Use of a 3D inkjet-printed model to access dust particle toxicology in the human alveolar barrier.

Fine dust particles in the air travel into our body via the airway tract and cause severe respiratory diseases. Thus, the analysis of the effects of dust particles on the respiratory system has been receiving significant research interest. However, most studies on the toxicity of dust particles involve two-dimensional (2D) cell cultures, animal models, and epidemiology. Here, we inkjet-printed a three-dimensional (3D) alveolar barrier model to study how dust particles cause respiratory diseases. The three-layered in vitro model was exposed to A2 fine test dust with varying concentrations and exposure durations. The results highlighted the destruction of the tissue architecture along with apoptosis in the bioprinted alveolar barrier. The damage at the cellular level induced an increase in the amount of pro-inflammatory cytokines secreted, followed by triggering of the signal transduction pathway and activation of transcription factors. As a consequence of the release of cytokines, the extracellular matrix was degraded, which led to the collapse of the cell structure, loss of cell polarity, and a decrease in barrier tightness. Further, the pulmonary surfactant protein-related genes in the dust-treated alveolar tissue were investigated to evaluate the possible role of dust particles in pulmonary surfactant dysfunction. This study demonstrated the use of 3D-printed tissue model to evaluate the physiological impact of fine dust particles on cytotoxicity, alveolar barrier rigidity, and surfactant secretion of an alveolar barrier.

Tissue-specific gelatin bioink as a rheology modifier for high printability and adjustable tissue properties.

Decellularized extracellular matrix (dECM) has emerged as an exceptional biomaterial that effectively recapitulates the native tissue microenvironment for enhanced regenerative potential. Although various dECM bioinks derived from different tissues have shown promising results, challenges persist in achieving high-resolution printing of flexible tissue constructs because of the inherent limitations of dECM's weak mechanical properties and poor printability. Attempts to enhance mechanical rigidity through chemical modifications, photoinitiators, and nanomaterial reinforcement have often compromised the bioactivity of dECM and mismatched the desired mechanical properties of target tissues. In response, this study proposes a novel method involving a tissue-specific rheological modifier, gelatinized dECM. This modifier autonomously enhances bioink modulus pre-printing, ensuring immediate and precise shape formation upon extrusion. The hybrid bioink with GeldECM undergoes a triple crosslinking system-physical entanglement for pre-printing, visible light photocrosslinking during printing for increased efficiency, and thermal crosslinking post-printing during tissue culture. A meticulous gelatinization process preserves the dECM protein components, and optimal hybrid ratios modify the mechanical properties, tailoring them to specific tissues. The application of this sequential multiple crosslinking designs successfully yielded soft yet resilient tissue constructs capable of withstanding vigorous agitation with high shape fidelity. This innovative method, founded on mechanical modulation by GeldECM, holds promise for the fabrication of flexible tissues with high resilience.

3D Inkjet-Bioprinted Lung-on-a-Chip.

There is an urgent need for physiologically relevant and customizable biochip models of human lung tissue to provide a niche for lung disease modeling and drug efficacy. Although various lung-on-a-chips have been developed, the conventional fabrication method has been limited in reconstituting a very thin and multilayered architecture and spatial arrangements of multiple cell types in a microfluidic device. To overcome these limitations, we developed a physiologically relevant human alveolar lung-on-a-chip model, effectively integrated with an inkjet-printed, micron-thick, and three-layered tissue. After bioprinting lung tissues inside four culture inserts layer-by-layer, the inserts are implanted into a biochip that supplies a flow of culture medium. This modular implantation procedure enables the formation of a lung-on-a-chip to facilitate the culture of 3D-structured inkjet-bioprinted lung models under perfusion at the air-liquid interface. The bioprinted models cultured on the chip maintained their structure with three layers of tens of micrometers and achieved a tight junction in the epithelial layer, the critical properties of an alveolar barrier. The upregulation of genes involved in the essential functions of alveoli was also confirmed in our model. Our culture insert-mountable organ-on-a-chip is a versatile platform that can be applied to various organ models by implanting and replacing culture inserts. It is amenable to mass production and the development of customized models through the convergence with bioprinting technology.

3D pulmonary fibrosis model for anti-fibrotic drug discovery by inkjet-bioprinting.

Pulmonary fibrosis (PF) is known as a chronic and irreversible disease characterized by excessive extracellular matrix accumulation and lung architecture changes. Large efforts have been made to develop prospective treatments and study the etiology of pulmonary fibrotic diseases utilizing animal models and spherical organoids. As part of these efforts, we created an all-inkjet-printed three-dimensional (3D) alveolar barrier model that can be used for anti-fibrotic drug discovery. Then, we developed a PF model by treating the 3D alveolar barrier with pro-fibrotic cytokine and confirmed that it is suitable for the fibrosis model by observing changes in structural deposition, pulmonary function, epithelial-mesenchymal transition, and fibrosis markers. The model was tested with two approved anti-fibrotic drugs, and we could observe that the symptoms in the disease model were alleviated. Consequently, structural abnormalities and changes in mRNA expression were found in the induced fibrosis model, which were shown to be recovered in all drug treatment groups. The all-inkjet-printed alveolar barrier model was reproducible for disease onset and therapeutic effects in the human body. This finding emphasized that thein vitroartificial tissue with faithfully implemented 3D microstructures using bioprinting technology may be employed as a novel testing platform and disease model to evaluate potential drug efficacy.

All-Inkjet-Printed 3D Alveolar Barrier Model with Physiologically Relevant Microarchitecture.

With the outbreak of new respiratory viruses and high mortality rates of pulmonary diseases, physiologically relevant models of human respiratory system are urgently needed to study disease pathogenesis, drug efficacy, and pharmaceutics. In this paper, a 3D alveolar barrier model fabricated by printing four human alveolar cell lines, namely, type I and II alveolar cells (NCI-H1703 and NCI-H441), lung fibroblasts (MRC5), and lung microvascular endothelial cells (HULEC-5a) is presented. Automated high-resolution deposition of alveolar cells by drop-on-demand inkjet printing enables to fabricate a three-layered alveolar barrier model with an unprecedented thickness of ≈10 µm. The results show that the 3D structured model better recapitulate the structure, morphologies, and functions of the lung tissue, compared not only to a conventional 2D cell culture model, as expected, but also a 3D non-structured model of a homogeneous mixture of the alveolar cells and collagen. Finally, it is demonstrated that this thin multilayered model reproduce practical tissue-level responses to influenza infection. Drop-on-demand inkjet-printing is an enabling technology for customization, scalable manufacturing, and standardization of their size and growth, and it is believed that this 3D alveolar barrier model can be used as an alternative to traditional test models for pathological and pharmaceutical applications.

A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography.

Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.