Analysis of the Binding of Cytokines to Highly Charged Polymer Networks

We present a model describing the binding of biological signaling proteins to highly charged polymer networks. The networks are formed by polyelectrolyte chains for which the distance between two charges at the chain is smaller than the Bjerrum length. Counterion condensation on such highly charged chains immobilizes a part of the counterions which do no more contribute to the osmotic pressure. The Donnan-equilibrium between the polymer network and the aqueous solution with salt concentration csb is used to calculate the salt concentration of the co- and counterions csg entering the network. Two factors lead to adsorption of proteins to charged polymer networks: i) The electrostatic interaction between the network and the protein is given by the Donnan-potential of the network and the net charge of the protein. In addition to this leading term, a second term describes the change of the Born-energy of the proteins when entering the network. ii) The interaction of the protein with the highly charged chains within the network is governed by counterion release: Patches of positive charge at the protein become multivalent counterions of the polyelectrolyte chains thus releasing a concomitant number of condensed counterions. The model is compared to experimental data obtained on a set of biohybrid polymer networks composed of crosslinked glycosaminoglycan chains that interact with a mixture of key signaling proteins. The analysis of the experimental binding constants reveals that the counterion release mechanism is decisive for protein adsorption to the network at physiological salt concentration

Chemokine‐Capturing Wound Contact Layer Rescues Dermal Healing

Excessive inflammation often impedes the healing of chronic wounds. Scavenging of chemokines by multiarmed poly(ethylene glycol)-glycosaminoglycan (starPEG-GAG) hydrogels has recently been shown to support regeneration in a diabetic mouse chronic skin wound model. Herein, a textile-starPEG-GAG composite wound contact layer (WCL) capable of selectively sequestering pro-inflammatory chemokines is reported. Systematic variation of the local and integral charge densities of the starPEG-GAG hydrogel component allows for tailoring its affinity profile for biomolecular signals of the wound milieu. The composite WCL is subsequently tested in a large animal (porcine) model of human wound healing disorders. Dampening excessive inflammatory signals without affecting the levels of pro-regenerative growth factors, the starPEG-GAG hydrogel-based WCL treatment induced healing with increased granulation tissue formation, angiogenesis, and deposition of connective tissue (collagen fibers). Thus, this biomaterials technology expands the scope of a new anti-inflammatory therapy toward clinical use

Modellierung und Simulation flexibler Körper in alaska-Softwareprodukten

Im Vortrag werden die Möglichkeiten der Modellierung und Verwendung flexibler Körper in Produkten der alaska-Softwarefamilie, die am Institut für Mechatronik e.V. entwickelt werden, vorgestellt. Die alaska-Softwarefamilie umfasst neben dem alaska/ModellerStudio, einer Mehrkörperdynamik-Simulationsumgebung für die allgemeine Verwendung, auch anwendungsspezifische Simulationswerkzeuge mit Fokussierung auf eng begrenzte Einsatzgebiete. Im Interesse realitätsnaher Simulationsergebnisse werden in der Mehrkörperdynamik neben starren Körpern und idealen Gelenken verstärkt elastisch verformbare Körper und nachgiebige Gelenke verwendet. Je nach Einsatzgebiet und Simulationsziel (hohe Genauigkeit, hohe Performance) kommen unterschiedliche Verfahren der Beschreibung flexibler Körper zum Einsatz. Diese werden im Vortrag diskutiert, präsentierte Anwendungsbeispiele illustrieren die Verwendung

Heparin modified polyethylene glycol microparticle aggregates for focal cancer chemotherapy

Focal cancer therapy can improve clinical outcomes. Here, we evaluated injectable heparin-containing hydrogel material loaded with doxorubicin as a focal breast cancer therapy. We utilized a binary heparin/polyethylene glycol (PEG) hydrogel that was processed post synthesis into hydrogel microparticle aggregates to yield a readily injectable hydrogel. When loaded with doxorubicin, the injectable hydrogel microparticle aggregates had excellent short- and long-term anticancer activity against human breast cancer cells in vitro. Efficacy as a focal anticancer therapy was also evaluated in vivo by local injection of the doxorubicin-loaded PEG-heparin hydrogel microparticle aggregates into mice with established human orthotopic breast tumours. Animals showed significant antitumour responses by reduction in both primary tumour growth and metastasis when compared to animals which received the equivalent doxorubicin dose via an intravenous bolus injection. Overall, PEG-heparin hydrogel microparticle aggregates are emerging as a potential anticancer drug delivery system for focal therapy

Tuning the Local Availability of VEGF within Glycosaminoglycan-Based Hydrogels to Modulate Vascular Endothelial Cell Morphogenesis

