Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials

Predictive Modeling of the In Vitro Responses of Preosteoblastic MC3T3-E1 Cells on Polymeric Surfaces Using Fourier Transform Infrared Spectroscopy

Biomaterials' surface properties elicit diverse cellular responses in biomedical and biotechnological applications. Predicting the cell behavior on a polymeric surface is an ongoing challenge due to its complexity. This work proposes a novel modeling methodology based on attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Spectra were collected on wetted polymeric surfaces to incorporate both surface chemistry and information on water-polymer interactions. Results showed that predictive models built with spectra from wetted surfaces ("wet spectra") performed much better than models built using spectra acquired from dry surfaces ("dry spectra"), suggesting that the water-polymer interaction is critically important to the prediction of subsequent cell behavior. The best model was seen to predict total area of focal adhesions with coefficient of determination for prediction (R²_P) of 0.94 and root-mean-square errors of prediction (RMSEP) of 4.03 μm² when tested on an independent experimental set. This work offers new insights into our understanding of cell-biomaterial interactions. The presence of carboxyl groups in polymers promoted larger adhesion areas, yet the formation of carbonyl-to-water interaction decreased adhesion areas. Surface wettability, which was related to the water-polymer interaction, was proven to highly influence cell adhesion. The good predictive ability opens new possibilities for high throughput monitoring of cell attachment on polymeric substrates.

Hard Block Degradable Polycarbonate Urethanes: Promising Biomaterials for Electrospun Vascular Prostheses

We report biodegradable thermoplastic polyurethanes for soft tissue engineering applications, where frequently used carboxylic acid ester degradation motifs were substituted with carbonate moieties to achieve superior degradation properties. While the use of carbonates in soft blocks has been reported, their use in hard blocks of thermoplastic polyurethanes is unprecedented. Soft blocks consist of poly(hexamethylene carbonate), and hard blocks combine hexamethylene diisocyanate with the newly synthesized cleavable carbonate chain extender bis(3-hydroxypropylene)carbonate (BHPC), mimicking the motif of poly(trimethylene carbonate) with highly regarded degradation properties. Simultaneously, the mechanical benefits of segmented polyurethanes are exploited. A lower hard block concentration in BHPC-based polymers was more suitable for vascular grafts. Nonacidic degradation products and hard block dependent degradation rates were found. Implantation of BHPC-based electrospun degradable vascular prostheses in a small animal model revealed high patency rates and no signs of aneurysm formations. Specific vascular graft remodeling and only minimal signs of inflammatory reactions were observed.

The Effect of Mechanical Overloading on Surface Roughness of the Coronary Arteries

Background

Surface roughness can be used to identify disease within biological tissues. Quantifying surface roughness in the coronary arteries aids in developing treatments for coronary heart disease. This study investigates the effect of extreme physiological loading on surface roughness, for example, due to a rupture of an artery.

Methods

The porcine left anterior descending (LAD) coronary arteries were dissected ex vivo. Mechanical overloading was applied to the arteries in the longitudinal direction to simulate extreme physiological loading. Surface roughness was calculated from three-dimensional reconstructed images. Surface roughness was measured before and after damage and after chemical processing to dehydrate tissue specimens.

Results

Control specimens confirmed that dehydration alone results in an increase of surface roughness in the circumferential direction only. No variation was noted between the hydrated healthy and damaged specimens, in both the longitudinal (0.91 ± 0.26 and 1.05 ± 0.25 μm) and circumferential (1.46 ± 0.38 and 1.47 ± 0.39 μm) directions. After dehydration, an increase in surface roughness was noted for damaged specimens in both the longitudinal (1.28 ± 0.33 μm) and circumferential (1.95 ± 0.56 μm) directions.

Conclusions

Mechanical overloading applied in the longitudinal direction did not significantly affect surface roughness. However, when combined with chemical processing, a significant increase in surface roughness was noted in both the circumferential and longitudinal directions. Mechanical overloading causes damage to the internal constituents of the arteries, which is significantly noticeable after dehydration of tissue.

Identification of Magnesium Oxychloride Cement Biomaterial Heterogeneity using Raman Chemical Mapping and NIR Hyperspectral Chemical Imaging

The present study investigated spatial heterogeneity in magnesium oxychloride cements within a model of a mould using hyperspectral chemical imaging (HCI). The ability to inspect cements within a mould allows for the assessment of material formation in real time in addition to factors affecting ultimate material formation. Both macro scale NIR HCI and micro scale pixel-wise Raman chemical mapping were employed to characterise the same specimens. NIR imaging is rapid, however spectra are often convoluted through the overlapping of overtone peaks, which can make interpretation difficult. Raman spectra are more easily interpretable, however Raman imaging can suffer from slower acquisition times, particularly when the signal to noise ratio is relatively poor and the spatial resolution is high. To overcome the limitations of both, Raman/NIR data fusion techniques were explored and implemented. Spectra collected using both modalities were co-registered and intra and inter-modality peak correlations were investigated while k-means cluster patterns were compared. In addition, partial least squares regression models, built using NIR spectra, predicted chemical-identifying Raman peaks with an R2 of up to >0.98. As macro scale imaging presented greater data collection speeds, chemical prediction maps were built using NIR HCIs.