A Case Report of Toxicity to an Opioid Analgesic

Opioid synthetic pain killers act centrally and are commonly used for management of high grade pain. Although it is a standard drug being prescribed all over the world, its accidental or intentional overdose is more common in adolescent and adult males. We report the case of a 7-year-old boy who was brought to our clinic in an unconscious state following opioid intoxication. He was managed with conservative approach and made a significant improvement before discharge

Determinação das regras de associação de variáveis de tempo ponderadas baseadas em utilidades mediante a aplicação de uma árvore de padrões frequentes

Introduction: The present research was conducted at Birla Institute of Technology, off Campus in Noida, India, in 2017. Methods: To assess the efficiency of the proposed approach for information mining a method and an algorithm were proposed for mining time-variant weighted, utility-based association rules using fp-tree. Results: A method is suggested to find association rules on time-oriented frequency-weighted, utility-based data, employing a hierarchy for pulling-out item-sets and establish their association. Conclusions: The dimensions adopted while developing the approach compressed a large time-variant dataset to a smaller data structure at the same time fp-tree was kept away from the repetitive dataset, which finally gave us a noteworthy advantage in articulations of time and memory use. Originality: In the current period, high utility recurrent-pattern pulling-out is one of the mainly noteworthy study areas in time-variant information mining due to its capability to account for the frequency rate of item-sets and assorted utility rates of every item-set. This research contributes to maintain it at a corresponding level, which ensures to avoid generating a big amount of candidate-sets, which ensures further development of less execution time and search spaces. Limitations: The research results demonstrated that the projected approach was efficient on tested datasets with pre-defined weight and utility calculations.Introducción: la presente investigación se realizó en el Birla Institute of Technology, fuera del campus en Noida, India, en 2017. Métodos: para evaluar la eficacia del enfoque propuesto para la minería de información, se propusieron un método y un algoritmo para minar las reglas de asociación basadas en la utilidad ponderada en el tiempo usando un árbol de patrones frecuentes (fp). Resultados: se sugiere un método para encontrar reglas de asociación en datos basados en la utilidad ponderada en frecuencia orientada al tiempo, que emplea una jerarquía para extraer conjuntos de elementos y establecer su asociación. Conclusiones: las dimensiones adoptadas al desarrollar el enfoque comprimieron un gran conjunto de datos de variante de tiempo hasta alcanzar una estructura de datos más pequeña. A su vez, el árbol fp se mantuvo alejado del conjunto de datos repetitivos, lo que finalmente generó una ventaja considerable en tiempo y uso de memoria. Originalidad: en la actualidad, la extracción de patrones recurrentes de alta utilidad es una de las áreas de estudio más desarrollada en la minería de información con respecto a la variable temporal debido a su capacidad de dar cuenta de la frecuencia de los conjuntos de elementos y las tasas de servicios varios de cada conjunto de elementos. Esta investigación contribuye a mantener el estudio sobre el tema a un buen nivel, lo que permite evitar generar una gran cantidad de conjuntos posibles, y por ende garantiza mayor desarrollo en menores tiempos de ejecución y espacios de búsqueda. Limitaciones: Los resultados de la investigación demostraron que la aproximación fue eficiente en conjuntos de datos probados con cálculos predefinidos de peso y utilidad.Introdução: esta pesquisa foi realizada no Instituto Birla de Tecnologia e Ciência, fora do campus, em Noida, na Índia, em 2017. Métodos: para avaliar a eficácia do enfoque proposto para mineração de informação, foram propostos um método e um algoritmo para minerar as regras de associação baseadas na utilidade ponderada no tempo usando uma árvore de padrões frequentes (fp).Resultados: é recomendado um método para encontrar regras de associação nos dados baseados na utilidade ponderada em frequência orientada ao tempo, que emprega uma hierarquia para extrair conjuntos de elementos e estabelecer a associação entre eles.Conclusões: as dimensões utilizadas ao desenvolver o enfoque comprimiram um grande conjunto de dados de variante de tempo até alcançar uma estrutura de dados menor, enquanto isso, a árvore fp se manteve distante do conjunto de dados repetitivos, o que finalmente gerou uma vantagem considerável em tempo e uso de memória.Originalidade: na atualidade, a extração de padrões recorrentes de alta utilidade é uma das áreas de estudo mais desenvolvidas na mineração de informação com respeito à variável temporal, devido a sua capacidade de dar conta da frequência dos conjuntos de elementos e das taxas de serviços vários de cada conjunto de elementos. Esta pesquisa ajuda a manter o estudo desse tema em um nível avançado, o que garante evitar gerar uma grande quantidade de conjuntos possíveis e, dessa forma, um maior desenvolvimento em um menor tempo de execução e espaço de busca.Limitações: os resultados da pesquisa demonstraram que a aproximação foi eficiente em conjuntos de dados provados com cálculos predefinidos de peso e utilidade

Optimal Specially Structured N X 2 Flow Shop Scheduling to Minimize Total Waiting Time of Jobs Including Job Block Concept with Processing Time Separated From Set up Time

In the present state of affairs the current engineering and manufacturing built- up units are facing mishmash of problems in a lot of aspects such as man power, machining time, raw material, electricity and customer’s constraints. The flow-shop scheduling is one of the most significant manufacturing behaviors particularly in manufacturing planning. The creation of every time admirable schedules has verified to be enormously complicated. This paper involves the fortitude of the order of processing of m jobs on 2 machines. This paper proposes the specially structured Flow Shop Scheduling problem separated from set up time assuming that maximum of the equivalent processing time on first machine is less than or equal to the minimum of equivalent processing time on second machine with the objective of getting the optimal sequence of jobs for total waiting time of jobs using the heuristic algorithm by taking two of the jobs as a group job. The proposed technique is followed by numerical example

Kimura’s disease: a diagnostic and therapeutic enigma

Kimura’s disease is a rare chronic inflammatory disorder present in 2nd and 3rd decade. It has a predilection for head and neck region presenting as a slowly growing painless swelling. It is usually accompanied by peripheral eosinophilia and elevated serum IgE and hence it was initially thought to be of allergic origin. Histologically the lesions are characterized by reactive hyperplasia of lymph nodes, eosinophilic infiltration and increase in postcapillary venules. Authors have reported a male patient with a slowly growing right sided neck swelling which is recurring even after course of steroids and excision done twice at an interval of 6 months. Kimura’s disease although a benign Lymphoid disorder but the incidence of recurrence despite taking treatment is a cause of much concern for the patient

Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related meta-information, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/3

Sitopaladi Churna As Anti-Tussive: A Review

“SitopaladiChurna” is an Ayurvedic preparation used as an antitussive, analgesic and antipyretic. The present study reports on the anti-tussive activity of the macerated extract of SitopaladiChurna by comparison to other marketed formulations as well as reference drug Codeine phosphate using the acetic acid induced cough models in guinea pigs. The percentage inhibition of bouts of cough for in-house formulation was very significant compared to the standard as well as other marketed formulations. Thus the present study justifies the traditional claims of SitopaladiChurna in the treatment of cough

Epidemiological characteristics of deceased of Influenza A in a tertiary care hospital at Ajmer, Rajasthan, India

Background: Influenza A (H1N1) is a novel strain of the Influenza virus and is widely known as Swine Flu. It causes a respiratory illness and is very contagious. Symptoms of Influenza A (H1N1) are similar to those of the seasonal flu. First laboratory confirmed Influenza A (H1N1) case of India was reported from Hyderabad on 16th May 2009.Methods: The study was retrospective descriptive, record-based study and carried in deceased of Influenza A (H1N1) who were admitted in Jawaharlal Nehru Medical College and Hospital, Ajmer, Rajasthan, India from 01 January 2016 to 31 December 2018.Results: A total 1524 suspected cases were screened for Influenza A (H1N1) at JLN Medical College and Hospital, Ajmer during the period from 1st January 2016 to 31st December 2018. 294 cases were diagnosed as Influenza A (H1N1) positive and 44 died. Majority 25 (56.81%) were belonged to rural area and 30 (68.18%) females died as compare to 14 (31.18%) male.Conclusions: Incidence and mortality is common throughout the year but it more in winter months. Mortality is high in age group of 50 years and above, females and rural area. IEC activities should be throughout year

Role of the actin cytoskeleton in regulating the outer phosphatidylethanolamine levels in yeast plasma membrane

Transbilayer phosphatidylethanolamine (PtdEtn) movements in the plasma membrane of Saccharomyces cerevisiae are regulated by an ATP-dependent, protein-mediated process(es). To examine whether this process is influenced by the actin cytoskeleton, we have studied the PtdEtn translocation in S. cerevisiae cells after treatment with microfilament disrupting and microtubule-disrupting agents. PtdEtn translocation was studied by measuring the external PtdEtn levels, using fluorescamine as the external membrane probe, in the ATP-depleted, ATP-depleted and repleted, and N-ethylmaleimide-treated cells. The microfilaments and microtubules were disrupted by treatment with various cytochalasins and colchicine (or benomyl) respectively PtdEtn translocation became abnormal in the cytochalasin-treated cells but not in cells that were treated with microtubule-disrupting agents, such as colchicine or benomyl. These results have been interpreted to suggest that the actin cytoskeleton is involved in regulating the PtdEtn translocase activity in the yeast cell plasma membrane