Driving Deployment of Bioengineered Products-An Arduous, Sometimes Tedious, Challenging, Rewarding, Most Exciting Journey That Has to Be Made!

Bioengineering (Basel, Switzerland)（2024）

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Abstract

More than three decades ago, we embarked on a number of bioengineering explorations using the most advanced materials and fabrication methods. In every area we ventured into, it was our intention to ensure fundamental discoveries were deployed into the clinic to benefit patients. When we embarked on this journey, we did so without a road map, not even a compass, and so the path was arduous, sometimes tedious. Now, we can see the doorway to deployment on the near horizon. We now appreciate that overcoming the challenges has made this a rewarding and exciting journey. However, maybe we could have been here a lot sooner, and so maybe the lessons we have learned could benefit others and accelerate progress in clinical translation. Through a number of case studies, including neural regeneration, cartilage regeneration, skin regeneration, the 3D printing of capsules for islet cell transplantation, and the bioengineered cornea, here, we retrace our steps. We will summarise the journey to date, point out the obstacles encountered, and celebrate the translational impact. Then, we will provide a framework for project design with the clinical deployment of bioengineered products as the goal.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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