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Language: enSubject: Monitoring

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    Article
    Citation - WoS: 40
    Citation - Scopus: 51
    Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion
    (Asme, 2020) Ozel, Tugrul; Altay, Ayca; Kaftanoglu, Bilgin; Leach, Richard; Senin, Nicola; Donmez, Alkan
    The powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.
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    Article
    Citation - Scopus: 1
    Design and Fabrication of Dual-Layered PCL/PEG Theranostic Platforms Using 3D Melt Electrowriting for Targeted Delivery and Post-Treatment Monitoring
    (Springer, 2025) Ege, Zeynep Ruya; Enguven, Gozde; Ege, Hasan; Durukan, Barkan Kagan; Sasmazel, Hilal Turkoglu; Gunduz, Oguzhan
    Advanced pancreatic tumors remain highly resistant to treatment due to their dense stromal environment and poor vascularization, which limit drug penetration and efficacy. Even after surgical resection, the high recurrence rate frequently leads to poor prognosis and mortality. To address these challenges, we developed solvent-free three-dimensional (3D) melt electrowritten (MEW) theranostic microfiber patches composed of poly(epsilon-caprolactone) (PCL) and polyethylene glycol (PEG). The patches were designed as dual-layered, 10-layer structures, with gemcitabine (GEM) loaded in the bottom five layers for localized chemotherapy to suppress tumor recurrence, and indocyanine green (ICG) incorporated in the top five layers to enable fluorescence-based post-surgical monitoring. Following fabrication, the patches were characterized both materially and in vitro, with GEM loaded at 100, 250, or 500 mu g/ml. PEG incorporation improved patch flexibility, facilitating the implantation process. In vitro release analysis demonstrated an initial burst followed by sustained, pH-responsive GEM release (similar to 70% at pH 4.0 and similar to 30% at pH 7.4 for 500 mu g/mL GEM at 168 h), while ICG release reached similar to 25% (pH 7.4) and similar to 10% (pH 4.0). GEM-loaded patches significantly reduced Capan-1 cell viability in a dose- and time-dependent manner, achieving >= 50% reduction at 72 h with 500 mu g/mL. Importantly, ICG incorporation did not impair GEM cytotoxicity; confocal imaging confirmed ICG internalization in viable cells and showed a decline in ICG-positive cells with increasing GEM dose, supporting the potential for concurrent therapy and monitoring. Thus, the theranostic patches enable localized, pH-responsive GEM delivery with integrated ICG-based fluorescence imaging, achieving significant cytotoxicity against pancreatic cancer cells while providing a platform for post-surgical surveillance. This solvent-free, layer-addressable approach represents a promising strategy for personalized, locally implantable theranostic systems in pancreatic cancer treatment.