Applying biological macromolecule like silk fibroin (SF) is a promising material for corneal tissue engineering. However, designing an appropriate tissue-like construct to compensate the shortages of traditional routes are still challenging. SF besides possessing biocompatibility and transparency, the biomaterial should be mechanically strong. In the present study, a hybrid scaffold composed of poly-ε-caprolactone (PCL)-silk fibroin (SF) is fabricated through electro spinning technique. The aligned and non-aligned PCL-SF scaffolds with various weight ratios are fabricated. The results reveal that the addition of SF yields the scaffolds with more uniform and aligned structure. The ultimate tensile strength and Young's modulus of aligned and non-aligned PCL-SF (60:40 and 50:50) fibers are in an acceptable range for cornea applications. It is noteworthy that the aligned PCL-SF (60:40 and 50:50) scaffolds have more transparency, hydrophilicity, water uptake, and in vitro degradation rate than the other scaffolds. The cell compatibility results show that human stromal keratocyte cells are attached and proliferated on the aligned and non-aligned PCL-SF scaffolds. The overall results recommend that PCL-SF (60:40 and 50:50) scaffolds have a great potential for human corneal stromal regeneration.
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http://dx.doi.org/10.1016/j.ijbiomac.2020.06.045 | DOI Listing |
Polymers (Basel)
December 2024
Department of Neurological Surgery, The University of Washington, Seattle, WA 98109, USA.
Spinal cord trauma leads to the destruction of the highly organized cytoarchitecture that carries information along the axis of the spinal column. Currently, there are no clinically accepted strategies that can help regenerate severed axons after spinal cord injury (SCI). Hydrogels are soft biomaterials with high water content that are widely used as scaffolds to interface with the central nervous system (CNS).
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December 2024
Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA; Shriners Hospitals for Children, St. Louis, MO 63110, USA; Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Mechanical Engineering, Washington University, St. Louis, MO 63130, USA; Cytex Therapeutics, Inc., Durham, NC 27704, USA. Electronic address:
Nat Mach Intell
September 2024
IBM Research Europe, Rüschlikon, Switzerland.
Understanding the spatial heterogeneity of tumours and its links to disease initiation and progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily rely on hematoxylin and eosin and serial immunohistochemistry staining, a cumbersome, tissue-exhaustive process that results in non-aligned tissue images. We propose the VirtualMultiplexer, a generative artificial intelligence toolkit that effectively synthesizes multiplexed immunohistochemistry images for several antibody markers (namely AR, NKX3.
View Article and Find Full Text PDFAdv Sci (Weinh)
November 2024
Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
Replicating the microstructural basis and the near 100% excitation energy transfer efficiency in naturally occurring light-harvesting complexes (LHCs) remains challenging in synthetic energy-harvesting devices. Biological photosynthesis regulates active ensembles of light-absorbing and funneling chlorophylls in proteins in response to fluctuating sunlight. Here, use of long-range liquid crystal (LC) ordering to tailor chain orientation and packing structure in liquid crystalline conjugated polymer (LCCP) layers for bio-mimicry of certain structural basis and light-harvesting properties of LHCs is reported.
View Article and Find Full Text PDFSci Rep
July 2024
School of Engineering, University of Southampton, Southampton, UK.
The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein.
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