Modular design workflow for 3D printable bioresorbable patient-specific bone scaffolds: extended features and clinical validation.

A previously in-house developed patient-specific scaffold design workflow was extended with new features to overcome several limitations and to broaden its adaptability to diverse bone defects, thereby enhancing its fit for routine clinical use. It was applied to three clinical cases for further validation. A virtual surgical resection tool was developed to remove regions of the bone defect models.

Tissue-engineered patient-derived osteosarcoma models dissecting tumour-bone interactions.

Osteosarcoma is the most common malignant bone tumor, primarily affecting children and young adults. For these young patients, the current treatment options for osteosarcoma impose considerable constraints on daily life with significant morbidity and a low survival rate. Despite ongoing research efforts, the 5-year survival rate of first-diagnosed patients without metastases has not changed in the past four decades.

Lost in translation: the lack of agreement between surgeons and scientists regarding biomaterials research and innovation for treating bone defects.

Background: With over 2 million grafts performed annually, bone ranks second only to blood in the frequency of transplants. This high demand is primarily driven by the persistent challenges posed by bone defects, particularly following trauma or surgical interventions such as tumour excision. The demand for effective and efficient treatments has increased exponentially in the twenty-first century.

Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting.

Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology used in biomedical engineering, regenerative medicine, and soft robotics. Its transition from academia to industry faces challenges such as slow experimentation, low printing throughput, poor reproducibility, and user-dependent operation, largely due to the nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning to monitor and analyze the process in real-time through imaging of the MEW jet between the nozzle-collector gap.