Development,optimization, and characterization of a novel tea tree oil nanogel using response surface methodology

Purpose: To develop and optimize nanoemulsion (NE)-based emulgel (EG) formulation as a potential vehicle for topical delivery of tea tree oil (TTO).

Methodology: Central composite design was adopted for optimizing the processing conditions for NE preparation by high energy emulsification method viz. surfactant concentration, co-surfactant concentration, and stirring speed. The optimized NE was developed into emulgel (EG) using pH sensitive polymer Carbopol 940 and triethanolamine as alkalizer. The prepared EG was evaluated for its pH, viscosity, and texture parameters, ex vivo permeation at 37?°C and stability. Antimicrobial evaluation of EG in comparison to conventional gel and pure TTO was also carried out against selected microbial strains.

Results and discussion: Optimized NE had particle size and zeta potential of 16.23?±?0.411?nm and 36.11?±?1.234?mV, respectively. TEM analysis revealed the spherical shape of droplets. The pH of EG (5.57?±?0.05?) was found to be in accordance with the range of human skin pH. EG also illustrated efficient permeation (79.58?μL/cm²) and flux value (J_SS) of 7.96?μL cm²/h through skin in 10?h. Viscosity and texture parameters, firmness (9.3?±?0.08?g), spreadability (2.26?±?0.06?mJ), extrudability (61.6?±?0.05?mJ), and adhesiveness (8.66?±?0.08?g) depict its suitability for topical application. Antimicrobial evaluation of EG with same amount of TTO as conventional gel revealed broader zones of growth inhibitions against all the selected microbial strains. Moreover, EG was also found to be nonirritant (PII 0.0833). These parameters were consistent over 90 d.

Conclusion: TTO EG turned out to be a promising vehicle for the topical delivery of TTO with enhanced therapeutic efficacy.     

Gender-related differences in susceptibility of A/J mouse to benzo[a]pyrene-induced pulmonary and forestomach tumorigenesis

A commercial patient dose verification system utilizing non-invasive metal oxide semiconductor field effect transistor (MOSFET) dosimeters originally designed for radiotherapy applications has been evaluated for use at diagnostic energy levels. The system features multiple dosimeters that may be used to monitor entrance or exit skin dose and intracavity doses in phantoms in real time. We have characterized both the standard MOSFET dosimeter designed for radiotherapy dose verification and a newly developed "high sensitivity" MOSFET dosimeter designed for lower dose measurements. The sensitivity, linearity, angular response, post-exposure response, and physical characteristics were evaluated. The average sensitivity (free in air, including backscatter) of the radiotherapy MOSFET dosimeters ranged from 3.55 x 10(4) mV per C kg(-1) (9.2 mV R(-1)) to 4.87 x 10(4) mV per C kg(-1) (12.6 mV R(-1)) depending on the energy of the x-ray field. The sensitivity of the "high sensitivity" MOSFET dosimeters ranged from 1.15 x 10(5) mV per C kg(-1) (29.7 mV R(-1)) to 1.38 x 10(5) mV per C kg(-1) (35.7 mV R(-1)) depending on the energy of the x-ray field. The high sensitivity dosimeters demonstrated excellent linearity at high energies (90 and 120 kVp) and acceptable linearity at lower energies (60 kVp). The angular response was significant for free-in-air exposures, as illustrated by the sensitivity differences between the two sides of the dosimeter, but was excellent for measurements within a tissue equivalent cylinder. The post-exposure drift response is a complicated but reproducible function of time. Real-time monitoring requires little if any corrections for the post-exposure drift response. The MOSFET dosimeter system brings some unique capabilities to diagnostic radiology dosimetry including small size, real-time capabilities, nondestructive measurement, good linearity, and a predictable angular response.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

Anonymizing bipartite graph data using safe groupings

Private data often come in the form of associations between entities, such as customers and products bought from a pharmacy, which are naturally represented in the form of a large, sparse bipartite graph. As with tabular data, it is desirable to be able to publish anonymized versions of such data, to allow others to perform ad hoc analysis of aggregate graph properties. However, existing tabular anonymization techniques do not give useful or meaningful results when applied to graphs: small changes or masking of the edge structure can radically change aggregate graph properties. We introduce a new family of anonymizations for bipartite graph data, called (k, ℓ)-groupings. These groupings preserve the underlying graph structure perfectly, and instead anonymize the mapping from entities to nodes of the graph. We identify a class of “safe” (k, ℓ)-groupings that have provable guarantees to resist a variety of attacks, and show how to find such safe groupings. We perform experiments on real bipartite graph data to study the utility of the anonymized version, and the impact of publishing alternate groupings of the same graph data. Our experiments demonstrate that (k, ℓ)-groupings offer strong tradeoffs between privacy and utility.     

Diffusion of fluid confined to nanotube with rectangular cross section

A dynamical model is proposed to study self-diffusion coefficient by confining the fluid in rectangular nanotube. The theoretical model is based on the consideration that the confinement affects the movement at atomic level. The model predicts that the diffusion parallel to walls of channel is different from that of diffusion perpendicular to the walls. Near the walls the dynamics of fluid has been found to slow down to an extent that below a certain value of ratio of width to the diameter of particle, the molecules behave as if these belong to solid. The results are contrasted with the result obtained from the model based on similar considerations for a fluid confined only in one direction. It is found that tendency of freezing near the wall increases due to confinement from second direction. Empirical relation which governs the behavior of diffusion coefficient as function of distance from the confining walls has also been proposed. The effect of confinement is more pronounced for denser fluids than for dilute fluid.     

Skyline and mapping aware join query evaluation

Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on” on top of a traditional Select-Project-Join query plan. In many real-world applications, the skyline dimensions can be anti-correlated such as the attribute pair {price, mileage} for cars and {price, distance} for hotels. Anti-correlated data are particularly challenging for skyline evaluation and therefore have commonly been ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anti-correlated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local within individual data sources and across disparate sources—to significantly reduce the above-mentioned costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions.