Biomechanical and morphological analysis of the cells is a novel approach for monitoring the environmental features, drugs, and toxic compounds’ effects on cells. Graphene oxide (GO) has a broad range of medical applications such as tissue engineering and drug delivery. However, the effects of GO nanosheets on biological systems have not been completely understood. In this study, we focused on the biophysical characteristics of cells and their changes resulting from the effect of GO nanosheets. The biophysical properties of the cell population were characterized as follows: cell stiffness was calculated by atomic force microscopy, cell motility and invasive properties were characterized in the microfluidic chip in which the cells are able to visualize cell migration at a single-cell level. Intracellular actin was stained to establish a quantitative picture of the intracellular cytoskeleton. In addition, to understand the molecular interaction of GO nanosheets and actin filaments, coarse-grained (CG) molecular dynamics (MD) simulations were carried out. Our results showed that GO nanosheets can reduce cell stiffness in MCF7 cells and MDA-MB-231 cell lines and highly inhibited cell migration (39.2%) in MCF-7 and (38.6%) in MDA-MB-231 cell lines through the GO nanosheets-mediated disruption of the intracellular cytoskeleton. In the presence of GO nanosheets, the cell migration of both cell lines, as well as the cell stiffness, significantly decreased. Moreover, after GO nanosheets treatment, the cell actin network dramatically changed. The experimental and theoretical approaches established a quantitative picture of changes in these networks. Our results showed the reduction of the order parameter in actin filaments was 23% in the MCF7 cell line and 20.4% in the MDA-MB-231 cell line. The theoretical studies also showed that the GO nanosheet–actin filaments have stable interaction during MD simulation. Moreover, the 2D free energy plot indicated the GO nanosheet can induce conformational changes in actin filaments. Our findings showed that the GO nanosheets can increase the distance of actin-actin subunits from 3.22 to 3.5 nm and in addition disrupt native contacts between two subunits which lead to separate actin subunits from each other in actin filaments. In this study, the biomechanical characteristics were used to explain the effect of GO nanosheets on cells which presents a novel view of how GO nanosheets can affect the biological properties of cells without cell death. These findings have the potential to be applied in different biomedical applications.

We aimed to explore and compare new insights on the pharmacological potential of Oliveria decumbence essential oil (OEO) and its main components highlighting their antioxidant activity in-vitro, in-vivo, and in-silico and also cytotoxic effects of OEO against A549 lung cancer cells. At first, based on GC–MS analysis, thymol, carvacrol, p-cymene, and γ-terpinene were introduced as basic ingredients of OEO and their in-vitro antioxidant capacity was considered by standard methods. Collectively, OEO exhibited strong antioxidant properties even more than its components. In LPS-stimulated macrophages treated with OEO, the reduction of ROS (Reactive-oxygen-species) and NO (nitric-oxide) and down-regulation of iNOS (inducible nitric-oxide-synthase) and NOX (NADPH-oxidase) mRNA expression was observed and compared with that of OEO components. According to the results, OEO, thymol, and carvacrol exhibited the highest radical scavenging potency compared to p-cymene, and γ-terpinene. In-silico Molecular-Docking and Molecular-Dynamics simulation indicated that thymol and carvacrol but no p-cymene and γ-terpinene may establish coordinative bonds in iNOS active site and thereby inhibit iNOS. However, they did not show any evidence for NOX inhibition. In the following, MTT assay showed that OEO induces cytotoxicity in A549 cancer cells despite having a limited effect on L929 normal cells. Apoptotic death and its dependence on caspase-3 activity and Bax/Bcl2 ratio in OEO-treated cells were established by fluorescence microscopy, flow cytometry, colorimetric assay, and western blot analysis. Additionally, flow cytometry studies demonstrated increased levels of ROS in OEO-treated cells. Therefore, OEO, despite showing antioxidant properties, induces apoptosis in cancer cells by increasing ROS levels. Collectively, our results provided new insight into the usage of OEO and main components, thymol, and carvacrol, into the development of novel antioxidant and anti-cancer agents.

