Radiative Peristaltic Flow of Jeffrey Nanofluid with Slip Conditions and Joule Heating

Mixed convection peristaltic flow of Jeffrey nanofluid in a channel with compliant walls is addressed here. The present investigation includes the viscous dissipation, thermal radiation and Joule heating. Whole analysis is performed for velocity, thermal and concentration slip conditions. Related problems through long wavelength and low Reynolds number are examined for stream function, temperature and concentration. Impacts of thermal radiation, Hartman number, Brownian motion parameter, thermophoresis, Joule heating and slip parameters are explored in detail. Clearly temperature is a decreasing function of Hartman number and radiation parameter.     

Adsorption and immobilisation of human insulin on graphene monoxide,silicon carbide and boron nitride nanosheets investigated by molecular dynamics simulation

The adsorption and immobilisation of human insulin onto the bio-compatible nanosheets including graphene monoxide, silicon carbide and boron nitride nanosheets were studied by molecular dynamics simulation at the temperature of 310 K. After equilibration, heating and 100 ns production molecular dynamic runs, it was found that the insulin was adsorbed and immobilised onto the considered surfaces in a native folded state. The structural parameters, including root-mean-square deviation and fluctuation, surface accessible solvent area, radius of gyration (R_g) and the distance between the centre of the mass of immobilised protein and the surface of the considered nanosheets, were measured, analysed and discussed. The energetics of the studied systems such as the interaction energy between protein and nanosheet was also measured and addressed. The discussions were centred on the structural and energetic parameters of the protein and nanosheets, including charge density, hydrophobicity, hydrophilicity and residue polarity. The results also showed that the active site of C-termini of chain B played an important role in the adsorption process and this could be helpful in the protection of insulin in its smart delivery and release applications.     

Study of quercetin and fisetin synergistic effect on breast cancer and potentially involved signaling pathways

Naturally-derived drugs have drawn much attention in recent decades. Efficiency, lower toxicity, and economic reasons are some of their advantages that justify this broad range of administration for different diseases, including cancer. If we can find a specific combination that boosts the effects of their single therapy, leading to synergism effect, increased efficiency, and decreased toxicity, they can act even better. Quercetin and fisetin, two well-known flavonoids, have been used to fight against various cancers. In this study, we investigated their possible synergism quercetin and fisetin on MCF7, MDA-MB-231, BT549, T47D, and 4T1 breast cancer cell lines. Then the optimum combined dose was used to study their impacts on wound healing abilities and clonogenic properties. The real-time qPCR was used to study the expression of their validated downstream effectors in predicted pathways. A significant synergism effect (p < .01, combination index: <1) was observed for all cell lines. Combination therapy was significantly more effective in colony formation (p < .0001) and wound healing assays (p < .001) compared to single therapies. The expression level of potential effectors was also showed a greater change. In vivo study confirmed the in vitro results and showed how significantly (p < .001) their synergism promotes their singular function in inhibiting cancer progression. The breast cancer mouse models receiving combined therapy lived longer with higher average body weight and smaller tumor sizes. These results exhibit that quercetin and fisetin inhibit cancer cell proliferation, migration and colony formation synergistically, and matrix metalloproteinase signaling and apoptotic pathways are relatively responsible for inhibitory activities.     

Synthesis,Biological Evaluation,and Molecular Modeling Studies of New 1,3,4-Thiadiazole Derivatives as Potent Antimicrobial Agents

In this work, the synthesis, characterization, and biological activities of a new series of 1,3,4-thiadiazole derivatives were investigated. The structures of final compounds were identified using ¹H-NMR, ¹³C-NMR, elemental analysis, and HRMS. All the new synthesized compounds were then screened for their antimicrobial activity against four types of pathogenic bacteria and one fungal strain, by application of the MIC assays, using Ampicilin, Gentamycin, Vancomycin, and Fluconazole as standards. Among the compounds, the MIC values of 4 and 8 μg/mL of the compounds 3f and 3g , respectively, are remarkable and indicate that these compounds are good candidates for antifungal activity. The docking experiments were used to identify the binding forms of produced ligands with sterol 14-demethylase to acquire insight into relevant proteins. The MD performed about 100 ns simulations to validate selected compounds’ theoretical studies. Finally, using density functional theory (DFT) to predict reactivity, the chemical characteristics and quantum factors of synthesized compounds were computed. These results were then correlated with the experimental data. Furthermore, computational estimation was performed to predict the ADME properties of the most active compound 3f .     

Design,Synthesis, and Biological Evaluation of Novel Indanone Derivatives as Cholinesterase Inhibitors for Potential Use in Alzheimer's Disease

Indanone derivatives containing meta/para-substituted aminopropoxy benzyl/benzylidene moieties were designed based on the structures of donepezil and ebselen analogs as the cholinesterase inhibitors. The designed compounds were synthesized and their acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activities were measured. Inhibitory potencies (IC₅₀ values) for the synthesized compounds ranged from 0.12 to 11.92 μM and 0.04 to 24.36 μM against AChE and BChE, respectively. Compound 5 c showed the highest AChE inhibitory potency with IC₅₀ value of 0.12 μM, whereas the highest BChE inhibition was achieved by structure 7 b (IC₅₀=0.04 μM). Structure-activity relationship (SAR) analysis revealed that there is no significant difference between meta and para-substituted derivatives in AChE and BChE inhibition. However, the most potent AChE inhibitor 5 c belongs to meta-substituted compounds, while the most active BChE inhibitor is para-substituted derivative 7 b . The order of enzyme inhibition potency based on the substituted amine group is dimethyl amine>piperidine>morpholine. Compounds containing C=C linkage are more potent AChE inhibitors than the corresponding saturated structures. Molecular docking studies indicated that 5 c interacts with AChE in a very similar way to that observed experimentally for donepezil. The introduced indanone-aminopropoxy benzylidenes could be used in drug-discovery against Alzheimer's disease.     

Expression of a human cytomegalovirus gp58 antigenic domain fused to the hepatitis B virus nucleocapsid protein

Abstract Hepatitis B virus core antigen (HBcAg) has been used as a carrier for expression and presentation of a variety of heterologous viral epitopes in particulate form. The aim of this study was to produce hybrid antigens comprising HBcAg and an immunogenic epitope of human cytomegalovirus (HCMV). A direct comparison was made of amino and carboxyl terminal fusions in order to investigate the influence of position of the foreign epitope on hybrid core particle formation, antigenicity and immunogenicity. HCMV DNA encoding a neutralising epitope of the surface glycoprotein gp58 was either inserted at the amino terminus or fused to the truncated carboxyl terminus of HBcAg and expressed in Escherichia coli . The carboxyl terminal fusion (HBc_3–144-HCMV) was expressed at high levels and assembled into core like particles resembling native HBcAg. Protein with a similar fusion at the amino terminus (HCMV-HBc_1–183) could not be purified or characterised immunologically, although it formed core like particles. HBc_3–144-HCMV displayed HBc antigenicity but HCMV antigenicity could not be detected by radioimmunoassay or western blotting using anti-HCMV monoclonal antibody 7–17 or an anti-HCMV human polyclonal antiserum. Following immunisation of rabbits with HBc_3–144-HCMV, a high titre of anti-HBc specific antibody was produced along with lower titres of HCMV/gp58 specific antibody.     

Strain design optimization using reinforcement learning

Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization.In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbe’s metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains.We demonstrate the method’s capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the method’s performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library.Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels.