Outer membrane vesicles (OMVs) are spherical, bilayered, and nanosized membrane vesicles that are secreted from gram-negative bacteria. OMVs play a pivotal role in delivering lipopolysaccharide, proteins and other virulence factors to target cells. Multiple studies have found that OMVs participate in various inflammatory diseases, including periodontal disease, gastrointestinal inflammation, pulmonary inflammation and sepsis, by triggering pattern recognition receptors, activating inflammasomes and inducing mitochondrial dysfunction. OMVs also affect inflammation in distant organs or tissues via long-distance cargo transport in various diseases, including atherosclerosis and Alzheimer’s disease. In this review, we primarily summarize the role of OMVs in inflammatory diseases, describe the mechanism through which OMVs participate in inflammatory signal cascades, and discuss the effects of OMVs on pathogenic processes in distant organs or tissues with the aim of providing novel insights into the role and mechanism of OMVs in inflammatory diseases and the prevention and treatment of OMV-mediated inflammatory diseases.

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Protozoa, such as Ceratium and Paramecium, play a fundamental role in establishing sustainable ecosystems. The distribution and classification of certain protozoa and their species are informative indicators to evaluate environmental quality. However, protozoa analysis is traditionally performed by molecular biological (DNA, RNA) or morphological methods, which are time-consuming and require an experienced laboratory operator. In this work, we adopt a deep learning-based network to solve the protozoa classification task. This method utilizes microscope images to help researchers analyse the protozoa population and species, reducing the cost of experimental sample storage and relieving the burden on laboratory operators. However, the shape and size of protozoa vary greatly, which places a great burden on the optimization of DCNN feature distillation. It is a great challenge to build a fast and precise protozoa analysis image. We present a new version of YOLO-v5 with better performance and extend it with instance segmentation called PR-YOLO. Building on the original YOLOv5, we added two extra parallel branches to PR-YOLO, which perform different segmentation subtasks: (1) a branch generates a set of prototype masks (images); (2) the other branch predicts a set of mask coefficients corresponding to prototype masks for each instance mask generation. Then, to improve the classification accuracy, we introduced transformer encoding blocks and lightweight Convolution Block Attention Modules (CBAMs) to explore the prediction potential with a self-attention mechanism. To quantitatively evaluate the performance of PR-YOLO, a comprehensive experiment was carried out on the hand-segmented microscopic protozoa images. Our model obtained the best results, with average classification accuracy of 96.83% and mean Average Precision(mAP) of 86.92% with a speed of 25.2 fps, which proves that the method has high robustness in this application field.

Monocytes are one of the most abundant immune cells infiltrating the inflamed organs. However, the majority of studies on monocytes focus on circulating cells, rather than those in the tissue. Here, we identify and characterize an intravascular (i.v.) and extravascular (e.v.) synovial population (Syn Ly6C- cells) which lack cell surface markers of classical monocytes (Ly6C and CD62) or tissue macrophages (CD64 and Tim4), are transcriptionally distinct and conserved in RA patients. e.v. Syn Ly6C- cells are independent of NR4A1 and CCR2, long-lived and embryonically derived while the i.v. Syn Ly6C- cells are dependent on NR4A1, short lived and derived from circulating NCM. e.v. Syn Ly6C- cells undergo increased proliferation and reverse diapedesis dependent on LFA1 in response to arthrogenic stimuli and are required for the development of RA-like disease. These findings uncover a new facet of mononuclear cell biology and are imperative to understanding tissue-resident myeloid cell function in RA.