability index is calculated by the first-order reliability method (FORM). The MSE wall, founded on cohesionless soil, with horizontal backfill and uniform live traffic surcharge, is studied. The RBO results are reported for the height of MSE wall ranging from 1.5 m to 20 m. For target reliability index of 3, the optimized length to height ratio, Lopt/H, of the MSE wall is greater than 0.7 (the minimum length to height ratio requirement of AASHTO) for H≤4.5 m, and then it decreases below the minimum required value of 0.7 for H>4.5 m. The RBO approach presented in this study will help practitioners to achieve cost-effectiveness in design.
In recent years, research interest has been revolutionized to predict the rigid projectile penetration depth in concrete. The concrete penetration predictions persist, unsettled, due to the ...complexity of phenomena and the continuous development of revolutionized statistical techniques, such as machine learning, neural networks, and deep learning. This research aims to develop a new model to predict the penetration depth of the ogive nose rigid projectile into concrete blocks using machine learning. Genetic coding is used in Python programming to discover the underlying mathematical relationship from the experimental data in its non-dimensional form. A populace of erratic formulations signifies the rapport amid dependent parameters, such as the impact factor (I), the geometry function of the projectile (N), the empirical constant for concrete strength (S), the slenderness of the projectile (λ), and their independent objective variable, X/d, where X is the penetration depth of the projectile and d is the diameter of the projectile. Four genetic operations were used, including the crossover, sub-tree transfiguration, hoist transfiguration, and point transfiguration operations on supervised test datasets, which were divided into three categories, namely, narrow penetration (X/d < 0.5), intermediate penetration (0.5 ≤ X/d < 5.0), and deep penetration (X/d ≥ 5.0). The proposed model shows a significant relationship with all data in the category for medium penetration, where R2 = 0.88, and R2 = 0.96 for deep penetration. Furthermore, the proposed model predictions are also compared with the most commonly used NDRC and Li and Chen models. The outcome of this research shows that the proposed model predicts the penetration depth precisely, compared to the NDRC and Li and Chen models.
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