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Aggregate 2024年第3期5卷 258-267页

作者： Ahmed Choukri Abdullah Olgac Ozarslan Sara Soltanabadi Farshi Sajjad Rahmani Dabbagh savas tasoglu Department of Mechanical Engineering KoçUniversitySariyerTurkey Department of Biomedical Sciences and Engineering KoçUniversitySariyerTurkey KoçUniversity Arçelik Research Center for Creative Industries(KUAR) KoçUniversitySariyerTurkey Koc University Is Bank Artificial Intelligence Lab(KUIS AILab) KoçUniversitySariyerTurkey KoçUniversity Translational Medicine Research Center(KUTTAM) KoçUniversitySariyerTurkey Bogaziçi Institute of Biomedical Engineering Bogaziçi UniversityCengelkoyTurkey

Melt electrowriting(MEW)is a solvent-free(i.e.,no volatile chemicals),a high-resolution three-dimensional(3D)printing method that enables the fabrication of semi-flexible structures with rigid *** its advantages,the MEW pro-cess is sensitive to changes in printing parameters(e.g.,voltage,printing pressure,and temperature),which can causefluid column breakage,jet lag,and/orfiber pulsing,ultimately deteriorating the resolution and printing *** spite of the commonly used error-and-trial method to determine the most suitable parameters,here,we present a machine learning(ML)-enabled image analysis-based method for determining the optimum MEW printing parameters through an easy-to-use graph-ical user interface(GUI).We trainedfive different ML algorithms using 168 MEW 3D print samples,among which the Gaussian process regression ML model yielded 93%accuracy of the variability in the dependent variable,0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line *** of ML with a control feedback loop and MEW can reduce the error-and-trial steps prior to the 3D printing process,decreasing the printing time(i.e.,increasing the overall throughput of MEW)and material waste(i.e.,improving the cost-effectiveness of MEW).Moreover,embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML-based optimization techniques in the industrial section(i.e.,for users with no ML skills).

关键词： 3D printing additive manufacturing feedback control image analysis machine learning melt electrowriting optimization polymer

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3D bioprinted organ-on-chips

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Aggregate 2023年第1期4卷 1-26页

作者： Sajjad Rahmani Dabbagh Misagh Rezapour Sarabi Mehmet Tugrul Birtek Nur Mustafaoglu Yu Shrike Zhang savas tasoglu Department of Mechanical Engineering Koc UniversitySariyerIstanbulTurkey Koc University Arçelik Research Center for Creative Industries(KUAR) Koc UniversitySariyerIstanbulTurkey Koc University Is Bank Artificial Intelligence Lab(KUIS AILab) Koc UniversitySariyerIstanbulTurkey Faculty of Engineering and Natural Sciences Sabanci UniversityTuzlaIstanbulTurkey Division of Engineering in Medicine Department of MedicineBrigham and Women’s HospitalHarvard Medical SchoolCambridgeMassachusettsUSA Physical Intelligence Department Max Planck Institute for Intelligent SystemsStuttgartGermany

Organ-on-a-chip(OOC)platforms recapitulate human in vivo-like conditions more realistically compared to many animal models and conventional two-dimensional cell *** setups benefit from continuous perfusion of cell cultures through microfluidic channels,which promotes cell viability and ***,microfluidic chips allow the integration of biosensors for real-time monitoring and analysis of cell interactions and responses to administered ***-dimensional(3D)bioprinting enables the fabrication of multicell OOC platforms with sophis-ticated 3D structures that more closely mimic human tissues.3D-bioprinted OOC platforms are promising tools for understanding the functions of organs,disruptive influences of diseases on organ functionality,and screening the efficacy as well as toxicity of drugs on ***,common 3D bioprinting techniques,advantages,and limitations of each method are ***,recent advances,applica-tions,and potentials of 3D-bioprinted OOC platforms for emulating various human organs are ***,current challenges and future perspectives of OOC plat-forms are discussed.

关键词： biomaterials bioprinting disease-on-a-chip microfluidics organ-on-a-chip

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