Incorporation of sulfated glycosaminoglycans (GAGs) into cell-instructive polymer networks is shown to be instrumental in controlling the diffusivity and activity of growth factors. However, a subtle balance between local retention and release of the factors is needed to effectively direct cell fate decisions. To quantitatively unravel material characteristics governing these key features, the GAG content and the GAG sulfation pattern of star-shaped poly(ethylene glycol) (starPEG)–GAG hydrogels are herein tuned to control the local availability and bioactivity of GAG-affine vascular endothelial growth factor (VEGF165). Hydrogels containing varying concentrations of heparin or heparin derivatives with different sulfation pattern are prepared and thoroughly characterized for swelling, mechanical properties, and growth factor transport. Mathematical models are developed to predict the local concentration and spatial distribution of free and bound VEGF165 within the gel matrices. The results of simulation and experimental studies concordantly reveal how the GAG concentration and sulfation pattern determine the local availability of VEGF165 within the cell-instructive hydrogels and how the factor—in interplay with cell-instructive gel properties—determines the formation and spatial organization of capillary networks of embedded human vascular endothelial cells. Taken together, this study exemplifies how mathematical modeling and rational hydrogel design can be combined to pave the way for precision tissue engineering. © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Defined Geldrop Cultures Maintain Neural Precursor Cells

Distinct micro-environmental properties have been reported to be essential for maintenance of neural precursor cells (NPCs) within the adult brain. Due to high complexity and technical limitations, the natural niche can barely be studied systematically in vivo. By reconstituting selected environmental properties (adhesiveness, proteolytic degradability, and elasticity) in geldrop cultures, we show that NPCs can be maintained stably at high density over an extended period of time (up to 8 days). In both conventional systems, neurospheres and monolayer cultures, they would expand and (in the case of neurospheres) differentiate rapidly. Further, we report a critical dualism between matrix adhesiveness and degradability. Only if both features are functional NPCs stay proliferative. Lastly, Rho-associated protein kinase was identified as part of a pivotal intracellular signaling cascade controlling cell morphology in response to environmental cues inside geldrop cultures. Our findings demonstrate that simple manipulations of the microenvironment in vitro result in an important preservation of stemness features in the cultured precursor cells

Experimental Evidence of Direct Exchange Interaction Mediating Intramolecular Singlet Fission in Weakly-Coupled Dimers

The electronic interaction between an optically active singlet state ($S_1S_0$) and a dark state of singlet multiplicity, known as correlated triplet pair ($^1[TT]$), plays a crucial role in the effective transformation from $S_1S_0$ to $^1[TT]$ during intramolecular singlet fission (iSF). This process is understood through mechanisms such as direct exchange coupling and incoherent processes that involve super-exchange coupling through charge-transfer states. However, most insights into these mechanisms are derived from theoretical studies due to the difficulties in obtaining experimental evidence. In this study, we investigate the excited-state interactions between $S_1S_0$ and $^1[TT]$ in spiro-conjugated iSF sensitizers by employing transient two-dimensional electronic spectroscopy. This approach allows us to focus on the early stages of the conversion from $S_1S_0$ to $^1[TT]$. Upon optical excitation, a superposition of $S_1S_0$ and $^1[TT]$ is created, which gradually transitions to favor $^1[TT]$ within the characteristic time frames of iSF. The observed high-order signals indicate circular repopulation dynamic that effectively reinitiates the iSF process from higher energy electronic states. Our findings, supported by semi-quantum-mechanical simulations of the experimental data, suggest the presence of a direct iSF mechanism in the dimers, facilitated by weak non-adiabatic coupling between $S_1S_0$ and $^1[TT]$. This experiment provides new insights into the equilibrium between the two electronic states, a phenomenon previously understood primarily through theoretical models.Comment: 26 pages, 4 Figure

Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems

Recent years have witnessed a growth in mathematics for deep learning--which seeks a deeper understanding of the concepts of deep learning with mathematics and explores how to make it more robust--and deep learning for mathematics, where deep learning algorithms are used to solve problems in mathematics. The latter has popularised the field of scientific machine learning where deep learning is applied to problems in scientific computing. Specifically, more and more neural network architectures have been developed to solve specific classes of partial differential equations (PDEs). Such methods exploit properties that are inherent to PDEs and thus solve the PDEs better than standard feed-forward neural networks, recurrent neural networks, or convolutional neural networks. This has had a great impact in the area of mathematical modeling where parametric PDEs are widely used to model most natural and physical processes arising in science and engineering. In this work, we review such methods as well as their extensions for parametric studies and for solving the related inverse problems. We equally proceed to show their relevance in some industrial applications

Doubly Bridged Anthracenes: Blue Emitters for OLEDs

The photooxidative stability of a series of doubly bridged anthracenes was evaluated after their preparation via twofold macrocyclization of a bis(resorcinyl)anthracene. Lightfastness correlates with the energy levels of the highest occupied molecular orbital (HOMO), resulting in superior stability of the tetraesters compared to the tetraethers. The lengths and steric demand of the linker only plays a minor role for the ester-based compounds, which can be prepared in reasonable yields and thus tested in proof-of-concept organic light-emitting diodes. Double ester-bridging allows deep blue electro-luminescence, highlighting the importance of the choice of the functional groups used for macrocyclization.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Studienstiftung des Deutschen Volkes http://dx.doi.org/10.13039/501100004350Peer Reviewe