Cytotoxic and antibacterial activity of the mixture of olive oil and lime cream in vitro conditions

The mixture of olive oil and lime cream has been traditionally used to treat external burns in the region of Hatay/Antakya and middle Anatolia. Olive oil and lime cream have been employed by many physicians to treat many ailments in the past. A limited number of studies have shown the antibacterial effect of olive oil and that it does not have any toxic effect on the skin. But we did not find any reported studies on the mixture of olive oil and lime cream. The aim of this paper is to investigate the cytotoxic and antibacterial activity of olive oil and lime cream individually or/and in combination in vitro conditions, by using disk-diffusion method and in cell culture. The main purpose in using this mixture is usually to clear burns without a trace. Agar overlay, MTT (Cytotoxicity assay) and antibacterial susceptibility tests were used to investigate the cytotoxic and antibacterial activity of olive oil and lime cream. We found that lime cream has an antibacterial activity but also cytotoxic on the fibroblasts. On the other hand olive oil has limited or no antibacterial effect and it has little or no cytotoxic on the fibroblasts. When we combined lime cream and olive oil, olive oil reduced its cytotoxic impact. These results suggest that mixture of olive oil and lime cream is not cytotoxic and has antimicrobial activity.Keywords: Olive oil, lime cream, burn, in vitro, cytotoxic activity, antibacterial activit

Correction: Effects of droplet size and surfactants on anchoring in liquid crystal nanodroplets

Correction for ‘Effects of droplet size and surfactants on anchoring in liquid crystal nanodroplets’ by Zeynep Sumer et al., Soft Matter, 2019, 15, 3914–3922, DOI: 10.1039/C9SM00291J

Nanoparticles shape-specific emergent behaviour on liquid crystal droplets

Self-assembly attracts enormous research attention because it is at the core of important applications ranging from medical treatments to renewable energy production. Among several classes of self-assembling materials, liquid crystals (LCs) and nanoparticles yield ordered structures under well-defined thermodynamic conditions and could yield supra-molecular aggregates, respectively. In this work, nanoparticle self-assembly on LC nano-droplets is investigated. The LC nano-droplets act as templating agents on which homogeneous and Janus nanoparticles of various geometrical features are adsorbed. LC mesogens and water have low mutual solubility, and under the conditions chosen the LCs yield bipolar nano-droplets. Particle self-assembly on oil nano-droplets is also considered for comparison. Our results reveal that the mesogens can direct the assembly of the nanoparticles. This effect is mainly governed by the nanoparticle size and shape. In some cases, strong evidence of emergent behaviour is observed depending on entropic forces that arise because of the shape and patchiness of the nanoparticles. For example, while one small spherical homogeneous particle does not show preferential adsorption on specific LC nano-droplet locations, 100 spherical nanoparticles preferentially agglomerate at the nano-droplet boojums, providing evidence of emergent behaviour. On the contrary, Janus spherical nanoparticles do not show such a strong emergent behaviour. Cylindrical NPs manifest the opposite trend: while homogeneous nano-cylinders do not exhibit orientational order on the LC nano-droplet, Janus ones either locate at the LC nano-droplet boojums or orient towards the direction vector of bipolar droplets. Quantification of the orientational order within the LC nano-droplets suggests that the self-assembly of the LC mesogens does not significantly change upon nanoparticle adsorption. These simulations clearly suggest an interplay between nanoparticle size, shape and chemical composition upon their self-assembly on LC nano-droplets. The results could be helpful for the design of new sensors and for the directed self-assembly of advanced materials

Liquid crystal droplets under extreme confinement probed by a multiscale simulation approach

In this work, we computationally investigate liquid crystal (LC) droplets in the size range 0.03–1 μm, confined within shells of combined anchoring conditions. Two different types of surface were defined to promote homeotropic and planar degenerate anchoring, respectively. We identified the LC behaviour within the nanoscale droplets using a bespoke multiscale simulation approach. To study 30 nm droplets, we used coarse grained simulations within the dissipative particle dynamics formalism; to study 0.1 μm and larger droplets, we used a finite element method based on the Landau–de Gennes theory. Good agreement between the two methods was observed in our prior analysis and was confirmed in the present work. We explicitly study droplets of size 0.1 and 1 μm by using continuum mechanics calculations. Our results for the largest droplet are consistent with those available in the literature, suggesting that the extension to smaller droplets presented here is realistic, and therefore can be helpful for innovations in which device intensification could be achieved using LC nanodroplets

Engineered liquid crystal nano droplets: insights from multi-scale simulations

Liquid crystal (LC) droplets have been investigated for a wide range of applications, from displays to sensors. Over the years, a need has arisen for complete understanding of the behaviour of LCs in droplets under different conditions for the development of advanced devices, for which accurate modelling is necessary. We show here, for the first time, both qualitative and quantitative agreement between coarse-grained molecular models and Q-tensor theory calculations for liquid crystal (LC) droplets. The approach is demonstrated for two types of droplet surfaces, which possess strong planar degenerate and strong homeotropic anchoring, respectively. Once its reliability has been proven, our approach was used to identify defects due to changes in anchoring in a small region on the LC droplet surface, which could be triggered, for example, by the adsorption of a nano-particle or a protein. Both coarse-grained simulations and Q-tensor analysis show the appearance of defects in well-determined locations within the LC droplet, albeit sometimes affected by degeneracy due to the symmetry of the systems being investigated. These results suggest the possibility of using LC droplets, in the future, as platforms for advanced sensing as well as for signal intensification

Inferior alveolar nerve paresthesia caused by a dentigerous cyst associated with three teeth

The dentigerous cyst is a common pathologic entity associated with an impacted tooth, usually third molars. They generally are asymptomatic, being found on routine dental radiographic examination. This report describes the case of a 43 year old male with a large dentigerous cyst associated with mandibular canine, first and second premolar teeth that caused paresthesia of the inferior alveolar nerve

Effects of droplet size and surfactants on anchoring in liquid crystal nanodroplets

Liquid crystal (LC) droplets attract scientific attention for many advanced applications, including, but not limited to optical and sensing devices. To aid experimental advancements, theoretical calculations have been conducted to quantify molecular driving forces responsible for the collective behaviour of LC molecules within micrometer-size spherical droplets. To quantify the LC molecular anchoring within spherical physical constraints, molecular simulations at atomistic resolution would be useful. In an attempt to bridge the gap between computational capabilities and experimental interest, coarse-grained simulations are used here to study nematic LC nanodroplets dispersed in water. A LC phase diagram is generated as a function of droplet size and temperature. The effect of adding surfactants on LC anchoring was quantified, considering surfactants of different molecular features. When few surfactants are present, they self-assemble at the droplet boojums regardless of their molecular features. All surfactants tested shifted LC orientation from bipolar to uniaxial. When the surfactants have a hydrophobic tail of sufficient length, they cause deviations from the spherical symmetry of LC droplets. Increasing the concentration of these surfactants enhances such phenomenon. Simulations were also conducted to assess the ability of the surfactants to prevent the agglomeration between two LC droplets. The results showed that coalescence was inevitable at all conditions and suggested that large enough surfactant concentrations can delay the phenomenon. The results presented could be helpful for designing novel surface-active compounds to develop optical and/or sensing devices at conditions in which mutual solubility between water and LCs is low

Manipulating molecular order in nematic liquid crystal capillary bridges via surfactant adsorption: guiding principles from dissipative particle dynamics simulations

The ability of liquid crystals (LCs) to change orientational order is used in applications, ranging from sensors to displays. The aim of this work is to computationally investigate how surfactant adsorption on cylindrical LC bridges can be used to control such orientational order. Building from classical fundamental lessons, understanding the ordering of mesogens along a preferred axis with the help of molecular modelling contributes to investigations of systems that could be a platform for LC-based sensing applications. The coarse-grained dissipative particle dynamics (DPD) simulation method is implemented here, because it allows us to quantify the effect of molecular features on the properties of meso-scopic systems containing LC bridges, an aqueous solvent, and surfactants at various concentrations. Three surfactant types are modelled with short, medium, and long tail lengths, respectively. All surfactants adsorb at the LC-water interface. It is found that the length of the surfactant hydrophobic tail determines the effectiveness by which the LC order is affected. Short tails are not as effective as long ones. Surfactants with long tails affect the LC order, but, in agreement with experiments, predominantly only within a short distance from the LC-water interface. For these surfactants, the surface density at the LC-water interface is an important knob that can be used to control the order of the LCs. As the effective LC-surfactant interactions change, so does the distribution of the surfactants at the interface. Consistent with theoretical expectations, the results presented here elucidate the effect of molecular features on the anchoring mechanism between surfactants and mesogens within cylindrical bridges dispersed in aqueous systems and could be helpful for designing novel surface-active compounds in the development of advanced sensing devices based on LCs

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types