Crosslinking Collagen Constructs: Achieving Cellular Selectivity Through Modifications of Physical and Chemical Properties

Collagen-based constructs have emerged in recent years as ideal candidates for tissue engineering implants. For many biomedical applications, collagen is crosslinked in order to improve the strength, stiffness and stability of the construct. However, the crosslinking process may also result in unintended changes to cell viability, adhesion or proliferation on the treated structures. This review provides a brief overview of some of both the most commonly used and novel crosslinkers used with collagen, and suggests a framework by which crosslinking methods can be compared and selected for a given tissue engineering application

Ethyl Zinc β-Ketoiminates and β-Amidoenoates: Influence of Precursor Design on the Properties of Highly Conductive Zinc Oxide Thin Films from Aerosol-Assisted Chemical Vapour Deposition

Highly transparent (>85 %) and conductive (1.086×10-3  Ω cm) zinc oxide thin films have been deposited from specifically selected precursors allowing us to establish a direct correlation between their molecular structure and the optoelectronic properties of the deposited films. Mono-ligated ethyl zinc compounds of varying steric bulk: [EtZn(OC(Me)CH(Me)N(i Pr))]2 (1), [EtZn(OC(OEt)CH(Me)N(i Pr))]2 (2) and [EtZn(OC(OEt)CH(CH3 )N(Dipp))]2 (3) were compared with the related bis-ligated zinc complexes [Zn(OC(Me)CH(Me)N(i Pr))2 ] (4), [Zn(OC(OEt)CH(Me)N(i Pr))2 ] (5) and [Zn(OC(OEt)CH(Me)N(Dipp))2 ] (6). In all cases bulkier ligands resulted in poorer electronic properties of deposited films, whilst all mono-ligated compounds were shown as superior precursors. All complexes were characterised by 1 H and 13 C{1 H} NMR and elemental analysis, with the structure of 6 determined by single crystal X-ray diffraction. Zinc oxide films were deposited from single and dual source (with methanol) reactions of these precursors, and analysed via XRD, XPS and EDX. Optoelectronic properties were investigated through UV/vis spectroscopy and Hall effect measurements, and morphology was examined via SEM. Tauc plots from UV/vis data indicated that Film A showed the lowest band gap of 3.31 eV. Varying the elemental composition of the precursors led to changes in the elemental composition of the resultant films, as well as changes in their structural and optoelectronic properties. Using this approach of precursor design, we have been able to tune single source precursors towards zinc oxide to deposit films with specific properties

Feature importance in multi-dimensional tissue-engineering datasets: random forest assisted optimization of experimental variables for collagen scaffolds

Ice-templated collagen-based tissue-engineering scaffolds are ideal for controlled tissue regeneration since they mimic the micro-environment experienced in vivo. The structure and properties of scaffolds are fine-tuned during fabrication by controlling a number of experimental parameters. However, this parameter space is large and complex, rendering the interpretation of results and selection of optimal parameters to be challenging in practice. This paper investigates the impact of a cross section of this parameter space (drying conditions and solute environment) on the scaffold microstructure. Qualitative assessment revealed the previously unreported impact of drying temperature and pressure on pore wall roughness, and confirmed the influence of collagen concentration, solvent type, and solute addition on pore morphology. For quantitative comparison, we demonstrate the novel application of random forest regression to analyze multi-dimensional biomaterials datasets, and predict microstructural attributes for a scaffold. Using these regression models, we assessed the relative importance of the input experimental parameters on quantitative pore measurements. Collagen concentration and pH were found to be the largest factors in determining pore size and connectivity. Furthermore, circular dichroism peak intensities were also revealed to be a good predictor for structural variations, which is a parameter that has not previously been investigated for its effect on a scaffold microstructure. Thus, this paper demonstrates the potential for predictive models such as random forest regressors to discover novel relationships in biomaterials datasets. These relationships between parameters (such as circular dichroism spectra and pore connectivity) can therefore also be used to identify and design further avenues of investigation within biomaterials

MicroCT analysis of connectivity in porous structures: optimizing data acquisition and analytical methods in the context of tissue engineering.

