Mimicking the architecture of the extracellular matrix is an effective strategy for tissue engineering. Composite nanofibers similar to natural bone structure can be prepared via an electrospinning technique and used in biomedical applications. Stem cells from human exfoliated deciduous teeth (SHEDs) can differentiate into multiple cell lineages, such as cells that are alternative sources of stem cells for tissue engineering. Strontium has important functions in bone remodeling; for example, this element can simulate bone formation and decrease bone resorption. Incorporating strontium phosphate into nanofibers provides a potential material for bone tissue engineering. This study investigated the potential of poly(ε-caprolactone) (PCL) nanofibers coated or blended with strontium phosphate for the osteogenic differentiation of SHEDs. Cellular morphology and MTT assay revealed that nanofibers effectively support cellular attachment, spreading, and proliferation. Strontium-loaded PCL nanofibers exhibited higher expressions of collagen type I, alkaline phosphatase, biomineralization, and bone-related genes than pure PCL nanofibers during the osteogenic differentiation of SHEDs. This study demonstrated that strontium can be an effective inducer of osteogenesis for SHEDs. Understanding the function of bioceramics (such as strontium) is useful in designing and developing strategies for bone tissue engineering.
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http://dx.doi.org/10.1016/j.msec.2014.10.076 | DOI Listing |
Tissue Eng Regen Med
January 2025
College of Materials Science and Engineering, Hunan University, Changsha, 410072, People's Republic of China.
Background: Tissue engineering holds promise for vascular repair and regeneration by mimicking the extracellular matrix of blood vessels. However, achieving a functional and thick vascular wall with aligned fiber architecture by electrospinning remains a significant challenge.
Methods: A novel electrospinning setup was developed that utilizes an auxiliary electrode and a spring.
Arch Dermatol Res
January 2025
Burn and Wound Repair Center, The Third Hospital of Hebei Medical University, No. 139, Ziqiang Road, Shijiazhuang, Hebei Province, 050035, China.
This study aimed to investigate the role of transforming growth factor-beta 3 (TGF-β3) secreted by adipose-derived stem cells (ADSCs) in suppressing melanin synthesis during the wound healing process, particularly in burn injuries, and to explore the underlying mechanisms involving the cAMP/PKA signaling pathway. ADSCs were isolated from C57BL/6 mice and characterized using flow cytometry and differentiation assays. A burn injury model was established in mice, followed by UVB irradiation to induce hyperpigmentation.
View Article and Find Full Text PDFJ Biophotonics
January 2025
Department of Electrical Engineering, Columbia University, New York, New York, USA.
Epicardial catheter ablation is necessary to address ventricular tachycardia targets located far from the endocardium, but epicardial adipose tissue and coronary blood vessels can complicate ablation. We demonstrate that catheter-based near-infrared spectroscopy (NIRS) can identify these obstacles to guide ablation. Eighteen human ventricles were mapped ex vivo using NIRS catheters with optical source-detector separations (SDSs) of 0.
View Article and Find Full Text PDFBioelectromagnetics
January 2025
Seibersdorf Labor GmbH, Seibersdorf, Austria.
The electrical conductivity of human tissues is a major source of uncertainty when modelling the interactions between electromagnetic fields and the human body. The aim of this study is to estimate human tissue conductivities in vivo over the low-frequency range, from 30 Hz to 1 MHz. Noninvasive impedance measurements, medical imaging, and 3D surface scanning were performed on the forearms of ten volunteer test subjects.
View Article and Find Full Text PDFMicrosc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
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