The adaptable 3D biopriting technique can increase the output of engineering fabrics

The field of tissue engineering aims to reproduce the structure and function of real biological tissues. This fabric designed has potential applications in the modeling of diseases, the discovery of drugs and implantable transplants.

The 3D bio-act, which uses living cells, biocompatible materials and growth factors to build structures of tissue and three-dimensional organs, has become a key tool in the field. To date, one of the most used approaches for biopriting is based on additive manufacturing techniques and digital models, depositing 2D layers of bio-links, composed of cells in a soft gel, in a support bath, layer by layer, to build a 3D structure. Although these techniques make it possible to make complex architectures with features that are not easy to build manually, current approaches have limits.

“A major drawback of current 3D bioprimimation approaches is that they do not integrate processes control methods that limit defects in printed fabrics. The incorporation of processes control could improve reproducibility between tissues and improve the effectiveness of resources, for example, limiting material waste,” explains Ritu Raman, the EUGENE BELL CAREAIN DEVELOP Mechanical engineering.

“Given the diversified range of available 3D biopriting tools, it is important to develop processes optimization techniques that are modular, efficient and accessible.”

The need motivated Raman to seek the expertise of Professor Bianca Colosimo from the Polytechnic University of Milan, also known as Polimi. Colosimo recently completed SABBATIC SABBATIC LEARD, organized by John Hart, 1922 class teacher, co -director of the MIT initiative for the new manufacturing, director of the Center for Advanced Production Technologies and head of the Mechanical Engineering Department.

“Artificial intelligence and data exploration already reshapes our daily life, and their impact will be even deeper in the emerging field of 3D bioprimimation and in manufacturing as a whole,” explains Colosimo.

During her sabbatical MIT leave, she collaborated with Raman and her team to co-develop a solution that represents a first step towards the smart bioprine.

“This solution is now available in our Polimi and MIT laboratories, serving as a twin platform to exchange data and results in different environments and pave the way for many new joint projects in the coming years,” explains Colosimo.

A new article by Raman, Colosimo and the main authors Giovanni Zanderno, a Rocca Fellow in Polimi, and Ferdows Afghah of the MIT published in the journal Device Presents a new technique that takes up this challenge. The team has built and validated a modular monitoring technique, at low cost and printer which incorporates a compact tool for layer -layer imaging.

In their method, a digital microscope capture high -resolution images of fabrics during printing and quickly compares them to the planned design with an IA -based image analysis pipeline.

“This method allowed us to quickly identify printing defects, such as the deposit of too much or too little bio-ink, thus helping us to identify the optimal printing parameters for a variety of different materials,” explains Raman.

“The approach is a low-cost solution-less than $ 500-Scalable and adaptable which can be easily implemented on any standard 3D bioprinter. Here at MIT, the surveillance platform has already been integrated into the 3D-sur-Biopritation facilities in the hangar.

“Beyond the MIT, our research offers a practical path to greater reproducibility, better sustainability and automation in the field of tissue engineering. This research could have a positive impact on human health by improving the quality of the tissues that we manufacture to study and treat debilitating injuries and diseases.”

The authors indicate that the new method is more than a monitoring tool. It also serves as the basis for intelligent processes control in integrated biopritis. By allowing real -time inspection, adaptive correction and automated settings of parameters, researchers provide that the approach can improve reproducibility, reduce material waste and accelerate processes optimization for real world applications in tissue engineering.

This story is republished with the kind authorization of Mit News (Web.mit.edu/newsoffice/), a popular site that covers news of research, innovation and MIT teaching.