Biomaterials by the supramolecular control of nanofibers

In contemporary biomaterials research bioactive polymers are state of the art since they can interact with the in vivo environment. Especially nanofiber morphologies are shown to form a promising synthetic niche. The aim of this thesis is to develop a new concept to make bioactive thermoplastic elastomers (TPEs) by using their supramolecular interactions. In a modular and supramolecular approach bioactive peptides are equipped with the same well-defined hard segment units as present in the polymer. A desired polymer is formed by simple mixing the polymer with the desired bioactive peptide(s). Preferably dynamic and bioactive nanorods are formed due to the designed supramolecular interactions between the hard segments. In this way the good mechanical properties of TPEs are combined with the versatility of self assembly. In chapter 2 a poly(e-caprolactone)-based poly(urethane)urea and poly(urea) were synthesized and characterized in terms of mechanical properties, processability, and histocompatibility. The difference in hard segment structure does not significantly affect the potency for application as a biomaterial. Nevertheless, the small differences in hard block composition have a strong effect on the molecular recognition properties of the hydrogen bonding segments. It is shown that only an exact match between the polymer hard segment and the unit attached to a dye molecule results in strong incorporation of the dye in the polymer. Preliminary results reveal that a bis(ureido)butylene-functionalized GRGDS peptide incorporated in the bis(ureido)butylene hard segment stacks of the poly(urea) can indeed result in cell adhesion and spreading on the polymer surface. Poly(e-caprolactone) (PCL) is known to degrade very slowly in vivo, however. By preparing copolymers of e-caprolactone and 2-oxo-12-crown-4 ether, poly(CL-co-OC), we are able to increase the intrinsic rate of hydrolysis of the proposed soft segments as described in chapter 3. Combined with the enhanced hydrophilicity and reduced crystallinity, we are confident that the prepared poly(urethane)ureas with the poly(CLco- OC) soft segments have appropriate in vivo degradation rates for soft tissue engineering applications. Functionalization of the poly(urea) (PCLU4U) via our modular and supramolecular approach is studied in great detail in chapter 4 using a model system where the bis(ureido)butylene (U4U) unit is used as such to mix into the polymer. The fillers are indeed incorporated into the U4U hard segment rods of the polymer via bifurcated hydrogen bonding interactions up to 23 mol % (= 7.3 wt %) of incorporated filler. The incorporation of filler in this regime results in remarkable mechanical properties: a more than doubled stiffness of the material, but unaltered tensile strength and elongation at break. When more than 23 mol % of filler is added to PCLU4U, separate filler crystallites are observed and the Young’s modulus drops a little, followed by an increase upon adding even more filler. In this second regime, tensile strength and elongation at break decrease, revealing similar behavior to reinforcing thermoplastic elastomers with the more common micrometer-sized reinforcement fillers. To shed more light on this peculiar mechanical behavior, we combined small angle Xray scattering and infrared dichroism in chapter 5 to study the deformation mechanism of PCLU4U containing various amounts of incorporated filler on a macroscopic and molecular level, respectively. In the bare polymer and PCLU4U containing less than 25 mol % of filler, the hard segment nanorods align parallel to the strain axis upon uniaxial deformation up to the yield point. Permanent deformation is caused by fragmenting of the stacks which then start to orient perpendicular to the strain axis. Above 25 mol % of incorporated filler, all urea groups orient parallel to the strain axis before the yield point. Beyond the yield point, however, the deformation mechanism is not completely clear. The phase-separated filler stacks are not connected to the soft matrix and from the obtained data we propose that the