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The airway epithelium lining the luminal surface of the respiratory tract creates a protective barrier that ensures maintenance of tissue homeostasis and prevention of respiratory diseases. The airway epithelium, unfortunately, is frequently injured by inhaled toxic materials, trauma, or medical procedures. Substantial or repeated airway epithelial injury can lead to dysregulated intrinsic repair pathways and aberrant tissue remodeling that can lead to dysfunctional airway epithelium. While disruption in the epithelial integrity is directly linked to degraded epithelial barrier function, the correlation between the structure and function of the airway epithelium remains elusive. In this study, we quantified the impact of acutely induced airway epithelium injury on disruption of the epithelial barrier functions. By monitoring alternation of the flow motions and tissue bioimpedance at local injury site, degradation of the epithelial functions, including mucociliary clearance and tight/adherens junction formation, were accurately determined with a high spatiotemporal resolution. Computational models that can simulate and predict the disruption of the mucociliary flow and airway tissue bioimpedance have been generated to assist interpretation of the experimental results. Collectively, findings of this study advance our knowledge of the structure–function relationships of the airway epithelium that can promote development of efficient and accurate diagnosis of airway tissue injury. 

Abstract Ionizable lipid nanoparticles (LNPs) are pivotal in combating COVID‐19, and numerous preclinical and clinical studies have highlighted their potential in nucleic acid‐based therapies and vaccines. However, the effectiveness of endosomal escape for the nucleic acid cargos encapsulated in LNPs is still low, leading to suboptimal treatment outcomes and side effects. Hence, improving endosomal escape is crucial for enhancing the efficacy of nucleic acid delivery using LNPs. Here, a mechanical oscillation (frequency: 65 Hz) is utilized to prompt the LNP‐mediated endosomal escape. The results reveal this mechanical oscillation can induce the combination and fusion between LNPs with opposite surface charges, enhance endosomal escape of mRNA, and increase the transfection efficiency of mRNA. Additionally, cell viability remains high at 99.3% after treatment with oscillation, which is comparable to that of untreated cells. Furthermore, there is no obvious damage to mitochondrial membrane potential and Golgi apparatus integrity. Thus, this work presents a user‐friendly and safe approach to enhancing endosomal escape of mRNA and boosting gene expression. As a result, this work can be potentially utilized in both research and clinical fields to facilitate LNP‐based delivery by enabling more effective release of LNP‐encapsulated cargos from endosomes. 

Recent synergistic advances in organ-on-chip and tissue engineering technologies offer opportunities to create in vitro -grown tissue or organ constructs that can faithfully recapitulate their in vivo counterparts. Such in vitro tissue or organ constructs can be utilized in multiple applications, including rapid drug screening, high-fidelity disease modeling, and precision medicine. Here, we report an imaging-guided bioreactor that allows in situ monitoring of the lumen of ex vivo airway tissues during controlled in vitro tissue manipulation and cultivation of isolated rat trachea. Using this platform, we demonstrated partial removal of the rat tracheal epithelium ( i.e. , de-epithelialization) without disrupting the underlying subepithelial cells and extracellular matrix. Through different tissue evaluation assays, such as immunofluorescent staining, DNA/protein quantification, and electron beam microscopy, we showed that the epithelium of the tracheal lumen can be effectively removed with negligible disruption in the underlying tissue layers, such as cartilage and blood vessel. Notably, using a custom-built micro-optical imaging device integrated with the bioreactor, the trachea lumen was visualized at the cellular level, and removal of the endogenous epithelium and distribution of locally delivered exogenous cells were demonstrated in situ . Moreover, the de-epithelialized trachea supported on the bioreactor allowed attachment and growth of exogenous cells seeded topically on its denuded tissue surface. Collectively, the results suggest that our imaging-enabled rat trachea bioreactor and localized cell replacement method can facilitate creation of bioengineered in vitro airway tissue that can be used in different biomedical applications. 

When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color bleeding, haze, etc.) and this diversity in appearance makes flare removal challenging. Existing analytical solutions make strong assumptions about the artifact’s geometry or brightness, and therefore only work well on a small subset of flares. Machine learning techniques have shown success in removing other types of artifacts, like reflections, but have not been widely applied to flare removal due to the lack of training data. To solve this problem, we explicitly model the optical causes of flare either empirically or using wave optics, and generate semi-synthetic pairs of flare-corrupted and clean images. This enables us to train neural networks to remove lens flare for the first time. Experiments show our data synthesis approach is critical for accurate flare removal, and that models trained with our technique generalize well to real lens flares across different scenes, lighting conditions, and cameras.