Lamellar and hexagonal lipid structures are of particular importance in the biological processes such as membrane fusion and budding. Atomistic simulations of formation of these phases and transitions between them are computationally prohibitive, hence development of coarse-grained models is an important part of the methodological development in this area. Here we apply systematic bottom-up coarse-graining to model different phase structures formed by 1,2-dioleoylphosphatidylethanolamine (DOPE) lipid molecules. We started from atomistic simulations of DOPE lipids in water carried out at two different water/lipid molar ratio corresponding to the lamellar L α and inverted hexagonal H II structures at low and high lipid concentrations respectively. The atomistic trajectories were mapped to coarse-grained trajectories, in which each lipid was represented by 14 coarse-grained sites. Then the inverse Monte Carlo method was used to compute the effective coarse-grained potentials which for the coarse-grain model reproduce the same structural properties as the atomistic simulations. The potentials derived from the low concentration atomistic simulation were only able to form a bilayer structure, while both L α and H II lipid phases were formed in simulations with potentials obtained at high concentration. The typical atomistic configurations of lipids at high concentration combine fragments of both lamellar and non-lamellar structures, that is reflected in the extracted coarse-grained potentials which become transferable and can form a wide range of structures including the inverted hexagonal, bilayer, tubule, vesicle and micellar structures.

Electrically conductive textiles have received considerable attention due to its possible applications in the areas of electromagnetic shielding, chemical sensors and heating fabrics 1-3. A new approach to highly conductive textile materials is the use of intrinsically conductive organic polymers 4,5. The conductive polymers are based on conjugated electron structure, first created in the early 1980's with poly acetylene, a nonprocessable oxygen-sensitive polymer 6. Today, the inherently conductive polymers such as poly aniline, polypyrrole, polythiophene and poly(per-naphthalene) can be processed and are being used in industrial applications. However, typically the conductive polymer materials are semiconductors. A common approach is to apply dispersions or powders of fully prepared conductive polymers as coating. These approaches usually result in rather low conducting materials. An interesting alternative is to create the conductive polymers by the polymerization of monomers on the textile. Methods for in situ polymerization (by oxidation) are well known for polypyrrole and poly aniline 7-9. Polypyrrole cannot be directly processed due to its intrinsically poor mechanical properties. Therefore, it is generally used for coating other materials. Polypyrrole-coated fabrics have good electrical conductivity, thermal properties and flexibility suitable for a number of applications 10. In addition, deposition of thin layers of conductive polymers on the fiber

Knowledge-based potentials are developed to investigate the differentiation of native structures from their decoy sets. This work presents the construction of two different distancedependent potential energy functions based on two basic assumptions using mathematical modeling. In the first case, according to Anfinsen's dogma, we assumed that the energy of each model structure should be more positive than the corresponding native type. In the second one, we assumed that the energy difference between the native and decoy structures changes linearly with the root-mean-square deviation of structures. These knowledge-based potentials are expressed by the B-spline basis functions of the pairwise distances between Cα-Cα of inter-residues. The potential function parameters in the above two approaches were optimized using the linear programming algorithm on a large collection of Titan-HRD and tested on the remainder. We found that the potential functions produced by Anfinsen's dogma detect native structures more accurately than those developed by the root-mean-square deviation. Both linear programming knowledge-based potentials (LPKP) successfully detect the native structures from an ensemble of decoys. However, the LPKP of the first approach is able to correctly identify 130 native structures out of 150 tested cases with an average rank of 1.67. While the second approach LPKP detects 124 native structures from their decoys. We concluded that linear programming optimization is a promising method in generating knowledge-based potential functions. All the high-resolution structures (training and testing) used for this work are available online and can be downloaded from http://titan.princeton.edu/HRDecoys.