filler stacks therefore remain parallel to the strain axis, while the polymer hard segments with the strongly incorporated part of the filler behaves similar to the hard segment stacks in the pure polymer. In chapter 6 a true combination of traditional TPEs and supramolecular polymers is made: we added lateral interactions to ureidopyrimidinone (UPy) based supramolecular polymers by introducing a urea (U) or urethane (UT) unit, six carbons away from the UPy group. The strong directionality of the urea units results in urea-urea stacking, further stabilized by p-p interactions between UPy-UPy dimers. Long, well-defined UPy- U nanorods were observed with a high melting temperature (120 ºC). These nanorods orient in the direction of the applied force upon uniaxial deformation. The UT-UT hydrogen bonds are much weaker, therefore the UPy-UT stacks are less defined and show a low melting temperature (40 ºC). The UPy-U units are completely phase separated from the soft matrix, while a significant part of the urethanes seems to be dissolved in the soft matrix. Based on the previous chapter, we synthesized a series of ureidopyrimidinone-C6- urea (UPy-U) based thermoplastic elastomers with various substituents at the C-6 position of the UPy and studied its influence on the formation and morphology of the UPy-U nanorods. A bulky C-6 substituent results in less strongly packed crystal structures, as evidenced by significantly decreased melting temperatures. However, the enantiomeric excess (e.e.), determines the speed at which the nanorod crystals are formed. A methyl substituent allows for instantaneous crystallization into UPy-U hard segment nanorods with high melting temperature (~130 °C). The nanorods are straight and have a high aspect ratio based on AFM images. A more bulky ethylpentyl unit at C-6 (e.e. is 0%) results in partial and slow crystallization of the UPy-U units into low melting (~60 °C) nanorods. When the optically pure and bulky (S) or (R) citronellyl- UPy-U units are used at the C-6 position, the citronellyl-UPy-U units completely crystallize into slightly winding nanorods that melt at intermediate temperatures (70- 90 °C). The optically most pure (R, e.e. is > 99%) citronellyl-UPy-U polymer shows a crystallization speed similar to that of the Me-UPy-U hard segments. The slightly less optically pure (S, e.e. is 98.4%) citronellyl-UPy-U units crystallize much slower. From this chapter, we concluded that the most promising candidates for a dynamic UPy-U nanorod are the (S) citronellyl-UPy-U and the Me-Upy-U unit. In chapter 8 we show that by combining our biofunctionalized supramolecular TPEs with electrospinning of nanosized fibers, both top-down and bottom-up approaches are used to arrive at ideal biomaterials. UPy-U functionalized peptides are shown to be incorporated in the poly(e-caprolactone) based UPy-U polymer in a highly specific way. Therefore, PCL-UPy-U is selected as cell supporting material in tissue engineering of the renal tubule. Electrospun membranes of this material are prepared with or without UPy-U functionalized peptides in the solution. While culturing human primary tubule epithelial cells (PTECs) on PCL-UPy-U membranes under static conditions results in large gaps between adjacent cells, culturing PTECs under perfusion conditions show closed monolayers of polarized epithelial cells. Additionally, four peptides, selected to mimic the natural ECM, are functionalized with the UPy-U unit. Though the UPy-U peptides act as chain stoppers, a very high concentration of the peptides and the polymer results in a solution usable for electrospinning. When all four peptides are incorporated in the membranes, a slightly higher cell density is observed on these membranes than on bare PCL-UPy-U membranes. Excitingly, the collagen I derived DGEA peptide seems to be beneficial to maintain the epithelial phenotype. In conclusion, the use of well-designed supramolecular interactions to produce bioactive nanorods embedded in a biocompatible soft matrix is shown to be an exciting new approach to bioactive thermoplastic elastomers and holds great potential for soft tissue engineering

