Engineered tissue barrier models offer alternatives in toxicology and disease research. To mimic barrier-tissue microenvironment, a porous membrane that can approach the stiffness of physiological basement membranes is required. While several biocompatible membranes with micrometer range thickness (10 μm) and a stiffness less than polystyrene (3 GPa) or polyethylene (PET, 2 GPa), have been developed, there has been little effort to optimize the process to enable rapid and reproducible pore production in these membranes. Here, we investigate the use of laser irradiation with femtosecond (fs) pulses because the combination of high-precision and cold-ablation causes minimal damage to polymeric membranes. This process enables automated, high-throughput and reproducible fabrication of thin, microporous membranes that can be utilized to culture cells at air-liquid interface (ALI), a unique culture technique that simulates the tissue-barrier microenvironment. We show the optimization of laser parameters on a thin polyurethane membrane and patterned pores with an average diameter of 5 μm. Tissue was cultured at ALI for 28 days to demonstrate the membrane's utility in constructing a tissue barrier model. These results confirm the utilization of fs laser machining as a viable method for creating a porous barrier substrate in tissue engineering platforms.
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http://dx.doi.org/10.1021/acsbiomaterials.8b00578 | 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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