Factors associated with increased survival after surgical resection of glioblastoma in octogenarians.

Elderly patients with glioblastoma represent a clinical challenge for neurosurgeons and oncologists. The data available on outcomes of patients greater than 80 undergoing resection is limited. In this study, factors linked to increased survival in patients over the age of 80 were analyzed. A retrospective chart review of all patients over the age of 80 with a new diagnosis of glioblastoma and who underwent surgical resection with intent for maximal resection were examined. Patients who had only stereotactic biopsies were excluded. Immunohistochemical expression of oncogenic drivers (p53, EGFR, IDH-1) and a marker of cell proliferation (Ki-67 index) performed upon routine neuropathological examination were recorded. Stepwise logistic regression and Kaplan Meier survival curves were plotted to determine correlations to overall survival. Fifty-eight patients fit inclusion criteria with a mean age of 83 (range 80-93 years). The overall median survival was 4.2 months. There was a statistically significant correlation between Karnofsky Performance Status (KPS) and overall survival (P < 0.05). There was a significantly longer survival among patients undergoing either radiation alone or radiation and chemotherapy compared to those who underwent no postoperative adjuvant therapy (p < 0.05). There was also an association between overall survival and lack of p53 expression (p < 0.001) and lack of EGFR expression (p <0.05). In this very elderly population, overall survival advantage was conferred to those with higher preoperative KPS, postoperative adjuvant therapy, and lack of protein expression of EGFR and p53. These findings may be useful in clinical decision analysis for management of patients with glioblastoma who are octogenarians, and also validate the critical role of EGFR and p53 expression in oncogenesis, particularly with advancing age

Studies on Degradation of Reactive Red 135 Dye in Wastewater using Ozone

AbstractAn attempt has been made in this paper to identify effects of efficient ozonation processes for decolorization and/or degradation of organic pollutants for environmental protection. In dyes, since the chromosphere groups with conjugated double bonds, which are responsible for color, can be broken down by ozone either directly or indirectly forming smaller molecules, thereby decreasing the color of the effluents. Ozonation and its combinations are effective for wastewater treatment. In the present study 99.9% decolorization of RR 135 was accomplished by ozonation. The time required for the complete decolorization gradually increase with increase in the initial dye concentration. For RR135 after 48, 55 and 67min reaction time, complete decolorizaton was achieved at initial dye concentrations of 500, 1000 and 1500mg/L, respectively. The COD removal efficiency gradually decreased with an increase in the initial concentration. For RR135 after 60, 65 and 87min reaction time, the COD removal percentage was 66.66%, 61% and 56.64% at initial dye concentrations of 500, 1000 and 1500mg/L, respectively

Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning

Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning

RECOMBINANT EXPRESSION AND FUNCTIONAL CHARACTERIZATION OF ANTIMICROBIAL PEPTIDE CRUSTIN FROM ARTEMIA SALINA

Universidad Nacional Agraria La Molina. Escuela de Posgrado. Maestría en Ciencias AmbientalesSe estudiaron las características físicas de los residuos sólidos domiciliarios como la generación per cápita (GPC), densidad (kg/m3) y composición física, a nivel de 519 distritos pertenecientes a las 25 regiones del Perú, para determinar indicadores específicos para el Perú y su relación con factores socioeconómicos y geográficos. Las variables independientes fueron el gasto per cápita familiar (GsPC), índice de desarrollo humano (IDH), índice de pobreza total (IPT), necesidades básicas insatisfechas (NBI) y coeficiente de desigualdad (GINI); mientras que las variable dependientes fueron la GPC, densidad y composición física de los residuos sólidos domiciliarios. Se utilizaron datos provenientes de estudios de caracterización de residuos sólidos y la base de datos actualizada al 2014 del Sistema de Información para la Gestión de Residuos Sólidos del Ministerio del Ambiente (SIGERSOL). Los valores de la GPC fueron trabajados sin actualizar y actualizados al 2015, encontrándose que la GPC promedio ponderada nacional es de 0.577 kg/hab/día y la región natural selva es la que presenta mayores valores de GPC. A nivel espacial los valores de la GPC se agrupan en algunos casos siguiendo un patrón geográfico de región natural. Para el caso de la densidad de los residuos sólidos, esta fue mucho mayor en la región selva (233.985 kg/m3), que es la que además presenta la mayor cantidad de materia orgánica en sus residuos, diferenciándose significativamente de las otras dos regiones naturales. Con respecto a la relación entre la GPC y los factores socioeconómicos, se observó que existe una relación más marcada con el gasto per cápita familiar (GsPC), aunque estadísticamente los coeficientes de determinación y correlación no eran fuertes. Por último, la tasa de crecimiento de la GPC se encontró en el rango de 0.263 % a 14.741% dependiendo del crecimiento poblacional y el ingreso económico de los habitantes.Physical characteristics such as household solid waste generation per capita, density (kg/m3) and physical composition of 519 districts within 25 regions of Peru were studied to determine specific indicators for Peru and its relationship with socioeconomic and geographic factors. The independent variables were the household per capita expenditure, human development index, total poverty index, unsatisfied basic needs and coefficient of inequality; while the dependent variables were the household solid waste generation per capita, density and physical composition of solid household waste. Data from studies of characterization of solid waste and the database updated 2014 from Information System for Solid Waste Management of Ministry of Environment were used. Household solid waste generation per capita values were worked without updating and updated in 2015, finding that the per capita generation of solid waste is 0.577 kg/person/day and the jungle region has the higher value of per capita generation. Spatially, per capita generation values are grouped in some cases following a natural geographical pattern region. In the case of the density of solid waste, this was much higher in the jungle region (233.985 kg/m3), which is the one featuring the largest amount of organic matter in waste, significantly from the other two natural regions differing. Regarding the relationship between the per capita generation of solid waste and socioeconomic factors, it was observed that her is a stronger relationship with the family per capita spending, although statistically the coefficients of determination and correlation were not strong. Finally, the growth rate of per capita generation was found in the range of 0.263 % to 14.741 %, depending on the population growth and the income of the inhabitants.Tesi

Predicting Noise From Aircraft Turbine-Engine Combustors

COMBUSTOR and CNOISE are computer codes that predict far-field noise that originates in the combustors of modern aircraft turbine engines -- especially modern, low-gaseous-emission engines, the combustors of which sometimes generate several decibels more noise than do the combustors of older turbine engines. COMBUSTOR implements an empirical model of combustor noise derived from correlations between engine-noise data and operational and geometric parameters, and was developed from databases of measurements of acoustic emissions of engines. CNOISE implements an analytical and computational model of the propagation of combustor temperature fluctuations (hot spots) through downstream turbine stages. Such hot spots are known to give rise to far-field noise. CNOISE is expected to be helpful in determining why low-emission combustors are sometimes noisier than older ones, to provide guidance for refining the empirical correlation model embodied in the COMBUSTOR code, and to provide insight on how to vary downstream turbinestage geometry to reduce the contribution of hot spots to far-field noise

Chronic non-bacterial osteomyelitis of distal ulna in a paediatric patient: a case report and review of literature

Chronic non-bacterial osteomyelitis (CNO) is a rare disorder characterized by focal aseptic inflammation with a self-limiting, relapsing course of disease with female predominance and usually observed in paediatric age groups diagnosed by clinical, radiological and histopathological findings after ruling out all other differentials. We reported this rare case found in a 7 year old female with a history of greenstick fracture of distal ulna 3 years ago with flaring up of pain and swelling since trivial trauma to left wrist 1 month ago. We emphasize on the relevant data and findings to reach the conclusive diagnosis and treatment of the disease. CNO can present as benign unifocal non-relapsing to more severe form of multifocal relapsing inflammatory lesions involving metaphysis of long bones, vertebrae, clavicle and mandible. Diagnosis is made after excluding infection, malignancy, auto-immune and metabolic disorders. Treatment is mainly empirical to reduce pain and inflammation through NSAIDs, corticosteroids, bisphosphonates and sometimes TNF alpha inhibitors as no proper guidelines are available