Osaka, Japan – What if organ damage could be repaired by simply growing a new organ in the lab? Improving researchers’ ability to print live cells on demand into geometrically well-defined, soft complex 3D architectures is essential to such work, as well as for animal-free toxicological testing.

In a study recently published in ACS Biomaterials Science and Engineering, researchers from Osaka University have overcome prior limitations that have hindered cell growth and the geometrical fidelity of bioprinted architectures. This work might help bring 3D-printed cell constructs closer to mimicking biological tissue and organs.

Ever since bioprinting was first reported in 1988 by using a standard inkjet printer, researchers have explored the potential of this layer-by-layer tissue assembly procedure to regrow damaged body parts and test medical hypotheses. Bioprinting is to eject a cell-containing “ink” from a printing nozzle to form 3D structures. It is usually easier to print hard rather than soft structures. However, soft structures are preferable in terms of cell growth in the printed structures. When printing soft structures, doing so in a printing support is effective; however, solidification of ink in the support filled in a vessel can result in its contamination with unwanted substances from the support. Ink solidification into a soft matrix using a printing support without contamination, while retaining cell viability, was the goal of this work.

“In our approach, a 3D printer alternately dispenses the cell-containing ink and a printing support,” explains Takashi Kotani, lead author of the study. “The interesting point is that the support also plays a role in facilitating the solidification of the ink. All that’s necessary for ink solidification is in the support, and after removing the support, the geometry of the soft printed cell structures remains intact.”

Hydrogen peroxide from the support enabled an enzyme in the ink to initiate gelation of the ink, resulting in a gel-enclosed cell assembly within a few seconds. This rapid gelation prevented contamination of the assembly during formation. After removing the support, straightforward 3D constructs such as inverted trapezium geometries as well as human nose shapes—including bridges, holes, and overhangs—were readily obtained.

“We largely retain mouse fibroblast cell geometry and growth, and the cells remain viable for at least two weeks,” says Shinji Sakai, senior author. “These cells also adhere to and proliferate on our constructs, which highlights our work’s potential in tissue engineering.”

This new technique is an important step forward to engineering human cell assemblies and tissues. Further work might involve further optimizing the ink and support, as well as incorporating blood vessels into the artificial tissue to improve its resemblance to physiological architectures. Regenerative medicine, pharmaceutical toxicology, and other fields will all benefit from this work and further improvements in the precise fidelity of bioprinting.

###
The article, “Horseradish Peroxidase-Mediated Bioprinting via Bioink Gelation by Alternately Extruded Support Material,” was published in ACS Biomaterials Science and Engineering at DOI: https://pubs.acs.org/doi/10.1021/acsbiomaterials.3c00996

About Osaka University 
Osaka University was founded in 1931 as one of the seven imperial universities of Japan and is now one of Japan’s leading comprehensive universities with a broad disciplinary spectrum. This strength is coupled with a singular drive for innovation that extends throughout the scientific process, from fundamental research to the creation of applied technology with positive economic impacts. Its commitment to innovation has been recognized in Japan and around the world, being named Japan’s most innovative university in 2015 (Reuters 2015 Top 100) and one of the most innovative institutions in the world in 2017 (Innovative Universities and the Nature Index Innovation 2017). Now, Osaka University is leveraging its role as a Designated National University Corporation selected by the Ministry of Education, Culture, Sports, Science and Technology to contribute to innovation for human welfare, sustainable development of society, and social transformation. 
Website: https://resou.osaka-u.ac.jp/en

Voice pathology refers to a problem arising from abnormal conditions, such as dysphonia, paralysis, cysts, and even cancer, that cause abnormal vibrations in the vocal cords (or vocal folds). In this context, voice pathology detection (VPD) has received much attention as a non-invasive way to automatically detect voice problems. It consists of two processing modules: a feature extraction module to characterize normal voices and a voice detection module to detect abnormal ones. Machine learning methods like support vector machines (SVM) and convolutional neural networks (CNN) have been successfully utilized as pathological voice detection modules to achieve good VPD performance. Also, a self-supervised, pretrained model can learn generic and rich speech feature representation, instead of explicit speech features, which further improves its VPD abilities. However, fine-tuning these models for VPD leads to an overfitting problem, due to a domain shift from conversation speech to the VPD task. As a result, the pretrained model becomes too focused on the training data and does not perform well on new data, preventing generalization.

To mitigate this problem, a team of researchers from Gwangju Institute of Science and Technology (GIST) in South Korea, led by Prof. Hong Kook Kim, has proposed a groundbreaking contrastive learning method involving Wave2Vec 2.0—a self-supervised pretrained model for speech signals—with a novel approach called adversarial task adaptive pretraining (A-TAPT). Herein, they incorporated adversarial regularization during the continual learning process.

The researchers performed various experiments on VPD using the Saarbrucken Voice Database, finding that the proposed A-TAPT showed a 12.36% and 15.38% improvement in the unweighted average recall (UAR), when compared to SVM and CNN ResNet50, respectively. It also achieved a 2.77% higher UAR than the conventional TAPT learning. This shows that A-TAPT is better at mitigating the overfitting problem.

Talking about the long-term implications of this work, Mr. Park says who is the first author of this article: “In a span of five to 10 years, our pioneering research in VPD, developed in collaboration with MIT, may fundamentally transform healthcare, technology, and various industries. By enabling early and accurate diagnosis of voice-related disorders, it could lead to more effective treatments, improving the quality of life of countless individuals.”

Their article was made available online on 24 July 2023 and published in Volume 30 of the journal IEEE Signal Processing Letters. Their research, performed as part of a GIST funded project entitled ‘Extending Contrastive Learning to New Data Modalities and Resource-Limited Scenarios’ in collaboration with the MIT, Cambridge, MA, USA, embarks on a path that promises to redefine the landscape of VPD and artificial intelligence in medical applications. The project team includes Hong Kook Kim (EECS, GIST) and Dina Katabi (EECS, MIT) as Principal Investigators (PIs) as well as Jeany Son (AI Graduate School, GIST), Moongu Jeon (EECS, GIST), and Piotr Indyk (EECS, MIT) as co-PIs. 

Prof. Kim points out: “Our partnership with MIT has been instrumental in this success, facilitating ongoing exploration of contrastive learning. The collaboration is more than a mere partnership; it’s a fusion of minds and technologies that strive to reshape not only medical applications but various domains requiring intelligent, adaptive solutions.”

Furthermore, it is promising for health monitoring in vocally demanding professions like call center agent, ensuring robust voice authentication in security systems, making artificial intelligence voice assistants more responsive and adaptive, and developing tools for voice quality enhancement in the entertainment industry.   

Here’s hoping for further innovation in the field of self-supervised learning and contrastive learning!

***

Reference

DOI: https://doi.org/10.1109/LSP.2023.3298532

Affiliations:        

¹AI Graduate School and School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology

²Department of Electrical Engineering and Computer Science, Lab for Computer Science and Artificial Intelligence, MIT Center for Wireless Networks and Mobile Computing, MIT

About the Gwangju Institute of Science and Technology (GIST)
Website: http://www.gist.ac.kr/