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Background Recently, bacterial extracellular vesicles (EVs) have been considered to play crucial roles in various biological processes and have great potential for developing cancer therapeutics and biomedicine. However, studies on bacterial EVs have mainly focused on outer membrane vesicles released from gram-negative bacteria since the outermost peptidoglycan layer in gram-positive bacteria is thought to preclude the release of EVs as a physical barrier. Results Here, we examined the ultrastructural organization of the EV produced by gram-positive bacteria using super-resolution stochastic optical reconstruction microscopy (STORM) at the nanoscale, which has not been resolved using conventional microscopy. Based on the super-resolution images of EVs, we propose three major mechanisms of EV biogenesis, i.e., membrane blebbing (mechanisms 1 and 2) or explosive cell lysis (mechanism 3), which are different from the mechanisms in gram-negative bacteria, despite some similarities. Conclusions These findings highlight the significant role of cell wall degradation in regulating various mechanisms of EV biogenesis and call for a reassessment of previously unresolved EV biogenesis in gram-positive bacteria.

Intercellular interaction is the most crucial factor in promoting cell viability and functionality in an engineered tissue system. Of the various shapes available for cell-laden constructs, spheroidal multicellular microarchitectures (SMMs) have been introduced as building blocks and injectable cell carriers with substantial cell-cell and cell-extracellular matrix (ECM) interactions. Here, we developed a precise and expeditious SMM printing method that can create a tissue-specific microenvironment and thus be potentially useful for cell therapy. This printing strategy is designed to manufacture SMMs fabricated with optimal bioink blended with decellularized ECM and alginate to enhance the functional performance of the encapsulated cells. Experimental results showed that the proposed method allowed for size controllability and mass production of SMMs with high cell viability. Moreover, SMMs co-cultured with endothelial cells promoted lineage-specific maturation and increased functionality compared to monocultured SMMs. Overall, it was concluded that SMMs have the potential for use in cell therapy due to their high cell retention and proliferation rate compared to single-cell injection, particularly for efficient tissue regeneration after myocardial infarction. This study suggests that utilizing microextrusion-based 3D bioprinting technology to encapsulate cells in cell-niche-standardized SMMs can expand the range of possible applications.

Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.