Micro-computed X-ray tomography (MicroCT) is one of the most powerful techniques available for the three-dimensional characterization of complex multi-phase or porous microarchitectures. The imaging and analysis of porous networks are of particular interest in tissue engineering due to the ability to predict various large-scale cellular phenomena through the micro-scale characterization of the structure. However, optimizing the parameters for MicroCT data capture and analyses requires a careful balance of feature resolution and computational constraints while ensuring that a structurally representative section is imaged and analysed. In this work, artificial datasets were used to evaluate the validity of current analytical methods by considering the effect of noise and pixel size arising from the data capture, and intrinsic structural anisotropy and heterogeneity. A novel 'segmented percolation method' was developed to exclude the effect of anomalous, non-representative features within the datasets, allowing for scale-invariant structural parameters to be obtained consistently and without manual intervention for the first time. Finally, an in-depth assessment of the imaging and analytical procedures are presented by considering percolation events such as micro-particle filtration and cell sieving within the context of tissue engineering. Along with the novel guidelines established for general pixel size selection for MicroCT, we also report our determination of 3 μm as the definitive pixel size for use in analysing connectivity for tissue engineering applications

Self-assembly of collagen bundles and enhanced piezoelectricity induced by chemical crosslinking.

The piezoelectricity of collagen is purported to be linked to many biological processes including bone formation and wound healing. Although the piezoelectricity of tissue-derived collagen has been documented across the length scales, little work has been undertaken to characterise the local electromechanical properties of processed collagen, which is used as a base for tissue-engineering implants. In this work, three chemically distinct treatments used to form structurally and mechanically stable scaffolds-EDC-NHS, genipin and tissue transglutaminase-are investigated for their effect on collagen piezolectricity. Crosslinking with EDC-NHS is noted to produce a distinct self-assembly of the fibres into bundles roughly 300 nm in width regardless of the collagen origin. These fibre bundles also show a localised piezoelectric response, with enhanced vertical piezoelectricity of collagen. Such topographical features are not observed with the other two chemical treatments, although the shear piezoelectric response is significantly enhanced upon crosslinking. These observations are reconciled by a proposed effect of the crosslinking mechanisms on the molecular and nanostructure of collagen. These results highlight the ability to modify the electromechanical properties of collagen using chemical crosslinking methods.ERC, Bill and Melinda Gates Foundation, Geistlich Pharma A

Deposition of metallic silver from versatile amidinate precursors for use in functional materials

Silver (Ag) amidinate metal organic decomposition precursors of the type: [Ag2((ArN)2C(H))2] (Ar = 2,6-dimethylphenyl (1), 2,6-diethylphenyl (2) and 2,6-diisopropylphenyl (3)) have been used for the first time in the deposition of Ag films on glass with multiple functionalities with potential application in optical/biological sensors or for use in electronic circuitry. Precursors 1–3 were isolated from the reaction of silver acetate with the appropriate ligand in a 1:2 stoichiometry and were characterized by 1H and 13C{1H} NMR, thermal gravimetric analysis and single crystal X-ray diffraction for 2. Single-layer depositions at 200 °C on glass substrates via spin coating produced transparent (>90% transmittance) coatings, with well-defined Ag nanoparticles. Multi-layer depositions at 200 °C on glass had a metallic lustre and were found to be conductive ( ρ = 0.916–1.83 × 10−6 Ωm). All films were strongly adhered and displayed excellent coverage of the substrate. Ag films deposited from 1 to 3 were analysed by grazing incidence X-ray diffraction, X-ray photoelectron spectroscopy, energy-dispersive X-ray analysis and scanning electron microscopy, with optical properties determined by UV-Vis spectroscopy

Tailoring the biofunctionality of collagen biomaterials via tropoelastin incorporation and EDC-crosslinking

