Homogeneous films from amphiphilic hyaluronan and their characterization by confocal microscopy and nanoindentation.

Self-supporting films from amphiphilic hyaluronan are suitable for medical applications like wound dressings or resorbable implants. These films are typically cast from water/alcohol solutions. However, when the mixed solvent evaporates in ambient air, convection flows develop in the solution and become imprinted in the film, potentially compromising its properties. Consequently, we developed a novel film manufacturing method: drying in a closed box under saturated vapour conditions. Using this approach, we prepared a series of optically clear lauroyl-hyaluronan (LHA) films with uniform thickness and compared them to their air-dried counterparts. We first evaluated swelling ratios and elastic moduli for LHA films with varying degrees of substitution. The box-dried films swelled significantly less and were 1-2 orders of magnitude stiffer than air-dried films from the same LHA sample. Confocal microscopy revealed that box-dried films exhibited a regular microstructure, while air-dried films displayed a pore-size gradient and strong microstructure modulation due to convection flows. Local elastic modulus variations arising from these microstructures were assessed using nanoindentation mapping. Importantly, achieving the desired film stiffness requires much lower polymer modification when box-drying is used, enhancing the biological response to the material. These findings have implications for all polysaccharide formulations that utilize mixed solvents.

Thymidine utilisation pathway is a novel phenotypic switch of Mycoplasma hominis.

Introduction. Mycoplasma hominis is a bacterium belonging to the class Mollicutes. It causes acute and chronic infections of the urogenital tract. The main features of this bacterium are an absence of cell wall and a reduced genome size (517-622 protein-encoding genes). Previously, we have isolated morphologically unknown M. hominis colonies called micro-colonies (MCs) from the serum of patients with inflammatory urogenital tract infection.Hypothesis. MCs are functionally different from the typical colonies (TCs) in terms of metabolism and cell division.Aim. To determine the physiological differences between MCs and TCs of M. hominis and elucidate the pathways of formation and growth of MCs by a comparative proteomic analysis of these two morphological forms.Methodology. LC-MS proteomic analysis of TCs and MCs using an Ultimate 3000 RSLC nanoHPLC system connected to a QExactive Plus mass spectrometer.Results. The study of the proteomic profiles of M. hominis colonies allowed us to reconstruct their energy metabolism pathways. In addition to the already known pentose phosphate and arginine deamination pathways, M. hominis can utilise ribose phosphate and deoxyribose phosphate formed by nucleoside catabolism as energy sources. Comparative proteomic HPLC-MS analysis revealed that the proteomic profiles of TCs and MCs were different. We assume that MC cells preferably utilised deoxyribonucleosides, particularly thymidine, as an energy source rather than arginine or ribonucleosides. Utilisation of deoxyribonucleosides is less efficient as compared with that of ribonucleosides and arginine in terms of energy production. Thymidine phosphorylase DeoA is one of the key enzymes of deoxyribonucleosides utilisation. We obtained a DeoA overexpressing mutant that exhibited a phenotype similar to that of MCs, which confirmed our hypothesis.Conclusion. In addition to the two known pathways for energy production (arginine deamination and the pentose phosphate pathway) M. hominis can use deoxyribonucleosides and ribonucleosides. MC cells demonstrate a reorganisation of energy metabolism: unlike TC cells, they preferably utilise deoxyribonucleosides, particularly thymidine, as an energy source rather than arginine or ribonucleosides. Thus MC cells enter a state of energy starvation, which helps them to survive under stress, and in particular, to be resistant to antibiotics.

Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors.

Design and development of scale-down approaches, such as microbioreactor (µBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of µBRs in biotechnology remains the lack of appropriate software and automated data interpretation of µBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for µL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of µBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs.

Numerical Modeling of Viscoelasticity in Particle Suspensions Using the Discrete Element Method.

The rheological behavior of particle suspensions is a challenging problem because its description depends on the interaction of two phases with different material properties. This interaction can lead to complex behavior because of acting forces at the solid-liquid interface such as lubrication. The goal of this work is to propose a method for the modeling of fluids viscoelasticity in the presence of spherical particles including fluid-particle interactions. To accomplish this, we employed a simplified approach using the discrete element method (DEM) coupled with computational fluid dynamics (CFD) to simulate a suspension of particles under oscillatory flow in a three-dimensional computational domain. The choice of DEM provides versatility to customize the constitutive relations of particle-particle and fluid-particle interactions. Particularly, we focused on studying the effect of solid-liquid interaction (lubrication forces) on the viscoelasticity of the particulate system. To analyze the effect of this interfacial force, we simplified the particle-particle interaction to a nonadhesive elastic contact, thus avoiding aggregation of the particles. The work consists of two parts: the first one is a pure CFD model of the oscillatory motion applied to a Newtonian fluid (without particles), and the second is an extended version including DEM to simulate the viscoelasticity of the particle suspension. In this way, we can isolate the effect of fluid inertia on the viscoelasticity of the particulate system. The obtained results show that the model is capable to reproduce qualitatively the increase of the storage modulus as a function of the solid volume fraction and the dependence of dynamic moduli on the applied shear strain. The presented methodology provides a new insight into modeling of rheology by customizing interactions at the particle level based purely on first-principles with model parameters including solely material properties and physically identifiable quantities.

Sensors for biosensors: a novel tandem monitoring in a droplet towards efficient screening of robust design and optimal operating conditions.

Understanding the biorecognition and transduction mechanisms is a key aspect in the development of robust sensing technologies. Therefore, the design of tools and analytical approaches that could allow gaining a deeper insight into the bio- and electrochemical processes would significantly accelerate the progress in the field of biosensors. Herein, we present a novel effective strategy for biosensor design screening based on tandem monitoring of individual system parameters in a droplet. The developed tandem approach couples the simultaneous chronoamperometric characterization of biosensors in the presence of an analyte (glucose) together with dissolved oxygen monitoring using a luminescence-based optical oxygen microsensor. Remarkably, an optical sensor was applied for the first time to analyse the amperometric biosensor response and kinetics. Two types of multi-layer glucose biosensors (first generation) were chosen as a case study and were evaluated at various operating conditions using multi-analytical techniques. Moreover, specific protocols were developed for the detection of oxygen conversion rates, iron and membrane elution inside the multi-layer glucose biosensor system. The presented tandem monitoring approach allows one to identify and build-up the correlations between the critical operation conditions and system parameters affecting the overall biosensor response, its sensitivity and lifetime. Thus, based on the obtained experimental results a more favorable composition of Nafion membrane films and enzyme loadings for glucose biosensors were identified in a time-efficient way and allowed to explain an improved stability (up to 3 months) and linear detection range of glucose concentrations (up to 5 mM). Furthermore, the presented tandem monitoring approach can be readily adapted to other oxygen dependent types of biosensors either for simultaneous multiple substrate detection or as an efficient tool for biosensor design and operating condition screening.

Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes.

Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multiobjective target optimization with an explicit aim to minimize the number of "expensive" experiments, guides the discovery process. This "black-box" approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semibatch emulsion copolymerization, targeting a specific particle size and full conversion.