In Vitro Nonalcoholic Fatty Liver Disease Model Elucidating the Effect of Immune Environment on Disease Progression and Alleviation.

Nonalcoholic fatty liver disease (NAFLD), which is a major cause of chronic liver disease, is characterized by fat accumulation in the liver. Existing models struggle to assess medication effects on liver function in the context of NAFLD's unique inflammatory environment. We address this by developing a 3D in vitro NAFLD model using HepG2 and THP-1 cells (mimicking liver and Kupffer cells) cocultured using transwell and hydrogel system. This mimics liver architecture and allows for manipulation of the immune environment. We demonstrate that the model recapitulates key NAFLD features: steatosis (induced by fatty acids), oxidative stress, inflammation, and impaired liver function embodying the interrelationship between NAFLD and the surrounding immune environment. This versatile model offers a valuable tool for preclinical NAFLD research by incorporating a disease-relevant immune environment.

Microfluidic-based observation of local bacterial density under antimicrobial concentration gradient for rapid antibiotic susceptibility testing.

The need for accurate and efficient antibiotic susceptibility testing (AST) has been emphasized with respect to the emerging antimicrobial resistance of pathogenic bacteria which has increased over the recent decades. In this study, we introduce a microfluidic system that enables rapid formation of the antibiotic concentration gradient with convenient bacterial growth measurement based on color scales. Furthermore, we expanded the developed system to analyze combinatory effects of antibiotics and measured the collective antibiotic susceptibility of bacteria compared to single microfluidic AST methods. By injecting a continuous flow precisely into the channel, the system enabled the concentration gradient to be established between two parallel channels of different antibiotic concentrations within 30 min, before bacteria enter the exponential growth phase. Moreover, the local bacterial growth levels under antibiotic gradient were quantitatively determined by calculating the position-specific grayscale values from the microscopic images and were compared with the conventional optical density measurement method. We tested five antibiotic types on our platform for the pathogenic Gram-negative bacteria strain Pseudomonas aeruginosa, and we were able to determine the minimum inhibitory concentration (MIC) at which 90% to 95% of bacterial growth was inhibited. Finally, we demonstrated the efficacy of our system by showing that most of the antibiotic MICs determined in our platform show good agreement with the MIC range suggested by the Clinical and Laboratory Standards Institutes.

On-chip phenotypic investigation of combinatory antibiotic effects by generating orthogonal concentration gradients.

Combinatory therapy using two or more kinds of antibiotics is attracting considerable attention for inhibiting multi-drug resistant pathogenic bacteria. Although the therapy mostly leads to more powerful antimicrobial effects than using a single antibiotic (synergy), interference may arise from certain antibiotic combinations, resulting in the antimicrobial effect being suppressed (antagonism). Here, we present a microfluidic-based phenotypic screening chip to investigate combinatory antibiotic effects by automatically generating two orthogonal concentration gradients on a bacteria-trapping agarose gel. Computational simulations and fluorescence experiments together verify the simultaneous establishment of 121 concentration combinations, facilitating on-chip drug testing with stability and efficiency. Against Gram-negative bacteria, Pseudomonas aeruginosa, our chip allows the measurement of phenotypic growth levels, and enables various types of analyses for all antibiotic pairs to be conducted in 7 h. Furthermore, by providing a specific amount of susceptibility data, our chip enables the two reference models, Loewe additivity and Bliss independence, to be implemented, which classify the antibiotic interaction types into synergy or antagonism. These results suggest the efficacy of our chip as a cell-based drug screening platform for exploring the underlying pharmacological patterns of antibiotic interactions, with potential applications in guidance in clinical therapies and in screening other cell-type agents.