Recreating the cell niche of virtually all tissues requires composite materials fabricated from multiple extracellular matrix (ECM) macromolecules. Due to their wide tissue distribution, physical attributes and purity, collagen, and more recently, tropoelastin, represent two appealing ECM components for biomaterials development. Here we blend tropoelastin and collagen, harnessing the cell-modulatory properties of each biomolecule. Tropoelastin was stably co-blended into collagen biomaterials and was retained after EDC-crosslinking. We found that human dermal fibroblasts (HDF), rat glial cells (Rugli) and HT1080 fibrosarcoma cells ligate to tropoelastin via EDTA-sensitive and EDTA-insensitive receptors or do not ligate with tropoelastin, respectively. These differing elastin-binding properties allowed us to probe the cellular response to the tropoelastin-collagen composites assigning specific bioactivity to the collagen and tropoelastin component of the composite material. Tropoelastin addition to collagen increased total Rugli cell adhesion, spreading and proliferation. This persisted with EDC-crosslinking of the tropoelastin-collagen composite. Tropoelastin addition did not affect total HDF and HT1080 cell adhesion; however, it increased the contribution of cation-independent adhesion, without affecting the cell morphology or, for HT1080 cells, proliferation. Instead, EDC-crosslinking dictated the HDF and HT1080 cellular response. These data show that a tropoelastin component dominates the response of cells that possess non-integrin based tropoelastin receptors. EDC modification of the collagen component directs cell function when non-integrin tropoelastin receptors are not crucial for cell activity. Using this approach, we have assigned the biological contribution of each component of tropoelastin-collagen composites, allowing informed biomaterial design for directed cell function via more physiologically relevant mechanisms. Statement of significance Biomaterials fabricated from multiple extracellular matrix (ECM) macromolecules are required to fully recreate the native tissue niche where each ECM macromolecule engages with a specific repertoire of cell-surface receptors. Here we investigate combining tropoelastin with collagen as they interact with cells via different receptors. We identified specific cell lines, which associate with tropoelastin via distinct classes of cell-surface receptor. These showed that tropoelastin, when combined with collagen, altered the cell behaviour in a receptor-usage dependent manner. Integrin-mediated tropoelastin interactions influenced cell proliferation and non-integrin receptors influenced cell spreading and proliferation. These data shed light on the interplay between biomaterial macromolecular composition, cell surface receptors and cell behaviour, advancing bespoke materials design and providing functionality to specific cell populations

Landscape of the PARKIN-dependent ubiquitylome in response to mitochondrial depolarization

The PARKIN (PARK2) ubiquitin ligase and its regulatory kinase PINK1 (PARK6), often mutated in familial early onset Parkinson’s Disease (PD), play central roles in mitochondrial homeostasis and mitophagy.1–3 While PARKIN is recruited to the mitochondrial outer membrane (MOM) upon depolarization via PINK1 action and can ubiquitylate Porin, Mitofusin, and Miro proteins on the MOM,1,4–11 the full repertoire of PARKIN substrates – the PARKIN-dependent ubiquitylome - remains poorly defined. Here we employ quantitative diGLY capture proteomics12,13 to elucidate the ubiquitylation site-specificity and topology of PARKIN-dependent target modification in response to mitochondrial depolarization. Hundreds of dynamically regulated ubiquitylation sites in dozens of proteins were identified, with strong enrichment for MOM proteins, indicating that PARKIN dramatically alters the ubiquitylation status of the mitochondrial proteome. Using complementary interaction proteomics, we found depolarization-dependent PARKIN association with numerous MOM targets, autophagy receptors, and the proteasome. Mutation of PARKIN’s active site residue C431, which has been found mutated in PD patients, largely disrupts these associations. Structural and topological analysis revealed extensive conservation of PARKIN-dependent ubiquitylation sites on cytoplasmic domains in vertebrate and D. melanogaster MOM proteins. These studies provide a resource for understanding how the PINK1-PARKIN pathway re-sculpts the proteome to support mitochondrial homeostasis

High School Students’ Perceptions of the Role of Social Support in Cultivating Their Interests in and Aspirations to STEM Degrees and Careers—A Middle Eastern Case Study

This case study intends to comprehend students’ perceptions of social support in cultivating their interests and aspirations for science, mathematics, engineering, and technology (STEM) degrees and careers. Survey-based quantitative research was employed, incorporating data from 1426 high school (grade 11th–12th) students in Qatar. The survey instrument encompassed four dimensions, i.e., (1) participants’ demographics, (2) STEM interests, (3) STEM supports/barriers and (4) STEM career aspirations to understand students’ perceptions. Spearman’s Rho correlation test demonstrated a positive correlation between students’ perceived social support (from family, teachers, and society) and their STEM interests (p < 0.01). Findings from the Mann-Whitney U test illustrated that females perceived enhanced social support (from teachers and society) in Qatar (p < 0.05). Even though teachers and society have been the stimulus to developing students’ STEM interests, there is still room to implement a policy for the consequential influence in constructing students’ STEM career aspirations. Thus, we believe these findings would urge policymakers to design tools that enable teachers and society to nurture, cultivate and sustain interest in STEM among the youth to meet Qatar’s National Vision 2030.The project was funded by Qatar University (Reference: QUCG-SESRI-20/21-1). The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Qatar University (QU-IRB 1424-EA/20) on 1 March 2020

Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models

Background Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models. Methods We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions. Results The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant. Conclusion The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases