Evaluation of the Reproducibility and Robustness of Extrusion-Based Bioprinting Processes Applying a Flow Sensor

Bioinks and biofabrication techniques for biosensors development: A review

3D bioprinting technologies and bioink development are enabling significant advances in miniaturized and integrated biosensors. For example, bioreceptors can be immobilized within a porous 3D structure to significantly amplify the signal, while biocompatible and mechanically flexible systems uniquely enable wearable chem- and bio-sensors. This advancement is accelerating translation by enabling the production of high performance, reproducible, and flexible analytical devices. The formulation of the bioink plays a crucial role in determining the bio-functionality of the resulting printed structures, e.g., the porosity that allows the analyte to diffuse through the 3D structure, the affinity and avidity of the receptors, etc. This review explores the next generation of advanced bioinks for biosensor development and provides insights into the latest cutting-edge bioprinting technologies. The bioprinting methods available for biosensor fabrication including inkjet, extrusion, and laser-based bioprinting, are discussed. The advantages and limitations of each method are analysed, and recent advancements in bioprinting technologies are presented. The review then delves into the properties of advanced bioinks, such as biocompatibility, printability, stability, and applicability. Different types of advanced bioinks are explored, including multicomponent, stimuli-responsive, and conductive bioinks. Finally, the next generation of bioinks for biosensors is considered, identifying possible new opportunities and challenges. Overall, this literature review highlights the combined importance of bioink formulation and bioprinting methods for the development of high-performance analytical biosensors.

The Past, Present, and Future of Tubular Melt Electrowritten Constructs to Mimic Small Diameter Blood Vessels - A Stable Process?

Melt Electrowriting (MEW) is a continuously growing manufacturing platform. Its advantage is the consistent production of micro- to nanometer fibers, that stack intricately, forming complex geometrical shapes. MEW allows tuning of the mechanical properties of constructs via the geometry of deposited fibers. Due to this, MEW can create complex mechanics only seen in multi-material compounds and serve as guiding structures for cellular alignment. The advantage of MEW is also shown in combination with other biotechnological manufacturing methods to create multilayered constructs that increase mechanical approximation to native tissues, biocompatibility, and cellular response. These features make MEW constructs a perfect candidate for small-diameter vascular graft structures. Recently, studies have presented fascinating results in this regard, but is this truly the direction that tubular MEW will follow or are there also other options on the horizon? This perspective will explore the origins and developments of tubular MEW and present its growing importance in the field of artificial small-diameter vascular grafts with mechanical modulation and improved biomimicry and the impact of it in convergence with other manufacturing methods and how future technologies like AI may influence its progress.

Enhancing quality control in bioprinting through machine learning

Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

Tissue-Engineered Constructions in Biophysics, Neurology and Other Fields and Branches of Medicine

This paper describes the gangliopexy method, a method for creating a new center of local neurohumoral regulation, based on the formation of new connections discovered between the nervous system and the vascular system. The prospects for the development of this method are studied. At the same time, novel concepts about the cycles of nitric oxide and the superoxide anion radical are introduced. A possible role of these cycles is examined in the protection of cells and the body as a whole against oxidative and nitrosative stress, which develops when (in 5-30% of cases) destructive changes in the displaced ganglion lead to vascular complications and an increased risk of mortality. Mechanisms that can protect nerve cells, prevent the development of destructive changes in these cells and reduce the risk of mortality are also investigated.

Structured Data Storage for Data-Driven Process Optimisation in Bioprinting

Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.