What decides: high mortality in paediatric acute renal failure?

Forty paediatric cases of A.R.F. (Acute Renal Failure) of various aetiology were included in the study. 60% of patients were less than 4 years of age with male predominance. 80% cases reported to us very late with oligoanuria of more than 24 hours (2-7 days). Diarrhoea, vomiting and fever were other dominant symptoms. Maximum cases were severely anaemic (87.5%) with mean Hb 7.73 +/- 1.9 gm%. 40% cases were of underweight while only one case (2.5%) was of over weight, inspite of volume excess in 40% cases. All 24 cases, who were estimated for serum albumin, found to have marked hypoalbuminemia. Mortality was found to be as high as 65% inspite of effective peritoneal dialysis in all cases. High mortality seems to be due to profound anuria of many days (because of marked delay in reaching the hospital), fever and malnutrition besides other factors as aetiology.     

Melt intercalation/exfoliation of polystyrene-sodium-montmorillonite nanocomposites using sulfonated polystyrene ionomer compatibilizers

Quaternary ammonium salts of sulfonated polystyrene (SPS) were used as compatibilizers for melt intercalation of PS and pristine Na-montmorillonite. Tetra-octyl ammonium SPS and tetra-decyl ammonium SPS ionomeric compatibilizers produced significant exfoliation and a homogeneous dispersion of the polymer-clay nanocomposites. Wide angle X-ray diffraction and transmission electron microscopy were primarily used to characterize the morphology of the nanocomposites. Image analysis was used to measure the percentage exfoliation. Exfoliation increased with the increasing length of the alkyl chain of the ammonium counter-ion of the SPS ionomer. The nanocomposites containing ionomers exhibited higher storage moduli compared to nanocomposites without the compatibilizer.     

Esterification of acrylic acid with methanol by reactive chromatography: Experiments and simulations

The esterification of acrylic acid with methanol using Amberlyst 15 as a stationary phase has been investigated using a chromatographic reactor. Several experimental runs at various operating conditions have been conducted on a batch column. A classical reactive chromatography model including lumped kinetics, a linear driving force transport model and a heterogeneous kinetic model for the catalytic reaction has been developed. The additional dispersion of concentration fronts due to density gradient effects has been accounted for in the model. The model parameters have been determined in a fast and reliable way by directly fitting the batch column experiments. In general, a good agreement between experimental and calculated results is obtained. The evaluation of the covariance of the fitted model parameters reveals important insights about the system behavior.Based on the detailed batch column model, a complete model of a simulated-moving-bed reactor has been implemented and its optimal point of operation for the synthesis of methyl acrylate from acrylic acid has been determined. Particularly when considering the low-operating temperature, we can regard this process as a possible competition for current technologies.     

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

Wireless Personal Communications - This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR)...     

Effect of the polyelectrolyte coating on the photothermal efficiency of gold nanorods and the photothermal induced cancer cell damage

Coating gold nanorods (GNRs) with polyelectrolytes is an effective approach to make them biocompatible for potential use in photothermal treatment (PTT) of cancer. The authors report the effect of coating of the GNRs with polystyrene sulphonate (PSS‐GNRs) and PSS plus poly di‐allyl di‐methyl ammonium chloride (PDDAC‐GNRs) on its photothermal conversion efficiency (PTE), cellular uptake and subsequently the photothermal induced cytotoxicity in human oral cancer cells (NT8e). Coating of GNRs with PSS led to decrease in PTE by ∼30% and further coating it with PDDAC led to its increase to similar level, with respect to as‐ prepared GNRs. The cellular uptake of PDDAC‐GNRs in cancer cells was double than that for PSS‐GNRs. PTT of cancer cells after treatment with 60 pM of either PDDAC‐GNRs or PSS‐GNRs resulted in cytotoxicty of ∼90%. At higher concentration of 120 pM, while PSS‐GNRs showed no further change, for PDDAC‐GNR the photothermal induced cytotoxicity decreased to ∼50%. The broadening of longitudinal surface plasmon peak of PDDAC‐GNRs and appearance of dark clusters in cells under bright‐field microscope suggested intracellular clustering of PDDAC‐GNRs. In conclusion, despite high PTE and cellular uptake of PDDAC‐GNRs, its intracellular clustering (due to acidic pH) adversely affect the PTT of cancer cells.Inspec keywords: polymer electrolytes, gold, nanorods, cancer, nanomedicine, cellular biophysics, toxicology, radiation therapy, polymer filmsOther keywords: polyelectrolyte coating, photothermal efficiency, gold nanorods, photothermal induced cancer cell damage, cancer photothermal treatment, polystyrene sulphonate, poly di‐allyl di‐methyl ammonium chloride, photothermal conversion efficiency, cellular uptake, photothermal‐induced cytotoxicity, human oral cancer cells, dark clusters, bright‐field microscope, PDDAC‐GNR intracellular clustering, acidic pH, intracellular compartment, Au     

Self-nanoemulsifying drug delivery system of docosahexanoic acid: development,in vitro,in vivo characterization

Context: Docosahexanoic acid (DHA) is an essential omega-3 fatty acid for normal brain development and its use has increased considerably in recent years.

Objective: The aim of this study is to develop and evaluate self-nanoemulsifying drug delivery systems (SNEDDS) of DHA for improved palatability, dispersibility and bioavailability.

Methods: The SNEDDS were prepared and evaluated for miscibility, employing different combinations of olive oil and soyabean oil as oil phase, Span 80, Span 20, soya phosphatidylcholine, Labrafil M 1944 CS as surfactants while Tween 80, PEG 400, Cremophor RH40 and propylene glycol as cosurfactants. Thermodynamically stable SNEDDS were characterized for dispersibility, self-emulsification time, droplet size, zeta potential along with sensory analysis. The optimized formulation was subjected to ex vivo and in vivo evaluation such as intestinal permeability, memory performance test, brain concentration and histopathology studies.

Results: The optimized SNEDDS formulation showed emulsification time of 27?±?4.7?s with droplet size of 17.6?±?3.5?nm and zeta potential of??37.6?±?0.5?mV. Intestinal absorption study depicted 18.3%, 21.5%, 41.5%, 98.7% absorption of DHA with SNEDDS-based formulation in comparison to 8.2%, 15.1%, 28.8%, 46.1% absorption of DHA with oil-based marketed formulation after 0.5, 1, 2 and 4?h. DHA concentration in brain homogenate was found to be increased to 2.6-fold in comparison to DHA-marketed formulation. This could be ascribed to enhanced dispersibility and bioavailability of DHA from nanosized formulation.

Conclusion: The developed formulation led to enhanced dispersibility and bioavailability of DHA due to the formation of nanodroplets.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Speed scaling with a solar cell

We consider the setting of a device that obtains its energy from a battery and some regenerative source such as a solar cell. We consider the speed scaling problem of scheduling a collection of tasks with release times, deadlines, and sizes, so as to minimize the energy recharge rate of the regenerative source. This is the first theoretical investigation of speed scaling for devices with a regenerative energy source. We show that the problem can be expressed as a polynomial sized convex program. We show that, using the KKT conditions, one can obtain an efficient algorithm to verify the optimality of a schedule. We show that the energy optimal YDS schedule is 2-approximate with respect to the recharge rate. We show that the online algorithm BKP is O(1)$O\left(1\right)$-competitive with respect to recharge rate.