Ms. Zainab N. Khan Profile

Zainab N. Khan

at King Abdullah Univ of Science and Technology

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Publications (3)

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Proceedings Article | 6 March 2023 Presentation + Paper

A predictive machine learning model to optimize flow rates on an integrated microfluidic pumping system for peptide-based 3D bioprinting

Noofa Hammad, Zainab Khan, Alexander Valle-Pérez, Charlotte Hauser

Proceedings Volume 12374, 1237402 (2023) https://doi.org/10.1117/12.2650440

KEYWORDS: 3D modeling, Bioprinting, Data modeling, Printing, Performance modeling, Machine learning, Education and training, 3D printing, Statistical modeling, Microfluidics

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3D bioprinting technology has promising applications in regenerative medicine and drug testing in the near future for the fabrication of patient-specific replicas of human organs, bones, etc. Previously, we have developed a dual-arm 3D bioprinting system, TwinPrint, using two robots to cooperatively bioprint peptide-based soft matter structures. During 3D bioprinting, optimization of extrusion flow rates of peptide bioinks is critical for efficient cell encapsulation and mechanical stability. Currently, it is dependent on user knowledge and experience from past experiments which may vary in reliability and quality. Thus, this paper proposes a multi-output regression machine learning model to predict optimized peptide flow rates for the microfluidic-based pumping component of the TwinPrint system. Specifically, parameters including peptide bioink type, peptide concentration, phosphate-buffered saline (PBS) concentration and nozzle size are used as inputs for machine learning methods. The output is estimated optimal flow rates of the bioink fluid components, essential in obtaining a consistent amount of gel extrusion. The dataset used to train and test the predictive model is collected from numerous bioprinting experiments conducted on-site. Performance evaluation metrics are applied to examine and assess the developed model, which is incorporated within our in-house developed TwinPrint software to automatically suggest flow rates once the user specifies initial parameters. Finally, the flow rate predictive software in conjunction with the advanced dual-arm robotic system hardware are demonstrated in this work to pave the way for automated optimization of 3D bioprinting for enhanced printability, repeatability and standardization

Proceedings Article | 5 March 2021 Presentation + Paper

Time-dependent pulsing of microfluidic pumps to enhance 3D bioprinting of peptide bioinks

Zainab Khan, Kowther Kahin, Charlotte A. Hauser

Proceedings Volume 11637, 1163709 (2021) https://doi.org/10.1117/12.2578830

KEYWORDS: Microfluidics, Printing, Tissue engineering, Skin, Fluid dynamics, 3D printing

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Using bioinks for 3D bioprinting of cellular constructs remains a challenge due to factors including viscosity, fluid dynamics and shear stress. The encapsulation of cells within the bioinks directly affects the quality of 3D bioprinting and microfluidic pumping is a commonly used supporting approach. The accuracy of microfluidic pumps can be further improved by introducing various mixing techniques. However, many of these techniques introduce complex geometries or external fields. In this study, we used a simple control technique of time-dependent pulsing for instant gelation of the peptide bioinks and observed its effect during the bioprinting process. Various time-dependent periodic signals are imposed on to a stable flow cycle and the effects are analyzed. The microfluidic pumps are programmed with different flow patterns represented by low frequency sinusoidal pulses, ramp inputs, and duty cycle pulses. Different combinations of these pulses are tested to achieve an optimal pulse for improved quality of printed constructs. Time-varied pulsing of microfluidic pumps, particularly as square waveforms, is found to provide better continuous flow and avoid material buildup within the extruder unit when compared to pumping at a constant flow rate with manual tuning. Clogging is avoided since the gelation rate is periodically reduced which avoids gel clumps in the printed constructs. This study substantially improves the use of suitable peptide bioinks, standardizes the 3D bioprinting process, and reduces clogging and clumping during printing. Our findings allow for printing of more accurate and complex constructs for applications in tissue engineering, such as skin grafting, and other regenerative medical applications.

Proceedings Article | 4 March 2019 Presentation + Paper

Development of a robotic 3D bioprinting and microfluidic pumping system for tissue and organ engineering

Kowther Kahin, Zainab Khan, Malak Albagami, Sadaf Usman, Sarah Bahnshal, Hibatallah Alwazani, M. Majid, Sakandar Rauf, Charlotte Hauser

Proceedings Volume 10875, 108750Q (2019) https://doi.org/10.1117/12.2507237

KEYWORDS: Robotics, Printing, 3D printing, 3D modeling, Tissues, Skin, MATLAB, Microfluidics, Prototyping, 3D image processing

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The technology of 3D bioprinting has gained significant interest in biomedical engineering, regenerative medicine, and the pharmaceutical industry. Providing a new scope in tissue and organ printing, 3D bioprinters are becoming commercialized for biological processes. However, the current technology is costly, ranging from USD$9,000-$30,000 and is limited to customized extrusion methods. Multiple microfluidic pump systems for bioink extrusion are commercially available at USD$30,000. Additionally, the use of Cartesian systems for 3D printing restricts the user to three axes of movement and makes multi-material modeling a challenge. Consequently, it was proposed to design a cost effective robotic 3D bioprinting system, compatible with peptide bioinks which were developed at KAUST Laboratory for Nanomedicine. The components of the system included a programmable robotic arm, an extruder for bioprinting, and multiple microfluidic pumps. The extruder was designed using a coaxial nozzle made of three inlets and one outlet. The programmable microfluidic pumps transported the peptide bioink, phosphate buffer saline (PBS) and human skin fibroblast cells (in cell culture media solution) through the nozzle to extrude a peptide nanogel thread. Model cell structures were printed and monitored for a period of two weeks and subsequently found to be alive and healthy. The system was kept well under a budget of USD$3,500. Future modifications of the current system will include adding a custom bioprinting arm to allow multi-material printing which can fully integrate and synchronize between the pumps and the robotic arm. This system will allow the production of a more advanced robotic arm-based 3D bioprinting system in the future.

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