On the Application and Possibilities of In Vivo Microscopy in Liver Research

In vivo microscopy (IVM) provides a valuable method for studying the histophysiology of the living liver. The method allows observation of living cells in the intact organ of an anesthetized animal with an undisturbed microcirculation, at a magnification and a resolution comparable to normal light microscopy of sectioned material. Due to the absence of preparative procedures, the image differs substantially from histological sections, but it has the advantage of providing us with a reference preparation free of artifacts. In the case of the liver, we have the opportunity to observe directly such details as bile capillaries, intracellular fat droplets, lysosomes, nucleoli and different types of sinusoidal cells and blood cells. By using epifluorescence, it is possible to visualize the phagocytosis of 0.8 μm fluorescent latex (or other) particles by Kupffer cells, to observe fluorescing substances such as FITC labeled asialofetuin during the process of endocytosis and intracellular transport in parenchymal cells, and to study the behavior of specific cell types such as white blood cells which stain specifically with acridine orange. It might be expected that in the very near future, the application of modern techniques based on processing TV images, such as image intensifying, averaging, filtering, integrating, gating and others will tremendously improve the possibilities of IVM and bring it to the level of observing and following single molecules, such as FITC labelled peptide hormones and other probes, at the cellular level

Isospectral flow in Loop Algebras and Quasiperiodic Solutions of the Sine-Gordon Equation

The sine-Gordon equation is considered in the hamiltonian framework provided by the Adler-Kostant-Symes theorem. The phase space, a finite dimensional coadjoint orbit in the dual space \grg^* of a loop algebra \grg, is parametrized by a finite dimensional symplectic vector space $W$ embedded into \grg^* by a moment map. Real quasiperiodic solutions are computed in terms of theta functions using a Liouville generating function which generates a canonical transformation to linear coordinates on the Jacobi variety of a suitable hyperelliptic curve.Comment: 12 pg

Lymphoid Microenvironments in the Thymus and Lymph Node

The three-dimensional architecture of the thymus and mesenteric lymph node reveals several different stromal cell types important in the development and function of T cells. In the thymic cortex, T cells proliferate and differentiate in a meshwork of epithelial-reticular cells. They then migrate towards the medulla where they may interact with interdigitating cells. T cells migrate from the thymus through perivascular spaces, surrounding large vessels at the cortico-medullary boundary. In this area also large thymic cystic cavities are found, their function remains at present unclear. Mature selected T cells leave the thymus most probably by the venous bloodstream, to enter peripheral lymph nodes. Upon entering the lymph node they cross the wall of high endothelial venules. On the other hand, lymph enters the node by afferent lymphatics draining into various types of sinuses. Here, macrophages are strategically located to phagocytose and process antigen. These cells then expose antigen to T cells and B cells within the lymph node parenchyma, thus creating a microenvironment for the onset of an immune response. The various microenvironments important in T cell development and T cell function are shown in this paper using scanning electron microscopy as a dissecting tool. We discuss our morphological findings in the light of recent data on the physiology of T cell differentiation and function

A machine learning approach to explore predictors of graft detachment following posterior lamellar keratoplasty:a nationwide registry study

Machine learning can be used to explore the complex multifactorial patterns underlying postsurgical graft detachment after endothelial corneal transplantation surgery and to evaluate the marginal effect of various practice pattern modulations. We included all posterior lamellar keratoplasty procedures recorded in the Dutch Cornea Transplant Registry from 2015 through 2018 and collected the center-specific practice patterns using a questionnaire. All available data regarding the donor, recipient, surgery, and practice pattern, were coded into 91 factors that might be associated with the occurrence of a graft detachment. In this research, we used three machine learning methods; a regularized logistic regression (lasso), classification tree analysis (CTA), and random forest classification (RFC), to select the most predictive subset of variables for graft detachment. A total of 3647 transplants were included in our analysis and the overall prevalence of graft detachment was 9.9%. In an independent test set the area under the curve for the lasso, CTA, and RFC was 0.70, 0.65, and 0.72, respectively. Identified risk factors included: a Descemet membrane endothelial keratoplasty procedure, prior graft failure, and the use of sulfur hexafluoride gas. Factors with a reduced risk included: performing combined procedures, using pre-cut donor tissue, and a pre-operative laser iridotomy. These results can help surgeons to review their practice patterns and generate hypotheses for empirical research regarding the origins of graft detachments

Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.Comment: Submitted to NeuroImage for revie