Tunnelling Effect and Hawking Radiation from a Vaidya Black Hole

In this paper, we extend Parikh' work to the non-stationary black hole. As an example of the non-stationary black hole, we study the tunnelling effect and Hawking radiation from a Vaidya black hole whose Bondi mass is identical to its mass parameter. We view Hawking radiation as a tunnelling process across the event horizon and calculate the tunnelling probability. We find that the result is different from Parikh's work because $\frac{dr_{H}}{dv}$ is the function of Bondi mass m(v)

Patient-controlled analgesia improves pain control in a vaso-occlusive crisis in sickle cell patients

A critical appraisal and clinical application of van Beers EJ, van Tuijn CF, Nieuwkerk PT, Friederich PW, Vranken JH, Biemond BJ. Patient-controlled analgesia versus continuous infusion of morphine during vaso-occlusive crisis in sickle cell disease, a randomized controlled trial. Am J Hematol. 2007;82(11):955-60. doi: 10.1002/ajh.2094

PREPARATION, CHARACTERIZATION, AND OPTIMIZATION OF MEBENDAZOLE SPHERICAL AGGLOMERATES USING MODIFIED EVAPORATIVE PRECIPITATION IN AQUEOUS SOLUTION (EPAS)

Objective: Mebendazole is a popular benzimidazole class anthelmintic drug useful in the treatment of main infections of threadworms as well as other less common worm infections like whipworm, roundworm, and hookworm in adults and children over 2 y of age. It is poorly soluble in water resulting in poor absorption from the intestinal tract leading to a decrease in bioavailability. Moreover, Mebendazole has poor flowability due to the needle-shaped crystals. This work was carried out with the aim of increasing the flowability and solubility of Mebendazole. Methods: A 32 full factorial design was used to investigate the effect of the concentration of Mebendazole and the quantity of water as an external phase using evaporative precipitation into an aqueous solution. The prepared agglomerates were characterized for particle size distribution, shape, Hausner ratio, Carr’s index and % dissolved in 60 min (C60).   Results: The prepared agglomerates were found to be monodispersed. They also showed a decrease in the Hausner ration and Carr’s index, indicating improved flowability. Increase in C60 indicated that the agglomerates were found to have increased water solubility. Conclusion: Scanning Electron Microscopy showed that the agglomerates were spherical in shape. Fourier Transformed Infra-Red studies showed no chemical change in the prepared spherical agglomerates. Differential Scanning Calorimetry and X-ray diffraction studies showed an increase in amorphous characteristics of prepared spherical agglomerates. This method may be used for drugs with similar characteristics as Mebendazole

From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice

FORMULATION DEVELOPMENT AND EVALUATION OF TEMOZOLOMIDE LOADED HYDROGENATED SOYA PHOSPHATIDYLCHOLINE LIPOSOMES FOR THE TREATMENT OF BRAIN CANCER

Objective: The objective of this study was to encapsulate temozolomide (TMZ) in the liposomal formulation for the treatment of glioblastoma. TMZis one of the most effective substances in vitro against cells derived from glioblastoma. However, it may not have a significant effect in vivo due topoor penetration in brain which may be attributed to the blood-brain-barrier. The main objective of this investigation is to develop a liposomal drugdelivery system which could improve the brain targeting, and solve the treatment-related problems.Methods: In this study, TMZ loaded liposomes were prepared by ethanol injection method. The characterization of formulated liposomes was carriedout by vesicle size, entrapment efficiency, surface morphology, and in vitro drug release study. The prepared liposomes were also evaluated for celluptake and cell cytotoxicity studies.Results: Particle size and entrapment efficiency were found to be 105.7±3.9 nm and 78.25±0.98%, respectively. 75% of the entrapped drug wasreleased in 24 hrs from the selected liposomal formulation. Cell uptake study reveals that hydrogenated soya phosphatidylcholine (HSPC) loaded TMZliposomes interact with the glioblastoma cells and kill the cancer cells effectively. Cytotoxicity assay confirms that drug loaded HSPC liposomes aremore efficient with respect to killing of glioblastoma cells as compared to plain drug.Conclusion: These results suggest that the TMZ loaded HSPC liposome may serve as a proficient targeted drug delivery system for the effectivemanagement of glioblastoma.Keywords: Temozolomide, Liposomes, Hydrogenated soya phosphatidylcholine, Cholesterol, Glioblastoma

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data

We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subnetworks, as modeled by rank one outer products. The subnetworks are combined using patient specific non-negative coefficients; these coefficients are also used to model, and subsequently predict the clinical severity of a given patient via a linear regression. Our generative-discriminative framework is able to exploit the structure of rs-fMRI correlation matrices to capture group level effects, while simultaneously accounting for patient variability. We employ ten fold cross validation to demonstrate the predictive power of our model on a cohort of fifty eight patients diagnosed with Autism Spectrum Disorder. Our method outperforms classical semi-supervised frameworks, which perform dimensionality reduction on the correlation features followed by non-linear regression to predict the clinical scores