skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on July 1, 2026

Title: Machine learning-assisted stiffness prediction in high-cell-density bioprinting
Abstract Bioprinting of cell-laden hydrogels is a rapidly growing field in tissue engineering. The advent of digital light processing (DLP) three-dimensional (3D) bioprinting technique has revolutionized the fabrication of complex 3D structures. By adjusting light exposure, it becomes possible to control the mechanical properties of the structure, a critical factor in modulating cell activities. To better mimic cell densities in real tissues, recent progress has been made in achieving high-cell-density (HCD) printing with high resolution. However, regulating the stiffness in HCD constructs remains challenging. The large volume of cells greatly affects the light-based DLP bioprinting by causing light absorption, reflection, and scattering. Here, we introduce a neural network-based machine learning technique to predict the stiffness of cell-laden hydrogel scaffolds. Using comprehensive mechanical testing data from 3D bioprinted samples, the model was trained to deliver accurate predictions. To address the demand of working with precious and costly cell types, we employed various methods to ensure the generalizability of the model, even with limited datasets. We demonstrated a transfer learning method to achieve good performance for a precious cell type with a reduced amount of data. The chosen method outperformed many other machine learning techniques, offering a reliable and efficient solution for stiffness prediction in cell-laden scaffolds. This breakthrough paves the way for the next generation of precision bioprinting and more customized tissue engineering.  more » « less
Award ID(s):
2135720
PAR ID:
10659919
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Springer
Date Published:
Journal Name:
Bio-Design and Manufacturing
Volume:
8
Issue:
4
ISSN:
2096-5524
Page Range / eLocation ID:
543 to 557
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; (, Biofabrication)
    Abstract Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes. 
    more » « less
  2. ; (, Gels)
    Temperature-Controlled-Cryoprinting (TCC) is a new 3D bioprinting technology that allows for the fabrication and cryopreservation of complex and large cell-laden scaffolds. During TCC, bioink is deposited on a freezing plate that descends further into a cooling bath, keeping the temperature at the nozzle constant. To demonstrate the effectiveness of TCC, we used it to fabricate and cryopreserve cell-laden 3D alginate-based scaffolds with high cell viability and no size limitations. Our results show that Vero cells in a 3D TCC bioprinted scaffold can survive cryopreservation with a viability of 71%, and cell viability does not decrease as higher layers are printed. In contrast, previous methods had either low cell viability or decreasing efficacy for tall or thick scaffolds. We used an optimal temperature profile for freezing during 3D printing using the two-step interrupted cryopreservation method and evaluated drops in cell viability during the various stages of TCC. Our findings suggest that TCC has significant potential for advancing 3D cell culture and tissue engineering. 
    more » « less
  3. ; ; (, Advanced Materials)
    Abstract Bioprinting is an emerging approach for fabricating cell‐laden 3D scaffolds via robotic deposition of cells and biomaterials into custom shapes and patterns to replicate complex tissue architectures. Bioprinting uses hydrogel solutions called bioinks as both cell carriers and structural components, requiring bioinks to be highly printable while providing a robust and cell‐friendly microenvironment. Unfortunately, conventional hydrogel bioinks have not been able to meet these requirements and are mechanically weak due to their heterogeneously crosslinked networks and lack of energy dissipation mechanisms. Advanced bioink designs using various methods of dissipating mechanical energy are aimed at developing next‐generation cellularized 3D scaffolds to mimic anatomical size, tissue architecture, and tissue‐specific functions. These next‐generation bioinks need to have high print fidelity and should provide a biocompatible microenvironment along with improved mechanical properties. To design these advanced bioink formulations, it is important to understand the structure–property–function relationships of hydrogel networks. By specifically leveraging biophysical and biochemical characteristics of hydrogel networks, high performance bioinks can be designed to control and direct cell functions. In this review article, current and emerging approaches in hydrogel design and bioink reinforcement techniques are critically evaluated. This bottom‐up perspective provides a materials‐centric approach to bioink design for 3D bioprinting. 
    more » « less
  4. ; ; ; ; ; ; ; ; ; (, Aggregate)
    Three‐dimensional (3D) printing is an emerging technique that has shown promising success in engineering human tissues in recent years. Further development of vat‐photopolymerization printing modalities has significantly enhanced the complexity level for 3D printing of various functional structures and components. Similarly, the development of microfluidic chip systems is an emerging research sector with promising medical applications. This work demonstrates the coupling of a digital light processing (DLP) printing procedure with a microfluidic chip system to produce size‐tunable, 3D‐printable porosities with narrow pore size distributions within a gelatin methacryloyl (GelMA) hydrogel matrix. It is found that the generation of size‐tunable gas bubbles trapped within an aqueous GelMA hydrogel‐precursor can be controlled with high precision. Furthermore, the porosities are printed in two‐dimensional (2D) as well as in 3D using the DLP printer. In addition, the cytocompatibility of the printed porous scaffolds is investigated using fibroblasts, where high cell viabilities as well as cell proliferation, spreading, and migration are confirmed. It is anticipated that the strategy is widely applicable in a range of application areas such as tissue engineering and regenerative medicine, among others. 
    more » « less
  5. ; ; (, Proceedings of the IISE Annual Conference & Expo 2023)
    Babski-Reeves, K; Eksioglu, B; Hampton, D. (Ed.)
    Extrusion-based three-dimensional (3D) bio-printing is one of the several 3D bioprinting methods that is frequently used in current times. This method enables the accurate deposition of cell-laden bio-ink while ensuring a predetermined scaffold architecture that may allow living tissue regeneration. Natural hydrogels are a strong choice for bio-ink formulation for the extrusion-based 3D bioprinting method because they have a combination of unique properties, which include biocompatibility, reduced cell toxicity, and high-water content. However, due to its low mechanical integrity, hydrogel frequently struggles to retain structural stability. To overcome this challenge, we evaluated the rheological characteristics of distinct hybrid hydrogels composed of carboxymethyl cellulose (CMC), a widely used alginate, and nanofibers generated from cellulose (TEMPO-mediated nano-fibrillated cellulose, TONFC). Therefore, to examine the rheological properties, a set of compositions was developed incorporating CMC (1%–4%), alginate (1%–4%), and higher and lower contents of TONFC (0.5%) and (0.005%) respectively. From the flow diagram, the shear thinning coefficients of n and K were calculated, which were later linked to the 3D printability. With the guidance of diverse nanofiber ratios, it is possible to regulate the rheological properties and create 3D bioprinted scaffolds with well-defined scaffold architecture. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email