Image-based quantification of fiber alignment within electrospun tissue engineering scaffolds is related to mechanical anisotropy

Sequential Dual Alignments Introduce Synergistic Effect on Hexagonal Boron Nitride Platelets for Superior Thermal Performance

Planarly aligning 2D platelets is challenging due to their additional orientational freedom compared to 1D materials. This study reports a sequential dual-alignment approach, employing an extrusion-printing-induced shear force and rotating-magnetic-field-induced force couple for platelet planarly alignment in a yield-stress support bath. It is hypothesized that the partial alignment induced by a directional shear force facilitates subsequent axial rotation of the platelets for planar alignment under an external force couple, resulting in a synergistic alignment effect. This sequential dual-alignment approach achieves better planar alignment of 2D modified hexagonal boron nitride (mhBN). Specifically, the thermal conductivity of the 40 wt% mhBN/epoxy composite is significantly higher (692%) than that of unaligned composites, surpassing the cumulative effect of individual methods (only 133%) with a 5 times more synergistic effect. For 30, 40, and 50 wt% mhBN composites, the thermal conductivity values (5.9, 9.5, and 13.8 W m-1 K-1) show considerable improvement compared to the previously reported highest values (5.3, 6.6, and 8.6 W m-1 K-1). Additionally, a 3D mhBN/epoxy heat sink is printed and evaluated to demonstrate the feasibility of device fabrication. The approach enables the planar alignment of electrically or thermally conducting 2D fillers during 3D fabrication.

Tumor-Derived Membrane Vesicles Restrain Migration in Gliomas By Altering Collective Polarization

Mechanobiology is a cornerstone in physiology. However, its role in biomedical applications remains considerably undermined. In this study, we employed cell membrane vesicles (CMVs), which are currently being used as nanodrug carriers, as tactile cues for mechano-regulation of collective cell behaviors. Gliomas, which are among the most resilient brain tumors and have a low patient survival rate, were used as the cell model. We observed that mechanical responses due to the application of glioma- or microglia-derived CMVs resulted in the doubling of the traction stress of glioma cell collectives with a 10-fold increase in the CMV concentration. Glioma-CMVs constrained cell protrusions and hindered their collective migration, with the migration speed of such cells declining by almost 40% compared to the untreated cells. We speculated that the alteration of collective polarization leads to migration speed changes, and this phenomenon was elucidated using the cellular Potts model. In addition to intracellular force modulation and cytoskeletal reorganization, glioma-CMVs altered drug diffusion within glioma spheroids by downregulating the mechano-signaling protein YAP-1 while also marginally enhancing the associated apoptotic events. Our results suggest that glioma-CMVs can be applied as an adjuvant to current treatment approaches to restrict tumor invasion and enhance the penetration of reagents within tumors. Considering the broad impact of mechano-transduction on cell functions, the regulation of cell mechanics through CMVs can provide a foundation for alternative therapeutic strategies.

Comparative Analysis of Fiber Alignment Methods in Electrospinning

Fabrication of anisotropic materials is highly desirable in designing biomaterials and tissue engineered constructs. Electrospinning has been broadly adopted due to its versatility in producing non-woven fibrous meshes with tunable fiber diameters (from 10 nanometers to 10 microns), microarchitectures, and construct geometries. A myriad of approaches have been utilized to control fiber alignment of electrospun materials to achieve complex microarchitectures, improve mechanical properties, and provide topographical cellular cues. This review provides a comparative analysis of the techniques developed to generate fiber alignment in electrospun materials. A description of the underlying mechanisms that drive fiber alignment, setup variations for each technique, and the resulting impact on the aligned microarchitecture is provided. A critical analysis of the advantages and limitations of each approach is provided to guide researchers in method selection. Finally, future perspectives of advanced electrospinning methodologies are discussed in terms of developing a scalable method with precise control of microarchitecture.

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

Nanofiber Alignment Regulates NIH3T3 Cell Orientation and Cytoskeletal Gene Expression on Electrospun PCL+Gelatin Nanofibers

To examine the influence of substrate topology on the behavior of fibroblasts, tissue engineering scaffolds were electrospun from polycaprolactone (PCL) and a blend of PCL and gelatin (PCL+Gel) to produce matrices with both random and aligned nanofibrous orientations. The addition of gelatin to the scaffold was shown to increase the hydrophilicity of the PCL matrix and to increase the proliferation of NIH3T3 cells compared to scaffolds of PCL alone. The orientation of nanofibers within the matrix did not have an effect on the proliferation of adherent cells, but cells on aligned substrates were shown to elongate and align parallel to the direction of substrate fiber alignment. A microarray of cyotoskeleton regulators was probed to examine differences in gene expression between cells grown on an aligned and randomly oriented substrates. It was found that transcriptional expression of eight genes was statistically different between the two conditions, with all of them being upregulated in the aligned condition. The proteins encoded by these genes are linked to production and polymerization of actin microfilaments, as well as focal adhesion assembly. Taken together, the data indicates NIH3T3 fibroblasts on aligned substrates align themselves parallel with their substrate and increase production of actin and focal adhesion related genes.

Electrically conductive nanofibrous scaffolds based on poly(ethylene glycol)s-modified polyaniline and poly(ε-caprolactone) for tissue engineering applications

The objective of this study was to design and development of electrically conductive nanofibrous scaffolds composed of PEGs-b-(PANI)₄ and PCL for tissue engineering applications.